diff --git a/DEV.md b/DEV.md index 20631a1..6aab0ff 100644 --- a/DEV.md +++ b/DEV.md @@ -8,10 +8,12 @@ The one-sentence thesis: prose instructions decay over a session; mechanisms don - `rules.py`: the behavior layer. Built-in inspectors ("session rules"), the `Rule` type, and doc-state tracking with per-conversation persistence. `RuleBlock` itself belongs to clikernel (the mechanism defines the blocking contract); rules here raise it. - `dojo.py`: the scored practice round an agent completes before real work. -- `claudedojo` (notebook-sourced): capture a clean round from a live session (or headlessly: `capture_dojo`/`claudedojo --capture` plays a scripted round via the Agent SDK, gated by `is_clean`), curate it into a deterministic template, store it, and launch Claude Code sessions that resume it. See "The session template" below; `llmdojo/dojo_data/dojo_template.ipynb` is the one canonical template dialog, shipped as package data. -- `codexdojo` (notebook-sourced): the Codex mirror of claudedojo - same stages over native Responses items, creating or refreshing Codex threads through app-server. Both backends build their stores from the one packaged dialog above, and share the representation-independent layer in `tmpl.py` (notebook-sourced): prompts, store, launch loader, content gates, round boundaries, doc-name derivation, and `refresh_template` (replay the round's cells through a fresh clikernel to regenerate every output with no model spend). +- `claudedojo` (notebook-sourced): capture a clean round from a live session (or headlessly: `capture_dojo`/`claudedojo --capture` plays the two template prompts via the Agent SDK, gated by `is_clean`), curate it into a deterministic template, store it, and launch Claude Code sessions that resume it. See "The session template" below; the packaged dialogs are `llmdojo/dojo_data/dojo_round.ipynb` (the shared round) and `clik_boot.ipynb` (the clikernel hosts' bootstrap), shipped as package data. +- `codexdojo` (notebook-sourced): the Codex mirror of claudedojo - same stages over native Responses items, creating or refreshing Codex threads through app-server. Both backends build their stores from the same packaged pair, and share the representation-independent layer in `tmpl.py` (notebook-sourced): prompts, store, launch loader, content gates, the captured dialog's boundaries, doc-name derivation, `assemble`/`split_template` (a host's bootstrap plus the shared round, and back), and `refresh_template` (replay a packaged dialog's cells through a fresh clikernel to regenerate every output with no model spend). +- `ipyaidojo` (notebook-sourced): the ipyai backend. ipyai sessions are aidialog dialogs and its model context is rebuilt from the dialog each turn, so the store is the dialog itself; what differs is the tool (`py`) and the bootstrap (`dojo_data/ipyai_boot.ipynb`, captured live from an ipyai session by `capture_boot`, since ipyai has no `clik`), so `build_template` assembles that bootstrap with the shared round and replays both through ipyai's kernel client, and `prep_dojo` writes the result as a session under `./.ipyai/sessions/` for `ipyai -r`. No capture child and no extractors: the packaged dialogs are already its input. - `dojo_data/proj/`: the fixture project the dojo copies for each round. - `claude/`: the kernel startup file and its printed text, symlinked into `~/.config/clikernel/`; see `claude/README.md`. The Claude Code and codex hooks that consume `llmdojo.rules` live in aai-coding's `aai_coding/harness.py` (the `aai-hook` CLI). +- `ipyai/`: the kernel startup file for ipyai, symlinked into `~/.config/ipyai/`; see `ipyai/README.md`. ipyai runs it at kernel seeding: the imports and the doc aliases (no banner and no `clik`: ipyai's tool is `py`, its bootstrap prompt names the reads, and `ipyaidojo` sessions start with the bootstrap and the round already in history), plus the session rules, applied to the model's cells only (ipyai runs them with `store_history=False`, the user's with `True`). Doc-state keys on the session file: ipyai names its stem in the kernel's `LLMDOJO_HOST_ID`, which `_host_session` reads live. ## The rules @@ -58,9 +60,9 @@ The doced file is the source of truth, not a backup: the inspector re-reads it b A fresh session gets its tooling *instructions* up front but no *examples*, and models imitate what their context shows far more reliably than what it tells — so the first tool calls of every session were being produced cold, and early mistakes compounded into session habits. claudedojo fixes this with in-context learning: every session opens as if it had just finished a clean dojo round, by *resuming* a curated session file. Two facts make this cheap: transcripts are just JSONL files the CLI trusts (records chain via `parentUuid`; synthetic tool calls resume fine), and the template is byte-for-byte deterministic (ids re-derived from position + salt, timestamps pinned), so every session shares one identical prefix — ideal for prompt caching. -Four stages, plus a shortcut: when only the *outputs* have staled (doc text, card wording, rendering), a bare `dojobuild` refreshes the canonical dialog and rebuilds both stores: `refresh_template` replays the round's cells through a fresh clikernel and regenerates every receipt deterministically - the completion id is content-derived, so replaying an unchanged round is byte-stable - and only a change to the round itself needs the full path below. **Capture**: play one clean round — live (zero API cost: the session's agent plays the round, narration riding in comment-only cells, then runs `capture_current()` from that same session — no markers needed: the round's boundaries are structural (`capture_slice`, shared with codexdojo: bootstrap `doc()` reads through the first score of the last dealt round), doc-names are derived from the round's own `doc()` calls (`doced_names`), and failed calls are curated away) or unattended (`capture_dojo` / `claudedojo --capture [--model M] [--effort E]`: an Agent SDK session whose system prompt carries the *exact numbered cell list* to run, so the model's discretion shrinks to narration and reading receipts — LLMs are good at judgment and bad at restraint, and the earlier tips-style script produced improvised detours; with the exact script, sonnet at high effort captures cleanly for well under a dollar. The script never appears in transcript records, and the child runs bare per the setting_sources finding above. Gated by `is_clean` — one round, first-try clean score, no tool errors, no script phrases in visible text — since a fumbled round baked into a template teaches every future session the fumbles). **Curate**: `curate_dojo` mechanically takes the round's span, strips thinking, drops hook noise, mid-round prompts, and failed calls; then the curation pass, done by the session's agent via `reply2dlg`/`dlg2reply` and agreed with Jeremy first: cut the harness turns (the deferred-tools `ToolSearch`, any stray no-op calls), fix narration that references the script or numbered cells (the round must read as self-motivated work) (the run dir needs no attention: rounds deal into the state dir's fixed `dojo/run`, and stored templates canonicalize that path to `/tmp/dojo` in cells and outputs alike - `canon_tmpl` - so the committed artifact reads the same on every machine). A clean capture writes the round as a dialog (one prompt whose reply holds the whole round, each tool call a details block spelling the full `mcp__clikernel__execute(code=...)` call, as llmsurgery renders it); the curated copy lives at `llmdojo/dojo_data/dojo_template.ipynb` (shipped as package data) and `dojobuild --claude`/`--codex` compile it into the stores. The template is the role model every session imitates, so it must be *exemplary*, not merely clean: anything may be edited to make it a better role model — narration freely, and tool calls/outputs too, so long as edited receipts stay faithful to what the live tooling prints (when a change would touch many outputs, replaying the round is the cheaper generator of true ones); turns whose content session-start machinery re-injects live get cut — skill-reading turns especially, since baking a skill text ships a stale snapshot. Environment attachments survive in the directly-stored capture but drop in the dialog round-trip: they carry no message, and a spawned session gets its environment injected live as deltas anyway. **Store** (`save_template`): package data - `dojobuild` compiles the dialog into `dojo_data/claude_store/` and `codex_store/` (template plus metadata: completion id, dojo tooling version, the round's `doced` list), committed and shipped with the code, so updating llmdojo updates the template with nothing per-user to rebuild. **Launch** (`prep_dojo` / the CLI): write the template as a session of the target project (a fresh sid every launch), register the completion id, seed the spawned conversation's doc-state, `exec claude -r `. +A template is two prompt messages: the host's bootstrap (the prompt `BOOT_PROMPT`, answered with the doc reads the host's startup names - `doc(clik, pysk, edsk)` and the rest on clikernel hosts) and the shared round (the prompt `TMPL_PROMPT`, answered with `dojo_start()` through `dojo_score()`). The round is captured once and shared by every backend; the bootstrap is per host, captured by playing its prompt in that host, so the Claude/Codex pair is `clik_boot.ipynb` and ipyai's is `ipyai_boot.ipynb`. Four stages, plus a shortcut: when only the *outputs* have staled (doc text, card wording, rendering), a bare `dojobuild` refreshes the packaged clikernel dialogs and rebuilds every store: `refresh_template` replays a dialog's cells through a fresh clikernel and regenerates every receipt deterministically - the completion id is content-derived, so replaying an unchanged round is byte-stable - and only a change to the round itself needs the full path below. **Capture**: play one clean round — live (zero API cost: the session's agent answers the bootstrap prompt with the doc reads, then the dojo prompt with the round, narration riding in comment-only cells, then runs `capture_current()` from that same session — no markers needed: the dialog's boundaries are structural (`capture_slice`, shared with codexdojo: the bootstrap reads before the last dealt round, that round's start, and its first score), doc-names are derived from the capture's own `doc()` calls (`doced_names`), and failed calls are curated away) or unattended (`capture_dojo` / `claudedojo --capture [--model M] [--effort E]`: an Agent SDK session whose system prompt carries the *exact numbered cell list* to run and which prompt each cell answers, so the model's discretion shrinks to narration and reading receipts — LLMs are good at judgment and bad at restraint, and the earlier tips-style script produced improvised detours; with the exact script, sonnet at high effort captures cleanly for well under a dollar. The script never appears in transcript records, and the child runs bare per the setting_sources finding above. Gated by `is_clean` — one round, first-try clean score, no tool errors, no script phrases in visible text — since a fumbled round baked into a template teaches every future session the fumbles). **Curate**: `curate_dojo` mechanically takes the captured span, strips thinking, drops hook noise, user prompts, and failed calls; `mk_template` splits it at the `dojo_start()` call into the two prompt messages; then the curation pass, done by the session's agent via `reply2dlg`/`dlg2reply` and agreed with Jeremy first: cut the harness turns (the deferred-tools `ToolSearch`, any stray no-op calls), fix narration that references the script or numbered cells (the round must read as self-motivated work) (the run dir needs no attention: rounds deal into the state dir's fixed `dojo/run`, and stored templates canonicalize that path to `/tmp/dojo` in cells and outputs alike - `canon_tmpl` - so the committed artifact reads the same on every machine). A clean capture writes `boot.ipynb` and `round.ipynb` beside the store (each tool call a details block spelling the full `mcp__clikernel__execute(code=...)` call, as llmsurgery renders it); the curated copies live at `llmdojo/dojo_data/clik_boot.ipynb` and `dojo_round.ipynb` (shipped as package data) and `dojobuild` assembles and compiles them into the stores. The template is the role model every session imitates, so it must be *exemplary*, not merely clean: anything may be edited to make it a better role model — narration freely, and tool calls/outputs too, so long as edited receipts stay faithful to what the live tooling prints (when a change would touch many outputs, replaying is the cheaper generator of true ones); turns whose content session-start machinery re-injects live get cut — skill-reading turns especially, since baking a skill text ships a stale snapshot. Environment attachments survive in the directly-stored capture but drop in the dialog round-trip: they carry no message, and a spawned session gets its environment injected live as deltas anyway. **Store** (`save_template`): package data - `dojobuild` compiles the assembled dialog into `dojo_data/claude_store/`, `codex_store/`, and `ipyai_store/` (template plus metadata: completion id, dojo tooling version, the template's `doced` list), committed and shipped with the code, so updating llmdojo updates the template with nothing per-user to rebuild. **Launch** (`prep_dojo` / the CLI): write the template as a session of the target project (a fresh sid every launch), register the completion id, seed the spawned conversation's doc-state, `exec claude -r `. -Codex uses the same four stages with Responses items; everything representation-independent - the prompts, the store and launch loader, the content gates, the round's structural boundaries, and doc-name derivation - is `llmdojo.tmpl`, shared by both backends, which keep only their extractors, transport, and capture children. Both capture paths read the numbered script from `llmdojo/dojo_data/capture_prompt.md`: Claude appends it to the `claude_code` system preset, while `codexdojo --capture` writes it as the scratch project's `AGENTS.md` and runs `codex exec --json`. The Codex command takes the thread id from that event stream and passes it to `capture_current` — the same gate-curate-store call both backends expose (`--current` on each CLI). `llmsurgery.oai` converts native calls to readable blocks for review and reconstructs their custom-call wrappers afterward. `prep_dojo` creates a fresh thread through app-server and injects the reviewed items before `codex resume`. +Codex uses the same four stages with Responses items; everything representation-independent - the prompts, the store and launch loader, the content gates, the captured dialog's structural boundaries, doc-name derivation, and assembly - is `llmdojo.tmpl`, shared by the backends, which keep only their bootstrap dialog, extractors, transport, and capture children. Both clikernel capture paths read the numbered script from `llmdojo/dojo_data/capture_prompt.md`: Claude appends it to the `claude_code` system preset, while `codexdojo --capture` writes it as the scratch project's `AGENTS.md` and runs `codex exec --json` for the bootstrap prompt, then `codex exec resume --json` for the dojo prompt. The Codex command takes the thread id from that event stream and passes it to `capture_current` — the same gate-curate-store call both backends expose (`--current` on each CLI). `llmsurgery.oai` converts native calls to readable blocks for review and reconstructs their custom-call wrappers afterward. `prep_dojo` creates a fresh thread through app-server and injects the reviewed items before `codex resume`. ipyai's bootstrap is captured differently: play `ipyaidojo.BOOT_PROMPT` in a plain ipyai session, then `ipyaidojo --capture`, which takes the reply from the session file and replays it for full results (the session file stores tool results truncated). The template does not clear the kernel namespace. A new Claude or Codex session starts a new clikernel process, so its empty namespace agrees with the baked history. `prep_dojo` registers the versioned completion receipt and seeds the template's `doced` list for the new session. Completion receipts are machine-local; a dojo version change invalidates the stored template and makes the launcher warn until it is rebuilt. The converse does not hold: renames in the doc'd tooling do not bump the dojo version, so a same-version template can silently keep teaching stale names from its baked doc() output (observed twice, with fastcore.tools' renames and again after the editing-toolkit rename batch) - recapture after any rename that reaches the round's docs. ## Known sharp edges diff --git a/README.md b/README.md index 2dcd264..9ca410b 100644 --- a/README.md +++ b/README.md @@ -18,7 +18,7 @@ LLM coding agents imitate what their context shows far more reliably than what i $ pip install llmdojo ``` -Activation is two lines of clikernel user config: register the rules in `$XDG_CONFIG_HOME/clikernel/inspectors.py` (`from llmdojo.rules import make_inspector, RuleBlock; inspectors = [make_inspector()]`), and have your startup file print the bootstrap instructions that tell the agent to run the dojo. +Activation is two lines of clikernel user config: register the rules in `$XDG_CONFIG_HOME/clikernel/inspectors.py` (`from llmdojo.rules import make_inspector, RuleBlock; inspectors = [make_inspector()]`), and have your startup file print the bootstrap instructions that tell the agent which docs to read. For ipyai, link `ipyai/startup.py` into `~/.config/ipyai/` (see `ipyai/README.md`): its kernels then carry the same imports and the rules, applied to the model’s cells only, and `ipyaidojo` launches sessions with the bootstrap and the round already in history. ### Start a session @@ -38,17 +38,23 @@ $ claudedojo -r $ codexdojo -r ``` +ipyai starts warm the same way. `ipyaidojo` writes the round as an ipyai session of the current directory and runs `ipyai -r` on it (standing arguments from `ipyai_args` in `$XDG_CONFIG_HOME/ipyaidojo/config.toml`, `--sid` prints the id). Its kernels get the round’s imports from `~/.config/ipyai/startup.py`, see `ipyai/README.md`: + +``` sh +$ ipyaidojo +``` + ### Update the templates -The templates ship inside the package - the canonical dialog plus the compiled per-host stores - so *using* fresh templates takes no action beyond updating llmdojo. Maintaining them is one command: when tooling docs or skills change, the baked round’s outputs go stale, and `dojobuild` regenerates them with no model spend - it replays the round’s cells through a fresh kernel, splices the current outputs into the canonical template dialog, and recompiles both stores, all in your checkout for review: +The templates ship inside the package - the packaged dialogs plus the compiled per-host stores - so *using* fresh templates takes no action beyond updating llmdojo. A template is two prompts: a bootstrap (the host’s startup doc reads, captured by playing the bootstrap prompt in that host: `clik_boot.ipynb` for Claude and Codex, `ipyai_boot.ipynb` for ipyai) and the shared round (`dojo_round.ipynb`, captured once from Claude). Maintaining them is one command: when tooling docs or skills change, the baked outputs go stale, and `dojobuild` regenerates them with no model spend - it replays the packaged clikernel dialogs’ cells through a fresh kernel, splices the current outputs in, and recompiles every store from each host’s bootstrap plus the round, all in your checkout for review (the ipyai store replays its own bootstrap and the round through ipyai’s `py`, so `dojobuild` needs ipyai installed and a running rustygate): ``` sh $ dojobuild ``` -Only a change to the round itself - its cells, not their outputs - needs a fresh capture: `claudedojo --capture` plays a scripted round headlessly, `codexdojo --capture` does the same in a Codex child, and `--current` on either stores a clean round an existing session already played. After reviewing a captured dialog, `dojobuild --claude` and `dojobuild --codex` compile it into each store without re-replaying. +Only a change to the round itself - its cells, not their outputs - needs a fresh capture: `claudedojo --capture` plays the two prompts headlessly, `codexdojo --capture` does the same in a Codex child, and `--current` on either stores a clean round an existing session already played; each writes `boot.ipynb` and `round.ipynb` beside its store for review, to copy over the packaged pair. A change to a host’s bootstrap reads is a new bootstrap capture for that host (`ipyaidojo --capture` after playing its prompt in ipyai). After reviewing, `dojobuild --claude`, `--codex`, and `--ipyai` compile one store each without re-replaying the clikernel dialogs. -The bootstrap `doc()` reads (the cells before `dojo_start()`) live in two artifacts: `dojo_data/capture_prompt.md`, the script future captures replay, and the baked round in the canonical dialog itself. To change the set, update both, keeping each new `doc(x)` as its own cell placed after the `doc(clik, pysk, edsk)` one, then run `dojobuild`, which replays the new cell for a true output and recomputes the stored `doced` list. Updating only one leaves the demonstration and the capture script teaching different bootstraps. +The bootstrap reads are named in two places that must agree: the host’s startup text (`claude/startup.txt`, or `ipyaidojo.BOOT_PROMPT` for ipyai, which has no banner) and `dojo_data/capture_prompt.md`, the script future captures replay. To change the set, update both, then recapture the bootstrap. ### State and templates diff --git a/ipyai/README.md b/ipyai/README.md new file mode 100644 index 0000000..c062084 --- /dev/null +++ b/ipyai/README.md @@ -0,0 +1,22 @@ +# ipyai integration + +The file here wires ipyai's kernels to the llmdojo regime, the way `claude/` does for clikernel. It lives in this repo because it must change in lockstep with the code: the imports and aliases it sets up are the ones the template's `doc()` calls use. + +## The file + +`startup.py` runs inside every kernel ipyai owns (ipyai executes `~/.config/ipyai/startup.py` at seeding, with `__file__` bound). It imports the standard tooling modules and defines the short aliases the template's `doc()` calls use (`pysk`, `edsk`, `dsk`, `exh`, `rgsk`, `acp`). ipyai's model-facing tool is `py`, not clikernel's `execute`, so there is no `clik` alias here and no printed banner: ipyai's bootstrap prompt names the reads itself, and a session launched by `ipyaidojo` already has the bootstrap and the worked round in its history. + +It also installs the session rules (`llmdojo.rules.make_inspector`), applied to the model's cells only: ipyai runs the model's `py` cells with `store_history=False` and the user's typed cells with `True`, so IPython's `pre_run_cell` info tells them apart, and the user's own cells are never inspected. A rule's note prints ahead of the cell's output and so reaches the model inside the tool result; a blocking rule rejects the cell. Doc-state is keyed to the session file: ipyai names the session stem in the kernel's `LLMDOJO_HOST_ID`, and `ipyaidojo` seeds the baked template's doc-state under that key, so a warm session's docs count as read. + +## The bootstrap + +ipyai's bootstrap is its own dialog, `dojo_data/ipyai_boot.ipynb`: one prompt (`ipyaidojo.BOOT_PROMPT`) whose reply is the doc reads through `py`. It is captured live: in a Python project, start a plain `ipyai`, send that prompt, and once the model has answered run `ipyaidojo --capture` (or `capture_boot()`) from the same directory. The capture takes the reply from the session file, replays its cells for full results (the session file stores tool results truncated), and writes `boot.ipynb` beside the store; copy it over `dojo_data/ipyai_boot.ipynb` after review. The round is the shared `dojo_round.ipynb`. + +## Setup + +From the repo root: + + mkdir -p ~/.config/ipyai + ln -sf "$PWD/ipyai/startup.py" ~/.config/ipyai/ + +Then `ipyaidojo` starts ipyai with the bootstrap and the round in history, and `dojobuild` rebuilds the ipyai store (`dojo_data/ipyai_store/`) beside the Claude and Codex ones. diff --git a/ipyai/startup.py b/ipyai/startup.py new file mode 100644 index 0000000..ab02238 --- /dev/null +++ b/ipyai/startup.py @@ -0,0 +1,32 @@ +from fastcore.utils import * +from pyskills import list_pyskills, doc, xdir +from fastcore.editskill import * +from aidialog.dlgskill import * +from exhash.skill import * +from rgapi.skill import * +from llmdojo.dojo import * +from ipykernel_helper import info_md +import pyskills.skill as pysk, fastcore.editskill as edsk, aidialog.dlgskill as dsk, exhash.skill as exh, rgapi.skill as rgsk, aai_coding.coding_patterns as acp + +# The session rules, applied to the model's cells only. ipyai runs the model's `py` cells with store_history=False +# and the user's typed cells with True, so IPython's pre_run_cell info tells them apart; the user's own cells are +# never inspected. A note prints ahead of the cell's output (so it reaches the model inside the tool result); +# a RuleBlock rejects the cell; any other inspector error prints and the cell runs anyway. +from IPython import get_ipython +from IPython.core.error import InputRejected +from llmdojo.rules import make_inspector + +class _AIInspect: + def __init__(self, f): self.f, self.src, self.ai = f, '', False + def stash(self, info): self.src, self.ai = info.raw_cell, not info.store_history + def visit(self, tree): + if not self.ai: return tree + try: note = self.f(tree, self.src) + except InputRejected: raise + except Exception as e: note = f'inspector error (cell runs anyway): {e!r}' + if note: print(note, end='') + return tree + +_ai_inspect = _AIInspect(make_inspector()) +get_ipython().events.register('pre_run_cell', _ai_inspect.stash) +get_ipython().ast_transformers.append(_ai_inspect) diff --git a/llmdojo/__init__.py b/llmdojo/__init__.py index 6567ecf..69b2ac4 100644 --- a/llmdojo/__init__.py +++ b/llmdojo/__init__.py @@ -2,6 +2,7 @@ Modules: +- `llmdojo.ipyaidojo`: Start ipyai with a worked tooling session already in its history - `llmdojo.tmpl`: The template layer shared by claudedojo and codexdojo""" __version__ = "0.0.4" diff --git a/llmdojo/_modidx.py b/llmdojo/_modidx.py index 93759a1..cbc4b8d 100644 --- a/llmdojo/_modidx.py +++ b/llmdojo/_modidx.py @@ -6,11 +6,14 @@ 'git_url': 'https://github.com/AnswerDotAI/llmdojo', 'lib_path': 'llmdojo'}, 'syms': { 'llmdojo.claudedojo': { 'llmdojo.claudedojo._blocks': ('claudedojo.html#_blocks', 'llmdojo/claudedojo.py'), + 'llmdojo.claudedojo._boots': ('claudedojo.html#_boots', 'llmdojo/claudedojo.py'), 'llmdojo.claudedojo._cells': ('claudedojo.html#_cells', 'llmdojo/claudedojo.py'), 'llmdojo.claudedojo._dealt': ('claudedojo.html#_dealt', 'llmdojo/claudedojo.py'), 'llmdojo.claudedojo._is_err': ('claudedojo.html#_is_err', 'llmdojo/claudedojo.py'), 'llmdojo.claudedojo._is_prompt': ('claudedojo.html#_is_prompt', 'llmdojo/claudedojo.py'), 'llmdojo.claudedojo._load_reg': ('claudedojo.html#_load_reg', 'llmdojo/claudedojo.py'), + 'llmdojo.claudedojo._starts': ('claudedojo.html#_starts', 'llmdojo/claudedojo.py'), + 'llmdojo.claudedojo._tcells': ('claudedojo.html#_tcells', 'llmdojo/claudedojo.py'), 'llmdojo.claudedojo._vis': ('claudedojo.html#_vis', 'llmdojo/claudedojo.py'), 'llmdojo.claudedojo.append_dojo': ('claudedojo.html#append_dojo', 'llmdojo/claudedojo.py'), 'llmdojo.claudedojo.build_template': ('claudedojo.html#build_template', 'llmdojo/claudedojo.py'), @@ -27,7 +30,8 @@ 'llmdojo.claudedojo.prep_dojo': ('claudedojo.html#prep_dojo', 'llmdojo/claudedojo.py'), 'llmdojo.claudedojo.save_template': ('claudedojo.html#save_template', 'llmdojo/claudedojo.py'), 'llmdojo.claudedojo.strip_dojo': ('claudedojo.html#strip_dojo', 'llmdojo/claudedojo.py')}, - 'llmdojo.codexdojo': { 'llmdojo.codexdojo._capture_span': ('codexdojo.html#_capture_span', 'llmdojo/codexdojo.py'), + 'llmdojo.codexdojo': { 'llmdojo.codexdojo._boots': ('codexdojo.html#_boots', 'llmdojo/codexdojo.py'), + 'llmdojo.codexdojo._capture_span': ('codexdojo.html#_capture_span', 'llmdojo/codexdojo.py'), 'llmdojo.codexdojo._cell': ('codexdojo.html#_cell', 'llmdojo/codexdojo.py'), 'llmdojo.codexdojo._dealt': ('codexdojo.html#_dealt', 'llmdojo/codexdojo.py'), 'llmdojo.codexdojo._is_prompt': ('codexdojo.html#_is_prompt', 'llmdojo/codexdojo.py'), @@ -60,11 +64,24 @@ 'llmdojo.dojo_data.proj.core': {}, 'llmdojo.dojo_data.proj.report': {}, 'llmdojo.dojo_data.proj.tmpl': {}, + 'llmdojo.ipyaidojo': { 'llmdojo.ipyaidojo.build_template': ('ipyaidojo.html#build_template', 'llmdojo/ipyaidojo.py'), + 'llmdojo.ipyaidojo.capture_boot': ('ipyaidojo.html#capture_boot', 'llmdojo/ipyaidojo.py'), + 'llmdojo.ipyaidojo.ipyai_dialog': ('ipyaidojo.html#ipyai_dialog', 'llmdojo/ipyaidojo.py'), + 'llmdojo.ipyaidojo.load_template': ('ipyaidojo.html#load_template', 'llmdojo/ipyaidojo.py'), + 'llmdojo.ipyaidojo.main': ('ipyaidojo.html#main', 'llmdojo/ipyaidojo.py'), + 'llmdojo.ipyaidojo.prep_dojo': ('ipyaidojo.html#prep_dojo', 'llmdojo/ipyaidojo.py'), + 'llmdojo.ipyaidojo.replay_cells': ('ipyaidojo.html#replay_cells', 'llmdojo/ipyaidojo.py'), + 'llmdojo.ipyaidojo.template_dialog': ('ipyaidojo.html#template_dialog', 'llmdojo/ipyaidojo.py')}, 'llmdojo.rules': {}, 'llmdojo.tmpl': { 'llmdojo.tmpl._dojo_v': ('tmpl.html#_dojo_v', 'llmdojo/tmpl.py'), + 'llmdojo.tmpl._msg_cells': ('tmpl.html#_msg_cells', 'llmdojo/tmpl.py'), + 'llmdojo.tmpl._one_prompt': ('tmpl.html#_one_prompt', 'llmdojo/tmpl.py'), + 'llmdojo.tmpl._pmsgs': ('tmpl.html#_pmsgs', 'llmdojo/tmpl.py'), 'llmdojo.tmpl._replay_cells': ('tmpl.html#_replay_cells', 'llmdojo/tmpl.py'), 'llmdojo.tmpl._seed_doced': ('tmpl.html#_seed_doced', 'llmdojo/tmpl.py'), 'llmdojo.tmpl._turns': ('tmpl.html#_turns', 'llmdojo/tmpl.py'), + 'llmdojo.tmpl.assemble': ('tmpl.html#assemble', 'llmdojo/tmpl.py'), + 'llmdojo.tmpl.boot_gates': ('tmpl.html#boot_gates', 'llmdojo/tmpl.py'), 'llmdojo.tmpl.canon_tmpl': ('tmpl.html#canon_tmpl', 'llmdojo/tmpl.py'), 'llmdojo.tmpl.capture_slice': ('tmpl.html#capture_slice', 'llmdojo/tmpl.py'), 'llmdojo.tmpl.doced_names': ('tmpl.html#doced_names', 'llmdojo/tmpl.py'), @@ -72,7 +89,11 @@ 'llmdojo.tmpl.launch_config': ('tmpl.html#launch_config', 'llmdojo/tmpl.py'), 'llmdojo.tmpl.load_reg': ('tmpl.html#load_reg', 'llmdojo/tmpl.py'), 'llmdojo.tmpl.load_store': ('tmpl.html#load_store', 'llmdojo/tmpl.py'), + 'llmdojo.tmpl.localized_cells': ('tmpl.html#localized_cells', 'llmdojo/tmpl.py'), 'llmdojo.tmpl.main': ('tmpl.html#main', 'llmdojo/tmpl.py'), + 'llmdojo.tmpl.refresh_dialog': ('tmpl.html#refresh_dialog', 'llmdojo/tmpl.py'), 'llmdojo.tmpl.refresh_template': ('tmpl.html#refresh_template', 'llmdojo/tmpl.py'), 'llmdojo.tmpl.round_gates': ('tmpl.html#round_gates', 'llmdojo/tmpl.py'), - 'llmdojo.tmpl.save_store': ('tmpl.html#save_store', 'llmdojo/tmpl.py')}}} + 'llmdojo.tmpl.save_store': ('tmpl.html#save_store', 'llmdojo/tmpl.py'), + 'llmdojo.tmpl.split_template': ('tmpl.html#split_template', 'llmdojo/tmpl.py'), + 'llmdojo.tmpl.template_cells': ('tmpl.html#template_cells', 'llmdojo/tmpl.py')}}} diff --git a/llmdojo/claudedojo.py b/llmdojo/claudedojo.py index 46c8d28..94d086f 100644 --- a/llmdojo/claudedojo.py +++ b/llmdojo/claudedojo.py @@ -15,7 +15,7 @@ from fastcore.utils import * from fastcore.script import call_parse from llmsurgery.ant import * -from aidialog.ipynb import read_ipynb, write_ipynb +from aidialog.ipynb import write_ipynb from aidialog.hist import chat2dlg from .rules import _state_root from .tmpl import * @@ -38,10 +38,10 @@ def _cells(recs): def capture_span( recs, # Session records, e.g. from `load_sess` ): - "The thread records of the captured round: bootstrap docs through the first score of the last dealt round" + "The thread records of the captured dialog: bootstrap reads through the first score of the last dealt round" t = strip_think(sess_thread(recs)) calls = [(k, nested_idx(b,'input','code') or '', b['id']) for k,r in enumerate(t) for b in _blocks(r) if b.get('type')=='tool_use'] - i,j = capture_slice([c for _,c,_ in calls]) + i,_,j = capture_slice([c for _,c,_ in calls]) kend = first(k for k,r in enumerate(t) if any(b.get('tool_use_id')==calls[j][2] for b in _blocks(r))) return L(t[calls[i][0]:kend+1]) @@ -82,16 +82,20 @@ def is_clean( return probs # %% ../nbs/00_claudedojo.ipynb #71ddbc0b -OPENING = 'Bootstrapping: the startup script has already run its imports, so I read the tooling docs, check the catalog, then take the dojo.' +OPENING = 'Bootstrapping: the startup script has already run its imports, so I read the tooling docs and check the catalog.' + +def _starts(r): return any(re.match(_START_RE, nested_idx(b,'input','code') or '') for b in _blocks(r)) def mk_template( - picked, # Round records, e.g. from `curate_dojo` - opening=OPENING, # Reply text preceding the first tool call - closing="OK I'm ready.", # Reply text ending the round - doced=None, # Names doc()'d during the round, stored in the dialog's metadata for launch-time doc-state seeding + picked, # Captured records, e.g. from `curate_dojo`: the bootstrap reads, then the round + opening=OPENING, # Bootstrap reply text preceding its first tool call + closing="OK I'm ready.", # Reply text ending each prompt's reply + doced=None, # Names doc()'d during the capture, stored in the dialog's metadata for launch-time doc-state seeding ): - "A template dialog: `picked` wrapped in `TMPL_PROMPT` and framing text, with tool output untruncated" - recs = [mk_rec('user', TMPL_PROMPT), mk_rec('assistant', opening), *picked, mk_rec('assistant', closing)] + "A two-prompt template dialog: `picked` split at its `dojo_start()` call into the bootstrap reply and the round reply, each wrapped in its prompt and framing text, with tool output untruncated" + k = first(i for i,r in enumerate(picked) if _starts(r)) + recs = [mk_rec('user', BOOT_PROMPT), mk_rec('assistant', opening), *picked[:k], mk_rec('assistant', closing), + mk_rec('user', TMPL_PROMPT), *picked[k:], mk_rec('assistant', closing)] dlg = chat2dlg(recs2chat(recs), 'dojo_template', mx=None) dlg.meta['llmdojo'] = dict(doced=list(doced or [])) return dlg @@ -115,29 +119,29 @@ def load_template( # %% ../nbs/00_claudedojo.ipynb #658ba7f7 def build_template( - src, # Path to a template dialog .ipynb + boot=None, # The bootstrap dialog (or its path); the packaged clikernel bootstrap if None + rnd=None, # The round dialog (or its path); the packaged round if None d=None, # Store dir; `TMPL_DIR` if None cwd='.', # Project directory recorded in the records ): - "Convert a template dialog into the store; the dialog's `llmdojo.doced` metadata rides along for launch-time doc-state seeding" - dlg = read_ipynb(str(src)) - save_template(msgs2recs(dlg2msgs(dlg), key='claudedojo', cwd=str(cwd), model=None), d, - doced=nested_idx(dlg.meta, 'llmdojo', 'doced')) + "Assemble the bootstrap and the round into the store; the assembled `doced` rides along for launch-time doc-state seeding" + dlg = assemble(boot or CLIK_BOOT, rnd) + save_template(msgs2recs(dlg2msgs(dlg), key='claudedojo', cwd=str(cwd), model=None), d, doced=dlg.meta['llmdojo']['doced']) # %% ../nbs/00_claudedojo.ipynb #9069c1a2 def capture_current( sid=None, # Session id or name; the project's newest transcript if None cwd=None, # Project directory; the current directory if None d=None, # Template store dir; `TMPL_DIR` if None - docs=None, # Documented names; derived from the round's `doc(...)` calls if None + docs=None, # Documented names; derived from the captured `doc(...)` calls if None ): - "Gate an existing session's clean round and store its template" + "Gate an existing session's clean round and store its template; writes `boot.ipynb` and `round.ipynb` beside the store for review" selected = curate_dojo(load_sess(sid, cwd)) if probs := is_clean(selected): raise ValueError('; '.join(probs)) if docs is None: docs = doced_names(_cells(selected)) dlg = canon_tmpl(mk_template(selected, doced=docs)) save_template(msgs2recs(dlg2msgs(dlg), key='claudedojo', model=None), d, doced=docs) - write_ipynb(dlg, Path(d or TMPL_DIR)/'template.ipynb') + for part,nm in zip(split_template(dlg), ('boot', 'round')): write_ipynb(part, Path(d or TMPL_DIR)/f'{nm}.ipynb') return dlg # %% ../nbs/00_claudedojo.ipynb #f4e0e6c4 @@ -148,24 +152,25 @@ async def capture_dojo( attempts=3, # Gated attempts before giving up budget=10.0, # Max USD per attempt ): - "Play a scripted dojo round headlessly, gate it with `is_clean`, and store the curated template" + "Play a scripted dojo round headlessly (the bootstrap prompt, then the dojo prompt), gate it with `is_clean`, and store the curated template" from claude_agent_sdk import query, ClaudeAgentOptions, ResultMessage, AssistantMessage, TextBlock, ToolUseBlock for i in range(attempts): proj = Path(tempfile.mkdtemp(prefix='claudedojo_')) (proj/'pyproject.toml').write_text('[project]\nname = "dojo-capture"\nversion = "0"\n') sid = str(uuid.uuid4()) - opts = ClaudeAgentOptions(cwd=proj, session_id=sid, model=model, max_budget_usd=budget, permission_mode='bypassPermissions', + def _opts(**kw): return ClaudeAgentOptions(cwd=proj, model=model, max_budget_usd=budget, permission_mode='bypassPermissions', setting_sources=[], mcp_servers=dict(clikernel=dict(type='stdio', command=shutil.which('clikernel-mcp'))), strict_mcp_config=True, thinking=dict(type='adaptive'), effort=effort, env={'LLMDOJO_STATE_DIR': str(proj/'state'), 'MAX_MCP_OUTPUT_TOKENS': '50000'}, - system_prompt=dict(type='preset', preset='claude_code', append=CAPTURE_SCRIPT)) + system_prompt=dict(type='preset', preset='claude_code', append=CAPTURE_SCRIPT), **kw) cost,err = 0,None try: - async for m in query(prompt=TMPL_PROMPT, options=opts): - if isinstance(m, ResultMessage): cost = m.total_cost_usd or 0 - elif isinstance(m, AssistantMessage): - for b in m.content: - if isinstance(b, ToolUseBlock): print('>', str(b.input.get('code', b.input))[:80], flush=True) - elif isinstance(b, TextBlock) and b.text.strip(): print('.', b.text.strip()[:80], flush=True) + for prompt,opts in ((BOOT_PROMPT, _opts(session_id=sid)), (TMPL_PROMPT, _opts(resume=sid))): + async for m in query(prompt=prompt, options=opts): + if isinstance(m, ResultMessage): cost += m.total_cost_usd or 0 + elif isinstance(m, AssistantMessage): + for b in m.content: + if isinstance(b, ToolUseBlock): print('>', str(b.input.get('code', b.input))[:80], flush=True) + elif isinstance(b, TextBlock) and b.text.strip(): print('.', b.text.strip()[:80], flush=True) except Exception as e: err = f'run failed: {e}' recs = [] if err else sess_thread(load_sess(sid, proj)) probs = [err] if err else is_clean(recs) @@ -173,7 +178,7 @@ async def capture_dojo( if not probs: tdlg = capture_current(sid, proj, d) print(f"stored: cid {load_template(d)[1]['cid']}, {(Path(d or TMPL_DIR)/'template.jsonl').stat().st_size//1024}KB") - print(f"review dialog: {Path(d or TMPL_DIR)/'template.ipynb'} - curate it, then rebuild the stores with dojobuild --claude (and --codex)") + print(f"review dialogs: {Path(d or TMPL_DIR)/'boot.ipynb'} and round.ipynb: curate, copy over the packaged pair, then dojobuild") shutil.rmtree(proj) shutil.rmtree(sess_dir(proj), ignore_errors=True) if not probs: return tdlg @@ -212,14 +217,18 @@ def append_dojo( # %% ../nbs/00_claudedojo.ipynb #cff4225e def _is_prompt(r): return r.get('type')=='user' and rec_role(r)=='user' - -def _dealt(t): return any(re.match(_START_RE, nested_idx(b,'input','code') or '') for r in t for b in _blocks(r)) +def _tcells(t): return [nested_idx(b,'input','code') or '' for r in t for b in _blocks(r) if b.get('type')=='tool_use'] +def _dealt(t): return any(re.match(_START_RE, c) for c in _tcells(t)) +def _boots(t): return bool(cs := _tcells(t)) and not boot_gates(cs) def strip_dojo( recs, # Session records, e.g. from `load_sess` ): - "Copy of `recs` without the user turns that dealt a round (bare `dojo_start()`)" - return _turns(recs, _is_prompt).filter(_dealt, negate=True).concat() + "Copy of `recs` without the user turns that dealt a round (bare `dojo_start()`), nor a bootstrap turn (only doc reads) right before one" + ts = _turns(recs, _is_prompt) + drop = {i for i,t in enumerate(ts) if _dealt(t)} + drop |= {i-1 for i in drop if i and _boots(ts[i-1])} + return L(t for i,t in enumerate(ts) if i not in drop).concat() # %% ../nbs/00_claudedojo.ipynb #aa9b31fe def compact_dojo( diff --git a/llmdojo/codexdojo.py b/llmdojo/codexdojo.py index 583660a..73d3192 100644 --- a/llmdojo/codexdojo.py +++ b/llmdojo/codexdojo.py @@ -16,7 +16,7 @@ from fastcore.script import call_parse from llmsurgery.oai import * from llmsurgery.compact import compact_chat -from aidialog.ipynb import read_ipynb, write_ipynb +from aidialog.ipynb import write_ipynb from .rules import _state_root from .tmpl import * from .tmpl import _dojo_v, _seed_doced, _START_RE, _turns @@ -123,7 +123,7 @@ def is_clean( # %% ../nbs/01_codexdojo.ipynb #fce1bcb2 def _capture_span(src): - "Native custom-call pairs from the bootstrap docs through the first dojo score" + "Native custom-call pairs from the bootstrap reads through the first dojo score" items = _items(src) starts = [i for i,x in enumerate(items) if re.match(_START_RE,_cell(x))] if not starts: raise ValueError('no dojo_start() call') @@ -131,8 +131,8 @@ def _capture_span(src): users = [i for i,x in enumerate(items[:start]) if x.get('type')=='message' and x.get('role')=='user'] if not users: raise ValueError('no user message before dojo_start()') end = first((i for i,x in enumerate(items[start+1:],start+1) if x.get('type')=='message' and x.get('role')=='user'),len(items)) - pairs = collapse_deferred(items[users[-1]+1:end]) - i,j = capture_slice([_cell(x) for x in pairs[::2]]) + pairs = collapse_deferred(items[users[0]+1:end]) + i,_,j = capture_slice([_cell(x) for x in pairs[::2]]) return pairs[i*2:(j+1)*2] def capture_current( @@ -140,55 +140,59 @@ def capture_current( d=None, # Template store dir; `TMPL_DIR` if None docs=None, # Documented names; derived from captured `doc(...)` calls if None ): - "Gate an existing Codex thread's clean round and store its native template" + "Gate an existing Codex thread's clean round and store its native template; writes `boot.ipynb` and `round.ipynb` beside the store for review" selected = reid_items(_capture_span(response_items(load_rollout(ref))),'codexdojo') if probs:=is_clean(selected): raise ValueError('; '.join(probs)) if docs is None: docs = doced_names([_cell(x) for x in selected]) dlg = canon_tmpl(mk_template(selected,doced=docs)) save_template(dlg2items(dlg),d,doced=docs) - write_ipynb(dlg,Path(d or TMPL_DIR)/'template.ipynb') + for part,nm in zip(split_template(dlg), ('boot', 'round')): write_ipynb(part, Path(d or TMPL_DIR)/f'{nm}.ipynb') return dlg def capture_dojo( d=None, # Template store dir; `TMPL_DIR` if None ): - "Run a clean dojo in a bare Codex child and store its review dialog" + "Run a clean dojo in a bare Codex child (the bootstrap prompt, then the dojo prompt resumed onto it) and store its review dialogs" with tempfile.TemporaryDirectory(prefix='codexdojo_') as td: proj = Path(td) (proj/'pyproject.toml').write_text('[project]\nname = "dojo-capture"\nversion = "0"\n') (proj/'AGENTS.md').write_text(CAPTURE_SCRIPT) env = os.environ | {'LLMDOJO_STATE_DIR': str(proj/'state')} - cmd = ['codex','-a','never','-s','workspace-write','exec','--json','--skip-git-repo-check','-C',str(proj),TMPL_PROMPT] - proc = subprocess.Popen(cmd,stdout=subprocess.PIPE,text=True,env=env) + base = ['codex','-a','never','-s','workspace-write','exec','--json','--skip-git-repo-check','-C',str(proj)] sid = None - for line in proc.stdout: - event = json.loads(line) - if event.get('type')=='thread.started': - sid = event['thread_id'] - print(f'thread: {sid}',flush=True) - elif event.get('type')=='item.completed': - it = event.get('item',{}) - typ = it.get('type') - if typ=='agent_message': print(it['text'],flush=True) - elif typ!='reasoning': - c = parse_exec(it.get('input') or '') - desc = c.arguments.get('code',str(c.arguments)).splitlines()[0] if c else truncstr(json.dumps(it),160) - print(f'[{typ}] {desc}',flush=True) - if rc:=proc.wait(): raise subprocess.CalledProcessError(rc,cmd) - if not sid: raise RuntimeError('capture produced no thread id') + for cmd in (base+[BOOT_PROMPT], lambda: base+['resume',sid,TMPL_PROMPT]): + if callable(cmd): cmd = cmd() + proc = subprocess.Popen(cmd,stdout=subprocess.PIPE,text=True,env=env) + for line in proc.stdout: + event = json.loads(line) + if event.get('type')=='thread.started': + sid = event['thread_id'] + print(f'thread: {sid}',flush=True) + elif event.get('type')=='item.completed': + it = event.get('item',{}) + typ = it.get('type') + if typ=='agent_message': print(it['text'],flush=True) + elif typ!='reasoning': + c = parse_exec(it.get('input') or '') + desc = c.arguments.get('code',str(c.arguments)).splitlines()[0] if c else truncstr(json.dumps(it),160) + print(f'[{typ}] {desc}',flush=True) + if rc:=proc.wait(): raise subprocess.CalledProcessError(rc,cmd) + if not sid: raise RuntimeError('capture produced no thread id') return capture_current(sid,d) # %% ../nbs/01_codexdojo.ipynb #8c6b39a0 -OPENING = 'Bootstrapping: I read the tooling docs, check the catalog, then take the dojo.' +OPENING = 'Bootstrapping: I read the tooling docs and check the catalog.' def mk_template( - picked, # Native custom call/result pairs - opening=OPENING, # Reply text preceding the first tool call - closing="OK I'm ready.", # Reply text ending the round - doced=None, # Names doc()'d during the round + picked, # Native custom call/result pairs: the bootstrap reads, then the round + opening=OPENING, # Bootstrap reply text preceding its first tool call + closing="OK I'm ready.", # Reply text ending each prompt's reply + doced=None, # Names doc()'d during the capture ): - "A template dialog with native tool output left untruncated" - items = [codex_msg('user',TMPL_PROMPT),codex_msg('assistant',opening),*picked,codex_msg('assistant',closing)] + "A two-prompt template dialog with native tool output left untruncated: `picked` split at its `dojo_start()` call into the bootstrap reply and the round reply" + k = first(i for i,x in enumerate(picked) if re.match(_START_RE, _cell(x))) + items = [codex_msg('user',BOOT_PROMPT),codex_msg('assistant',opening),*picked[:k],codex_msg('assistant',closing), + codex_msg('user',TMPL_PROMPT),*picked[k:],codex_msg('assistant',closing)] dlg = items2dlg(items,'codexdojo_template',mx=None) dlg.meta['llmdojo'] = dict(doced=list(doced or [])) return dlg @@ -225,13 +229,13 @@ def load_template( # %% ../nbs/01_codexdojo.ipynb #635dbdd7 def build_template( - src, # Path to a template dialog + boot=None, # The bootstrap dialog (or its path); the packaged clikernel bootstrap if None + rnd=None, # The round dialog (or its path); the packaged round if None d=None, # Store dir; `TMPL_DIR` if None ): - "Convert a reviewed template dialog back into the native store" - dlg = read_ipynb(str(src)) - items = reid_items(dlg2items(dlg),'codexdojo') - save_template(items,d,doced=nested_idx(dlg.meta,'llmdojo','doced') or []) + "Assemble the bootstrap and the round into the native store" + dlg = assemble(boot or CLIK_BOOT, rnd) + save_template(reid_items(dlg2items(dlg),'codexdojo'),d,doced=dlg.meta['llmdojo']['doced']) # %% ../nbs/01_codexdojo.ipynb #c51d054b def _load_reg(d): @@ -274,14 +278,17 @@ async def append_dojo( # %% ../nbs/01_codexdojo.ipynb #c507e5da def _is_prompt(x): return x.get('type')=='message' and x.get('role')=='user' - def _dealt(t): return any(re.match(_START_RE, _cell(x)) for x in t) +def _boots(t): return bool(cs := [c for x in t if (c := _cell(x))]) and not boot_gates(cs) def strip_dojo( items, # Responses items in conversation order ): - "Copy of `items` without the user turns that dealt a round (bare `dojo_start()`)" - return _turns(items, _is_prompt).filter(_dealt, negate=True).concat() + "Copy of `items` without the user turns that dealt a round (bare `dojo_start()`), nor a bootstrap turn (only doc reads) right before one" + ts = _turns(items, _is_prompt) + drop = {i for i,t in enumerate(ts) if _dealt(t)} + drop |= {i-1 for i in drop if i and _boots(ts[i-1])} + return L(t for i,t in enumerate(ts) if i not in drop).concat() def _strip(items): "`strip_dojo`, unless nothing else would remain to compact" diff --git a/llmdojo/dojo.py b/llmdojo/dojo.py index 8a2ca0c..1acd658 100644 --- a/llmdojo/dojo.py +++ b/llmdojo/dojo.py @@ -107,7 +107,7 @@ def _card(): d = _RUN['dir'] ks = '\n'.join(f"{i}. (par {k['par']}) {k['prompt']}" for i,k in enumerate(KATAS, 1)) return f"""== llmdojo == -Assumed knowledge: the toolkit conventions taught by the startup doc() calls, doc(clik, pysk, edsk), doc(dsk, exh, rgsk), and doc(acp). Do not continue unless that output is visible in your context; if any is missing, read it now. +Assumed knowledge: the toolkit conventions taught by the docs your host's startup asked you to read: doc(pysk, edsk) (with the host's own skill module first, where it has one), doc(dsk, exh, rgsk), and doc(acp). Do not continue unless that output is visible in your context; if any is missing, read it now. Work only in: {d} %cd there first: chdir is never penalized, because the relative paths it enables make every later cell shorter - path tokens you never repeat are tokens saved, in the dojo and in real work. Scoring: kernel cell = 1 stroke; Bash tool call = 2; each print() call = +1. The tooling's reprs are designed to be optimally useful read bare, so end each cell with a bare expression and read what comes back. `doc()` returns a `PrettyString`: leave it as the final expression when you want to see it, or assign it when you do not want it rendered, for instance for very large docs you want to search through. Cells of only doc()/list_pyskills()/imports are free (bare calls, NOT wrapped in print()), as are comment-only narration cells and chdir cells (%cd / os.chdir). diff --git a/llmdojo/dojo_data/capture_prompt.md b/llmdojo/dojo_data/capture_prompt.md index e3ab532..1968b6f 100644 --- a/llmdojo/dojo_data/capture_prompt.md +++ b/llmdojo/dojo_data/capture_prompt.md @@ -1,22 +1,31 @@ # llmdojo capture -This session will become a worked example for future coding sessions. Complete the dojo cleanly on the first attempt. After any host-required skill reads, use only the clikernel MCP server's `execute` tool. Do not launch a terminal process or use a stream protocol. +This session will become a worked example for future coding sessions. You will get two prompts, a bootstrap prompt and then a dojo prompt. Each section below lists the cells that answer one prompt. Run exactly those cells, in order, one per kernel call, with no extra cells or warm-ups. After any host-required skill reads, use only the clikernel MCP server's `execute` tool. Do not launch a terminal process or use a stream protocol. -Run exactly the numbered cells below, in order, one per kernel call, with no extra cells or warm-ups. Before each cell, you may write one short line in your own words about what the last output showed. Narrate the work itself; never mention these numbered steps or that you were given them. If any step errors, stop and report it instead of improvising. +Before each cell, you may write one short line in your own words about what the last output showed. Narrate the work itself; never mention these numbered steps or that you were given them. If any step errors, stop and report it instead of improvising. + +## Bootstrap prompt + +Answer it with these cells, then reply exactly `OK I'm ready.` and stop. 1. `doc(clik, pysk, edsk)` 2. `doc(dsk, exh, rgsk)` 3. `doc(acp)` 4. `list_pyskills()` -5. `dojo_start()` -6. `%cd ` -7. `# kata 1` -8. `doc(find_msgs, view_dlg)` -9. `view_dlg('nbs/01_api.ipynb')` -10. `# kata 2` -11. `doc(lnhashview_file, file_exhash)` -12. `lnhashview_file('core.py')` -13. This cell exactly: + +## Dojo prompt + +Answer it with these cells, cleanly on the first attempt. + +1. `dojo_start()` +2. `%cd ` +3. `# kata 1` +4. `doc(find_msgs, view_dlg)` +5. `view_dlg('nbs/01_api.ipynb')` +6. `# kata 2` +7. `doc(lnhashview_file, file_exhash)` +8. `lnhashview_file('core.py')` +9. This cell exactly: ```python file_exhash('core.py', @@ -28,24 +37,24 @@ file_exhash('core.py', ) ``` -14. `# kata 3` -15. `lnhashview_file('tmpl.py')` -16. A cell whose first line is `%%exhash tmpl.py 4|dad2|,13|913e| c` and whose remaining lines are the replacement function from kata 3's card, byte for byte. -17. `# kata 4` -18. `doc(cell_exhash)` -19. `find_msgs(header_section='Retries', dlg='nbs/01_api.ipynb')` -20. This cell exactly: +10. `# kata 3` +11. `lnhashview_file('tmpl.py')` +12. A cell whose first line is `%%exhash tmpl.py 4|dad2|,13|913e| c` and whose remaining lines are the replacement function from kata 3's card, byte for byte. +13. `# kata 4` +14. `doc(cell_exhash)` +15. `find_msgs(header_section='Retries', dlg='nbs/01_api.ipynb')` +16. This cell exactly: ```python %%exhash nbs/01_api.ipynb d4f97726 % c On a connection error, `fetch_daily` retries the request twice more, making 3 attempts in all before giving up. ``` -21. `# kata 5` -22. `import report` -23. `doc(report.daily_report)` -24. `report.daily_report(report.SAMPLE, style='tagged')` -25. This cell exactly: +17. `# kata 5` +18. `import report` +19. `doc(report.daily_report)` +20. `report.daily_report(report.SAMPLE, style='tagged')` +21. This cell exactly: ```python dojo_score(bash_calls=0, diff --git a/llmdojo/dojo_data/claude_store/meta.json b/llmdojo/dojo_data/claude_store/meta.json index 84958a5..341d3e2 100644 --- a/llmdojo/dojo_data/claude_store/meta.json +++ b/llmdojo/dojo_data/claude_store/meta.json @@ -1 +1 @@ -{"cid": "24b2", "v": "0.0.4:5", "doced": ["acp", "cell_exhash", "clik", "daily_report", "dsk", "edsk", "exh", "file_exhash", "find_msgs", "lnhashview_file", "pysk", "report.daily_report", "rgsk", "view_dlg"]} \ No newline at end of file +{"cid": "dbfc", "v": "0.0.4:5", "doced": ["acp", "cell_exhash", "clik", "daily_report", "dsk", "edsk", "exh", "file_exhash", "find_msgs", "lnhashview_file", "pysk", "report.daily_report", "rgsk", "view_dlg"]} \ No newline at end of file diff --git a/llmdojo/dojo_data/claude_store/template.jsonl b/llmdojo/dojo_data/claude_store/template.jsonl index 0f6995b..3dbd13c 100644 --- a/llmdojo/dojo_data/claude_store/template.jsonl +++ b/llmdojo/dojo_data/claude_store/template.jsonl @@ -1,60 +1,62 @@ -{"type": "user", "uuid": "e772df4b-af54-5fb0-9aee-c743d1b9964a", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/Users/jhoward/aai-ws", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "user", "content": "Bootstrap and complete the dojo and tell me when you're ready."}} -{"type": "assistant", "uuid": "8f839a77-0432-503f-81fa-7afe5e8c71b8", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/Users/jhoward/aai-ws", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "assistant", "content": [{"type": "text", "text": "Bootstrapping: the startup script has already run its imports, so I read the tooling docs, check the catalog, then take the dojo."}, {"type": "tool_use", "id": "toolu_01VSqyGbsLQBosaqN92Tz7KB", "name": "mcp__clikernel__execute", "input": {"code": "doc(clik, pysk, edsk)"}}], "id": "msg_1a6f1b5fe36d5bacbdb3dc5e", "stop_reason": "tool_use", "stop_sequence": null, "stop_details": null, "usage": {"input_tokens": 18, "output_tokens": 43, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 0}}, "requestId": "req_fccda856db615c349fbbd400"} -{"type": "user", "uuid": "fe602d38-a513-5f5e-807a-64d28fa2a0fc", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/Users/jhoward/aai-ws", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "user", "content": [{"type": "tool_result", "tool_use_id": "toolu_01VSqyGbsLQBosaqN92Tz7KB", "content": "# module clikernel.skill:\n\n\"\"\"Use the `clikernel` MCP session as the default workspace for Python work: reading and changing files, notebook work, trying things out, checking how a library behaves, and reshaping data. One session stays open, so imports and variables carry between calls. Read this before writing, running, or debugging Python code in a session with `clikernel` connected.\n\nPrefer it over one-off Python scripts (`python -c`, shell heredocs). Prefer in-kernel tools over shell equivalents when they exist: file search and directory listing go through the `rgapi` pyskill (`rg()`/`fd()`/`ls()`), and GitHub and local git work through the `ghapi` pyskill, when those are installed. Shell commands remain the right tool for project test/build commands and non-Python tools.\n\n# Starting and stopping\n\nThe first `execute` creates a kernel and reports what it imported: read that banner, it says what to do next. That kernel stops when the conversation ends. Use a bare `connect` instead when the kernel should stay running afterwards, and tell the user its id.\n\nTo continue earlier work, or to use the user's solveit kernel, `list_kernels` then `connect` with the id. Attaching runs no setup: the kernel's live state is the point.\n\n`restart` gives a fresh interpreter under the same id, so redo any setup. `interrupt` stops a long `execute` and keeps state. If a reply says the kernel has stopped, `connect` again. If `connect` fails, the gateway server is not running: tell the user rather than working around it.\n\nRemote gateways are the same verbs with a `host`, named in `~/.config/clikernel/gateways.toml`.\n\n# Working in it\n\n- Magics work as written. `%cd` expands `~` and is the way to change directory: prefer it over `os.chdir`.\n- Only the last expression in a cell displays. `print(...)` any earlier value you need to see.\n- Everything a cell outputs lands in the conversation. Be selective: `len(v)` first, then decide what to show.\n- Don't re-run an `import` already run this session. If a name raises `NameError`, the kernel restarted or is newly attached: redo setup.\n- After an `nbdev-export` (or any edit to a module already imported), `importlib.reload` that module and re-import any names you hold from it: a name bound by `from x import y` keeps the old object, while a `@patch`ed method refreshes with the reload because the patch writes onto the shared class. Restart only when a class you hold instances of was itself redefined. Check what is loaded by calling it, never with `inspect.getsource`, which reads the file on disk.\n- Try the simple import or API call first, before changing the environment, monkeypatching, or adding setup.\n\"\"\"\n\n# module pyskills.skill:\n\n\"\"\"Pyskills are tool modules that Python packages register so you can find them without importing everything. `list_pyskills()` names the ones installed, with a one-line description each, and needs no imports. Load one with a normal import, then read its docs with `doc()`.\n\nUse `doc()` at increasing detail, module first, then the specific class or function before its first use:\n\n import pyskills.skill\n doc(pyskills.skill) # module overview: classes, functions, submodules\n doc(SkillTestClass, skill_test_func) # symbol detail: full signatures with docments, one section each\n\nNormally use `from import *` when loading a pyskill: each pyskill's `__all__` is carefully curated, so a star import brings in exactly the intended API.\n\n`doc()` returns a `PrettyString`. A bare final call is rendered by IPython; assigning the result is silent. Assign it when you do not want it rendered, for instance for very large docs you want to search through.\n\nDoc the module once while its output is still visible in the conversation, then doc each class or function right before its first call. This is conversation state, not Python-process state: read it again when the earlier output is no longer visible, not merely because the kernel restarted.\n\nIn a module overview, a trailing `...` on a line means docments and usage notes exist beyond the signature and first docstring line, and only `doc(func)` shows them: read it before the first call, however complete the line looks. A line with no `...` is that function's whole doc. A `**name` collector (other than `**kwargs` itself) is a shared param group: its params are listed once under `## shared params:` and are passed as ordinary keyword args. `doc` takes several objects at once, each returned as its own section, so batch the reads.\n\nWhen more than one pyskill looks like a candidate for a task, `doc()` each candidate rather than guessing from the one-line descriptions: some specialize by input type (e.g. `fastcore.tools` for plain text and files vs `aidialog.dlgskill` for notebooks and dialogs; prefer `exhash.skill` for text editing when it's available) and the short description won't always make the distinction clear.\n\nPyskill results are designed so the right call answers the question directly. Post-processing a result with generic Python (a `split`/`join`/slice/comprehension over its output) is a workaround smell: it usually means the call was wrong or a parameter was missed. Check `doc()` for the parameter or sibling function that answers directly; if it genuinely doesn't exist, propose extending the module rather than bridging with ad hoc code.\n\nThe same smell applies on the way in: wrapping an argument in `str()`, `expanduser()`, pre-escaping, or path-joining that the call already handles means either the docments weren't read or the tooling or its docs need fixing. Check the parameter before dressing the argument, and tell the user when the most ergonomic argument handling doesn't exist or isn't documented: improving tooling is always first priority.\n\nResults are built to be read as their bare reprs: end the cell with the bare expression rather than `print(...)`, which flattens a tuned display to plain `str()`. If a bare result ever reads worse than a printed or reformatted version, the repr is deficient: fix it or tell the user, never quietly work around it.\n\nSummarize what a pyskill's docs or results say rather than dumping the full output verbatim, unless the user actually needs to see all of it.\n\n`doc()` works on *all* python modules, not only pyskills.\n\n`xdir(sym, q=None)` complements `doc()`: it lists an object's public names, filtered by an optional case-insensitive regex. Use it when a module, class, or dynamic API surface is too big to `doc()` whole, e.g. `xdir(page.emulation, 'viewport')` on a fastcdp CDP domain, then `doc()` the match before calling it.\n\n`info_md(obj, source=False)` (from `ipykernel_helper`, preloaded by clikernel startup where installed) is the third way to read an object: IPython's `?` -- or `??` with `source=True` -- rendered as markdown. Reach for it when you want an object's real signature, docstring, and source together, rather than `inspect.getsource`/`inspect.signature` or bare `?`/`??`.\n\n`from pyskills import createskill; doc(createskill)` for how to build and register your own pyskill modules.\n\"\"\"\n\n## types:\n- class SkillTestClass(str): ... # Some class.\n\n## functions:\n- async def async_skill_test_func(x: int = 0) -> str: ... # A test function\u2026\n- def skill_test_func(x: int = 0) -> str: ... # A test function\u2026\n\n## elided: 1 submodules. `doc('pyskills.skill', all=True)` lists them.\n\n# module fastcore.editskill:\n\n\"\"\"Text, file, cell, and notebook editing from `fastcore.tools` and `fastcore.nbio`, plus the conventions the whole fastai editing toolkit follows. Read this before working with the editing tools in any package that shares them.\n\n`from fastcore.editskill import *` loads the fastcore editing layer: the text primitives and file tools of `fastcore.tools`, and the notebook I/O and cell editors of `fastcore.nbio`. Sibling packages extend the same toolkit, and where they are installed prefer them as noted: `exhash` for hash-verified editing, `rgapi` for search, `aidialog`/`dialoghelper` for dialogs.\n\n## Places\n\nEach editing function works on one place: text (a str in memory), a file (a path on disk), a cell (one cell's source in an .ipynb file, addressed by `path, cell_id`), or a notebook (the parsed .ipynb; `Notebook` and `NbCell` are its held-object forms). These are the representation layer: an .ipynb is a file of cells, whatever produced it. The dialog layer above adds the msg and dlg places: a Solveit dialog is an .ipynb whose cells are messages (notes, runnable code, prompt/reply pairs), and `aidialog.dlgskill` and `dialoghelper` provide the message tools, following the conventions here with their own nouns. The word picks the layer: cell tools answer representation questions (\"why does Jupyter reject this file?\"), msg tools answer content questions (\"what does this message say?\").\n\nThe section layer sits above both. `open_doc` (in exhash) parses a file (`fname=`, or a `Path` as `src`), URL (an `https?://` str), or text (any other str) into a `Section` tree, with sections taken from Markdown headings, tree-sitter definitions in code, or md-heading cells in a notebook. A section points at a span of lines in a file, or a run of cells in a notebook. To change what a section contains, edit those lines or cells with the file and cell tools.\n\n## Naming\n\nTwo name shapes cover the toolkit, and the pivot is the verb's direct object:\n\n- An operation on a whole place takes the place as its noun: verb_place. `view_file`, `create_file`, `read_nb`, `write_nb`, `view_cell`, `validate_nb`; in the dialog layer `view_msg` and `view_dlg`. Coined verbs follow the same shape: `lnhashview_cell` is \"lnhashview this cell\". When the verb's object is instead the unit inside the place, and that unit names its place uniquely, no prefix is needed - the unit noun is the place signal: `find_msgs`, `add_msg`, `del_msgs` (msgs live only in dialogs), `find_cells`, `summary_nb`'s rows (cells live only in notebooks).\n- An operation within a place already owns its noun (`insert_line`, `del_lines`, `replace_lines`, `str_replace`), so the place prefixes as a namespace and the op name survives intact: place_op, as in `file_del_lines`, `cell_del_lines`, `msg_del_lines`. The bare op names are the text-level primitives, and every place version keeps the identical signature after its address arguments, so each family is learned once and recognized everywhere.\n\nThe exceptions are deliberate and closed. `str_replace` keeps the name and argument order established by Anthropic's text editor tool. Instrument-named ops put the instrument first and elide their unit: `ast_replace` (the AST pattern is how the edit finds its target) and `exhash` (hash-verified line addresses travel inside its commands), place-prefixed like any other line-level op: `file_ast_replace`, `msg_ast_replace`, `file_exhash`, `cell_exhash`. Converters are named x2y (`nb2dict`, `cell2xml`; in aidialog, `dlg` on exactly one side of every converter), and on a held object the converter is a `to_y` method (`nb.to_dict()`). Plural marks arity: `view_cell` takes one cell, `lnhashview_cells` several, `del_msgs` many.\n\n## Parameters\n\nOne vocabulary, identical wherever it appears:\n\n- The place's address comes first (`text`; `path`; `path, cell_id`; a message `id`), the new text next, and ambient context last as keyword-only (message tools name their dialog that way).\n- `start_line`/`end_line`: 1-based, inclusive, `None` for first/last, negative counting from the end. `del_lines` accepts no defaults: state the range.\n- `re_filter`/`invert_filter`: restrict an edit to lines matching (or not matching) a regex, like ex's `g//` and `g!//`; combines with the range.\n- Searches read patterns as regex by default; editors read them as literal text until `use_regex=True`. Searching is read-only, so its default favors power; editing favors caution.\n- `nums` and `lnhashs` on any view: line numbers, or `lineno|hash|` addresses. `maxlen` caps characters per summary line; `trunc_out`/`trunc_in` truncate outputs and sources in dialog views.\n- Search tools share one filter vocabulary: `pattern` first, `root='.'`, and the same include/exclude/ext/hidden/ignore block across `fd`, `ls`, `rg`, and `nbrg`. Variants differ by defaults, not API: `ls` is `fd` with listing defaults. Boolean filters narrow as `only_*` and widen as `include_*`.\n- `context=` counts whatever units the place has: lines (or blocks in summary mode) for files, cells for notebooks, messages for dialogs. Dialog search defaults to context 1 because the neighbouring note usually explains the match: the why lives next to the what.\n- Every editor returns a diff (\"none: No changes.\" when nothing changed). The diff is the verification: read it instead of re-viewing the target.\n\n## Functions and methods\n\nEvery operation has two shapes with one contract each. The function is a transaction: it addresses a file by path, applies the edit, writes, and returns a diff. The method is a session: it changes the object in hand, and nothing reaches disk until an explicit save. The correspondence is mechanical - the method is the function minus its address arguments, keeping its name except that a place word which became `self` drops: `cell_str_replace(path, cell_id, ...)` is `c.str_replace(...)` on a held `NbCell`, `find_cells(path, pat)` is `nb.find_cells(pat)` on a held `Notebook`. Function-side reads return plain snapshot rows carrying addresses, source, and meta; session-side reads return the objects themselves. Use one shape at a time per file: save before switching to functions, reopen after.\n\n## Addresses\n\nEdits say where with line numbers, lnhash addresses, or section tokens. Take addresses from the read you were already doing instead of with a second look: views accept `nums=True` or `lnhashs=True`, and searches return addresses directly (`rg(lnhashs=True)`). Prefer lnhash addresses whenever `exhash` is installed: they are verified against current content at edit time, so a stale address fails loudly instead of editing nearby text, which is exactly why taking addresses early works. Plain line numbers are unverified and shift as edits apply, so re-view after each edit and apply multi-edits bottom-to-top. `exhash.skill` owns the address format and the verified editor. Section tokens make a long document navigable and editable a piece at a time. An outline row shows one, as in `1.6.|12|a3f2|,45|b1c3|`: the section number, then the lnhash addresses of its first and last lines. `d.at(token)` returns that section with its hash checked, `d.view(tok1, tok2)` prints the rendered text of each, and the same boundary pair drops straight into a range edit: `file_exhash(path, ('12|a3f2|,45|b1c3|', 'c', new_text))`. Read `exhash.skill` before working through a large Markdown, code, or notebook file, or before reading documentation from a URL: `open_doc` also searches within a tree and follows links, which is how the llms.txt docs workflow runs.\n\nWhen you don't yet know where to edit, locate with a summary first: `rgapi`'s `rg(summary=True)` and `nbrg`, aidialog's `summary_dlg`, and exhash's `open_doc` outlines each show one row per natural unit (block, cell, message, section), carrying the unit's address. For prose, config, and other paragraph-shaped text, blocks are usually the right unit for reading too: one summary row shows the whole matched paragraph with its boundary addresses, where a line-mode hit shows a fragment that then needs a context view. Summaries locate, views read, addresses edit, diffs confirm.\n\n## What's where\n\n- `fastcore.tools`: text primitives, file tools, and `line_hash`/`lnhash`/`lnhash_at` for creating addresses without exhash installed.\n- `fastcore.nbio`: notebook read/write/validate/repair, cell construction, cell editors, and the `Notebook`/`NbCell` session objects with their snapshot queries (`find_cells`, `summary_nb`).\n- `exhash.skill`: hash-verified editing for files and cells, plus `open_doc` section outlines for Markdown, code, and notebooks; prefer it for edits where installed.\n- `rgapi.skill`: `rg`/`fd`/`ls`/`nbrg` search with lnhash output, and `rgstr` to search text already in hand.\n- `remold`: structural search and rewrite for Python source (declarative ast-grep rules, LibCST matcher transforms, symbol queries); the engine behind `ast_replace`.\n- `aidialog.dlgskill`, `dialoghelper`: the dialog layer, including its own theory of dialogs and projections.\n\nDocs: https://fastcore.fast.ai/tools.html.md and https://fastcore.fast.ai/nbio.html.md\n\"\"\"\n\n## types:\n- class Notebook: ... # Read, query, and edit Jupyter notebooks\n- class NbCell(AttrDict,adict,dict)\n\n## functions:\n- def insert_line(text: str, insert_line: int, new_str: str): ... # Insert new_str at specified line number\u2026\n- def str_replace(text: str, old_str: str, new_str: str, **replace_params): ... # Replace occurrence(s) of old_str with new_str\u2026\n- def strs_replace(text: str, old_strs: list[str], new_strs: list[str], **replace_params): ... # Replace multiple strings simultaneously\u2026\n- def replace_lines(text: str, start_line: int = None, end_line: int = None, new_content: str = ''): ... # Replace line range with new content; the defaults replace the entire contents\u2026\n- def del_lines(text: str, start_line: int, end_line: int, re_filter: str = None, invert_filter: bool = False): ... # Delete line range; deletion is destructive, so both line numbers must be given explicitly (`1, -1` for all lines)\u2026\n- def ast_replace(text: str, repls: list): ... # Apply ast-grep structural pattern replacements to python source\u2026\n- def file_insert_line(path: str, insert_line: int, new_str: str): ... # Insert new_str at specified line number\u2026\n- def file_str_replace(path: str, old_str: str, new_str: str, **replace_params): ... # Replace occurrence(s) of old_str with new_str\u2026\n- def file_strs_replace(path: str, old_strs: list[str], new_strs: list[str], **replace_params): ... # Replace multiple strings simultaneously\u2026\n- def file_replace_lines(path: str, start_line: int = None, end_line: int = None, new_content: str = ''): ... # Replace line range with new content; the defaults replace the entire contents\u2026\n- def file_del_lines(path: str, start_line: int, end_line: int, re_filter: str = None, invert_filter: bool = False): ... # Delete line range; deletion is destructive, so both line numbers must be given explicitly (`1, -1` for all lines)\u2026\n- def file_ast_replace(path: str, repls: list): ... # Apply ast-grep structural pattern replacements to python source\u2026\n- def view_file(path: str, start_line: int = 1, end_line: int = None, nums: bool = True, lnhashs: bool = False): ... # Read file contents, optionally limited to 1-based line range\u2026\n- def create_file(path: str, contents: str, overwrite: bool = False): ... # Create a new file with contents. Error if file exists, unless `overwrite`.\u2026\n- def line_hash(line: str) -> str: ... # 4-char hex hash of `line`\u2026\n- def lnhash(lineno: int, line: str) -> str: ... # `lineno|hash|` exhash address for `line` at `lineno`\u2026\n- def lnhash_at(s: str | list | tuple, line: int) -> str: ... # `lineno|hash|` exhash address of line `line` of `s`\u2026\n- def read_nb(path): ... # Return notebook at `path` (expands `~`) [1bdc]\n- def write_nb(nb, path): ... # Write `nb` to `path` (expands `~`) [a307]\n- def new_nb(cells=None, meta=None, nbformat=4, nbformat_minor=5): ... # Returns an empty new notebook [dee4]\n- def mk_cell(text, cell_type='code', **kwargs): ... # Create an `NbCell` containing `text`\u2026\n- def validate_nb(nb): ... # Raise `ValueError` for structural problems in notebook `nb`; returns it unchanged if fine [141c]\n- def validate_cell(cell, idx=None): ... # Raise `ValueError` for structural problems in notebook cell dict `cell`; returns it unchanged if fine [0ba9]\n- def repair_nb(nb): ... # Fix deterministic structural problems in `nb`, returning a list of repairs made [137e]\n- def repair_cell(cell, idx=None): ... # Fix deterministic structural problems in `cell`, returning a list of repairs made [8864]\n- def view_cell(path: str, cell_id: str, start_line: int = 1, end_line: int = None, nums: bool = True, lnhashs: bool = False, incl_out: bool = False, trunc_out: bool = True): ... # View a cell's source, optionally limited to 1-based line range\u2026\n- def cell_insert_line(path: str, cell_id: str, insert_line: int, new_str: str): ... # Insert new_str at specified line number\u2026\n- def cell_str_replace(path: str, cell_id: str, old_str: str, new_str: str, **replace_params): ... # Replace occurrence(s) of old_str with new_str\u2026\n- def cell_strs_replace(path: str, cell_id: str, old_strs: list[str], new_strs: list[str], **replace_params): ... # Replace multiple strings simultaneously\u2026\n- def cell_replace_lines(path: str, cell_id: str, start_line: int = None, end_line: int = None, new_content: str = ''): ... # Replace line range with new content; the defaults replace the entire contents\u2026\n- def cell_del_lines(path: str, cell_id: str, start_line: int, end_line: int, re_filter: str = None, invert_filter: bool = False): ... # Delete line range; deletion is destructive, so both line numbers must be given explicitly (`1, -1` for all lines)\u2026\n- def cell_ast_replace(path: str, cell_id: str, repls: list): ... # Apply ast-grep structural pattern replacements to python source\u2026\n- def find_cells(path, pat: str = '', cell_type: str = None, ids='', context: int = None): ... # Snapshot `FoundCells` for matching cells in the notebook at `path`\u2026\n- def summary_nb(path, maxlen: int = 180): ... # One snapshot line per cell of the notebook at `path`\u2026\n\n## shared params:\n- **replace_params (from `str_replace`):\n start_line: int = None, # Optional 1-based start line to limit search\n end_line: int = None, # Optional 1-based end line to limit search\n n_matches: int = None, # Max replacements (None=all)\n re_filter: str = None, # If provided, only process lines matching this regex (like g// in ex)\n invert_filter: bool = False, # Invert the filter (like g!// in ex)\n use_regex: bool = False, # Treat old_str as a regex, and new_str as an `re.sub` template?"}]}, "toolUseResult": [{"type": "text", "text": "# module clikernel.skill:\n\n\"\"\"Use the `clikernel` MCP session as the default workspace for Python work: reading and changing files, notebook work, trying things out, checking how a library behaves, and reshaping data. One session stays open, so imports and variables carry between calls. Read this before writing, running, or debugging Python code in a session with `clikernel` connected.\n\nPrefer it over one-off Python scripts (`python -c`, shell heredocs). Prefer in-kernel tools over shell equivalents when they exist: file search and directory listing go through the `rgapi` pyskill (`rg()`/`fd()`/`ls()`), and GitHub and local git work through the `ghapi` pyskill, when those are installed. Shell commands remain the right tool for project test/build commands and non-Python tools.\n\n# Starting and stopping\n\nThe first `execute` creates a kernel and reports what it imported: read that banner, it says what to do next. That kernel stops when the conversation ends. Use a bare `connect` instead when the kernel should stay running afterwards, and tell the user its id.\n\nTo continue earlier work, or to use the user's solveit kernel, `list_kernels` then `connect` with the id. Attaching runs no setup: the kernel's live state is the point.\n\n`restart` gives a fresh interpreter under the same id, so redo any setup. `interrupt` stops a long `execute` and keeps state. If a reply says the kernel has stopped, `connect` again. If `connect` fails, the gateway server is not running: tell the user rather than working around it.\n\nRemote gateways are the same verbs with a `host`, named in `~/.config/clikernel/gateways.toml`.\n\n# Working in it\n\n- Magics work as written. `%cd` expands `~` and is the way to change directory: prefer it over `os.chdir`.\n- Only the last expression in a cell displays. `print(...)` any earlier value you need to see.\n- Everything a cell outputs lands in the conversation. Be selective: `len(v)` first, then decide what to show.\n- Don't re-run an `import` already run this session. If a name raises `NameError`, the kernel restarted or is newly attached: redo setup.\n- After an `nbdev-export` (or any edit to a module already imported), `importlib.reload` that module and re-import any names you hold from it: a name bound by `from x import y` keeps the old object, while a `@patch`ed method refreshes with the reload because the patch writes onto the shared class. Restart only when a class you hold instances of was itself redefined. Check what is loaded by calling it, never with `inspect.getsource`, which reads the file on disk.\n- Try the simple import or API call first, before changing the environment, monkeypatching, or adding setup.\n\"\"\"\n\n# module pyskills.skill:\n\n\"\"\"Pyskills are tool modules that Python packages register so you can find them without importing everything. `list_pyskills()` names the ones installed, with a one-line description each, and needs no imports. Load one with a normal import, then read its docs with `doc()`.\n\nUse `doc()` at increasing detail, module first, then the specific class or function before its first use:\n\n import pyskills.skill\n doc(pyskills.skill) # module overview: classes, functions, submodules\n doc(SkillTestClass, skill_test_func) # symbol detail: full signatures with docments, one section each\n\nNormally use `from import *` when loading a pyskill: each pyskill's `__all__` is carefully curated, so a star import brings in exactly the intended API.\n\n`doc()` returns a `PrettyString`. A bare final call is rendered by IPython; assigning the result is silent. Assign it when you do not want it rendered, for instance for very large docs you want to search through.\n\nDoc the module once while its output is still visible in the conversation, then doc each class or function right before its first call. This is conversation state, not Python-process state: read it again when the earlier output is no longer visible, not merely because the kernel restarted.\n\nIn a module overview, a trailing `...` on a line means docments and usage notes exist beyond the signature and first docstring line, and only `doc(func)` shows them: read it before the first call, however complete the line looks. A line with no `...` is that function's whole doc. A `**name` collector (other than `**kwargs` itself) is a shared param group: its params are listed once under `## shared params:` and are passed as ordinary keyword args. `doc` takes several objects at once, each returned as its own section, so batch the reads.\n\nWhen more than one pyskill looks like a candidate for a task, `doc()` each candidate rather than guessing from the one-line descriptions: some specialize by input type (e.g. `fastcore.tools` for plain text and files vs `aidialog.dlgskill` for notebooks and dialogs; prefer `exhash.skill` for text editing when it's available) and the short description won't always make the distinction clear.\n\nPyskill results are designed so the right call answers the question directly. Post-processing a result with generic Python (a `split`/`join`/slice/comprehension over its output) is a workaround smell: it usually means the call was wrong or a parameter was missed. Check `doc()` for the parameter or sibling function that answers directly; if it genuinely doesn't exist, propose extending the module rather than bridging with ad hoc code.\n\nThe same smell applies on the way in: wrapping an argument in `str()`, `expanduser()`, pre-escaping, or path-joining that the call already handles means either the docments weren't read or the tooling or its docs need fixing. Check the parameter before dressing the argument, and tell the user when the most ergonomic argument handling doesn't exist or isn't documented: improving tooling is always first priority.\n\nResults are built to be read as their bare reprs: end the cell with the bare expression rather than `print(...)`, which flattens a tuned display to plain `str()`. If a bare result ever reads worse than a printed or reformatted version, the repr is deficient: fix it or tell the user, never quietly work around it.\n\nSummarize what a pyskill's docs or results say rather than dumping the full output verbatim, unless the user actually needs to see all of it.\n\n`doc()` works on *all* python modules, not only pyskills.\n\n`xdir(sym, q=None)` complements `doc()`: it lists an object's public names, filtered by an optional case-insensitive regex. Use it when a module, class, or dynamic API surface is too big to `doc()` whole, e.g. `xdir(page.emulation, 'viewport')` on a fastcdp CDP domain, then `doc()` the match before calling it.\n\n`info_md(obj, source=False)` (from `ipykernel_helper`, preloaded by clikernel startup where installed) is the third way to read an object: IPython's `?` -- or `??` with `source=True` -- rendered as markdown. Reach for it when you want an object's real signature, docstring, and source together, rather than `inspect.getsource`/`inspect.signature` or bare `?`/`??`.\n\n`from pyskills import createskill; doc(createskill)` for how to build and register your own pyskill modules.\n\"\"\"\n\n## types:\n- class SkillTestClass(str): ... # Some class.\n\n## functions:\n- async def async_skill_test_func(x: int = 0) -> str: ... # A test function\u2026\n- def skill_test_func(x: int = 0) -> str: ... # A test function\u2026\n\n## elided: 1 submodules. `doc('pyskills.skill', all=True)` lists them.\n\n# module fastcore.editskill:\n\n\"\"\"Text, file, cell, and notebook editing from `fastcore.tools` and `fastcore.nbio`, plus the conventions the whole fastai editing toolkit follows. Read this before working with the editing tools in any package that shares them.\n\n`from fastcore.editskill import *` loads the fastcore editing layer: the text primitives and file tools of `fastcore.tools`, and the notebook I/O and cell editors of `fastcore.nbio`. Sibling packages extend the same toolkit, and where they are installed prefer them as noted: `exhash` for hash-verified editing, `rgapi` for search, `aidialog`/`dialoghelper` for dialogs.\n\n## Places\n\nEach editing function works on one place: text (a str in memory), a file (a path on disk), a cell (one cell's source in an .ipynb file, addressed by `path, cell_id`), or a notebook (the parsed .ipynb; `Notebook` and `NbCell` are its held-object forms). These are the representation layer: an .ipynb is a file of cells, whatever produced it. The dialog layer above adds the msg and dlg places: a Solveit dialog is an .ipynb whose cells are messages (notes, runnable code, prompt/reply pairs), and `aidialog.dlgskill` and `dialoghelper` provide the message tools, following the conventions here with their own nouns. The word picks the layer: cell tools answer representation questions (\"why does Jupyter reject this file?\"), msg tools answer content questions (\"what does this message say?\").\n\nThe section layer sits above both. `open_doc` (in exhash) parses a file (`fname=`, or a `Path` as `src`), URL (an `https?://` str), or text (any other str) into a `Section` tree, with sections taken from Markdown headings, tree-sitter definitions in code, or md-heading cells in a notebook. A section points at a span of lines in a file, or a run of cells in a notebook. To change what a section contains, edit those lines or cells with the file and cell tools.\n\n## Naming\n\nTwo name shapes cover the toolkit, and the pivot is the verb's direct object:\n\n- An operation on a whole place takes the place as its noun: verb_place. `view_file`, `create_file`, `read_nb`, `write_nb`, `view_cell`, `validate_nb`; in the dialog layer `view_msg` and `view_dlg`. Coined verbs follow the same shape: `lnhashview_cell` is \"lnhashview this cell\". When the verb's object is instead the unit inside the place, and that unit names its place uniquely, no prefix is needed - the unit noun is the place signal: `find_msgs`, `add_msg`, `del_msgs` (msgs live only in dialogs), `find_cells`, `summary_nb`'s rows (cells live only in notebooks).\n- An operation within a place already owns its noun (`insert_line`, `del_lines`, `replace_lines`, `str_replace`), so the place prefixes as a namespace and the op name survives intact: place_op, as in `file_del_lines`, `cell_del_lines`, `msg_del_lines`. The bare op names are the text-level primitives, and every place version keeps the identical signature after its address arguments, so each family is learned once and recognized everywhere.\n\nThe exceptions are deliberate and closed. `str_replace` keeps the name and argument order established by Anthropic's text editor tool. Instrument-named ops put the instrument first and elide their unit: `ast_replace` (the AST pattern is how the edit finds its target) and `exhash` (hash-verified line addresses travel inside its commands), place-prefixed like any other line-level op: `file_ast_replace`, `msg_ast_replace`, `file_exhash`, `cell_exhash`. Converters are named x2y (`nb2dict`, `cell2xml`; in aidialog, `dlg` on exactly one side of every converter), and on a held object the converter is a `to_y` method (`nb.to_dict()`). Plural marks arity: `view_cell` takes one cell, `lnhashview_cells` several, `del_msgs` many.\n\n## Parameters\n\nOne vocabulary, identical wherever it appears:\n\n- The place's address comes first (`text`; `path`; `path, cell_id`; a message `id`), the new text next, and ambient context last as keyword-only (message tools name their dialog that way).\n- `start_line`/`end_line`: 1-based, inclusive, `None` for first/last, negative counting from the end. `del_lines` accepts no defaults: state the range.\n- `re_filter`/`invert_filter`: restrict an edit to lines matching (or not matching) a regex, like ex's `g//` and `g!//`; combines with the range.\n- Searches read patterns as regex by default; editors read them as literal text until `use_regex=True`. Searching is read-only, so its default favors power; editing favors caution.\n- `nums` and `lnhashs` on any view: line numbers, or `lineno|hash|` addresses. `maxlen` caps characters per summary line; `trunc_out`/`trunc_in` truncate outputs and sources in dialog views.\n- Search tools share one filter vocabulary: `pattern` first, `root='.'`, and the same include/exclude/ext/hidden/ignore block across `fd`, `ls`, `rg`, and `nbrg`. Variants differ by defaults, not API: `ls` is `fd` with listing defaults. Boolean filters narrow as `only_*` and widen as `include_*`.\n- `context=` counts whatever units the place has: lines (or blocks in summary mode) for files, cells for notebooks, messages for dialogs. Dialog search defaults to context 1 because the neighbouring note usually explains the match: the why lives next to the what.\n- Every editor returns a diff (\"none: No changes.\" when nothing changed). The diff is the verification: read it instead of re-viewing the target.\n\n## Functions and methods\n\nEvery operation has two shapes with one contract each. The function is a transaction: it addresses a file by path, applies the edit, writes, and returns a diff. The method is a session: it changes the object in hand, and nothing reaches disk until an explicit save. The correspondence is mechanical - the method is the function minus its address arguments, keeping its name except that a place word which became `self` drops: `cell_str_replace(path, cell_id, ...)` is `c.str_replace(...)` on a held `NbCell`, `find_cells(path, pat)` is `nb.find_cells(pat)` on a held `Notebook`. Function-side reads return plain snapshot rows carrying addresses, source, and meta; session-side reads return the objects themselves. Use one shape at a time per file: save before switching to functions, reopen after.\n\n## Addresses\n\nEdits say where with line numbers, lnhash addresses, or section tokens. Take addresses from the read you were already doing instead of with a second look: views accept `nums=True` or `lnhashs=True`, and searches return addresses directly (`rg(lnhashs=True)`). Prefer lnhash addresses whenever `exhash` is installed: they are verified against current content at edit time, so a stale address fails loudly instead of editing nearby text, which is exactly why taking addresses early works. Plain line numbers are unverified and shift as edits apply, so re-view after each edit and apply multi-edits bottom-to-top. `exhash.skill` owns the address format and the verified editor. Section tokens make a long document navigable and editable a piece at a time. An outline row shows one, as in `1.6.|12|a3f2|,45|b1c3|`: the section number, then the lnhash addresses of its first and last lines. `d.at(token)` returns that section with its hash checked, `d.view(tok1, tok2)` prints the rendered text of each, and the same boundary pair drops straight into a range edit: `file_exhash(path, ('12|a3f2|,45|b1c3|', 'c', new_text))`. Read `exhash.skill` before working through a large Markdown, code, or notebook file, or before reading documentation from a URL: `open_doc` also searches within a tree and follows links, which is how the llms.txt docs workflow runs.\n\nWhen you don't yet know where to edit, locate with a summary first: `rgapi`'s `rg(summary=True)` and `nbrg`, aidialog's `summary_dlg`, and exhash's `open_doc` outlines each show one row per natural unit (block, cell, message, section), carrying the unit's address. For prose, config, and other paragraph-shaped text, blocks are usually the right unit for reading too: one summary row shows the whole matched paragraph with its boundary addresses, where a line-mode hit shows a fragment that then needs a context view. Summaries locate, views read, addresses edit, diffs confirm.\n\n## What's where\n\n- `fastcore.tools`: text primitives, file tools, and `line_hash`/`lnhash`/`lnhash_at` for creating addresses without exhash installed.\n- `fastcore.nbio`: notebook read/write/validate/repair, cell construction, cell editors, and the `Notebook`/`NbCell` session objects with their snapshot queries (`find_cells`, `summary_nb`).\n- `exhash.skill`: hash-verified editing for files and cells, plus `open_doc` section outlines for Markdown, code, and notebooks; prefer it for edits where installed.\n- `rgapi.skill`: `rg`/`fd`/`ls`/`nbrg` search with lnhash output, and `rgstr` to search text already in hand.\n- `remold`: structural search and rewrite for Python source (declarative ast-grep rules, LibCST matcher transforms, symbol queries); the engine behind `ast_replace`.\n- `aidialog.dlgskill`, `dialoghelper`: the dialog layer, including its own theory of dialogs and projections.\n\nDocs: https://fastcore.fast.ai/tools.html.md and https://fastcore.fast.ai/nbio.html.md\n\"\"\"\n\n## types:\n- class Notebook: ... # Read, query, and edit Jupyter notebooks\n- class NbCell(AttrDict,adict,dict)\n\n## functions:\n- def insert_line(text: str, insert_line: int, new_str: str): ... # Insert new_str at specified line number\u2026\n- def str_replace(text: str, old_str: str, new_str: str, **replace_params): ... # Replace occurrence(s) of old_str with new_str\u2026\n- def strs_replace(text: str, old_strs: list[str], new_strs: list[str], **replace_params): ... # Replace multiple strings simultaneously\u2026\n- def replace_lines(text: str, start_line: int = None, end_line: int = None, new_content: str = ''): ... # Replace line range with new content; the defaults replace the entire contents\u2026\n- def del_lines(text: str, start_line: int, end_line: int, re_filter: str = None, invert_filter: bool = False): ... # Delete line range; deletion is destructive, so both line numbers must be given explicitly (`1, -1` for all lines)\u2026\n- def ast_replace(text: str, repls: list): ... # Apply ast-grep structural pattern replacements to python source\u2026\n- def file_insert_line(path: str, insert_line: int, new_str: str): ... # Insert new_str at specified line number\u2026\n- def file_str_replace(path: str, old_str: str, new_str: str, **replace_params): ... # Replace occurrence(s) of old_str with new_str\u2026\n- def file_strs_replace(path: str, old_strs: list[str], new_strs: list[str], **replace_params): ... # Replace multiple strings simultaneously\u2026\n- def file_replace_lines(path: str, start_line: int = None, end_line: int = None, new_content: str = ''): ... # Replace line range with new content; the defaults replace the entire contents\u2026\n- def file_del_lines(path: str, start_line: int, end_line: int, re_filter: str = None, invert_filter: bool = False): ... # Delete line range; deletion is destructive, so both line numbers must be given explicitly (`1, -1` for all lines)\u2026\n- def file_ast_replace(path: str, repls: list): ... # Apply ast-grep structural pattern replacements to python source\u2026\n- def view_file(path: str, start_line: int = 1, end_line: int = None, nums: bool = True, lnhashs: bool = False): ... # Read file contents, optionally limited to 1-based line range\u2026\n- def create_file(path: str, contents: str, overwrite: bool = False): ... # Create a new file with contents. Error if file exists, unless `overwrite`.\u2026\n- def line_hash(line: str) -> str: ... # 4-char hex hash of `line`\u2026\n- def lnhash(lineno: int, line: str) -> str: ... # `lineno|hash|` exhash address for `line` at `lineno`\u2026\n- def lnhash_at(s: str | list | tuple, line: int) -> str: ... # `lineno|hash|` exhash address of line `line` of `s`\u2026\n- def read_nb(path): ... # Return notebook at `path` (expands `~`) [1bdc]\n- def write_nb(nb, path): ... # Write `nb` to `path` (expands `~`) [a307]\n- def new_nb(cells=None, meta=None, nbformat=4, nbformat_minor=5): ... # Returns an empty new notebook [dee4]\n- def mk_cell(text, cell_type='code', **kwargs): ... # Create an `NbCell` containing `text`\u2026\n- def validate_nb(nb): ... # Raise `ValueError` for structural problems in notebook `nb`; returns it unchanged if fine [141c]\n- def validate_cell(cell, idx=None): ... # Raise `ValueError` for structural problems in notebook cell dict `cell`; returns it unchanged if fine [0ba9]\n- def repair_nb(nb): ... # Fix deterministic structural problems in `nb`, returning a list of repairs made [137e]\n- def repair_cell(cell, idx=None): ... # Fix deterministic structural problems in `cell`, returning a list of repairs made [8864]\n- def view_cell(path: str, cell_id: str, start_line: int = 1, end_line: int = None, nums: bool = True, lnhashs: bool = False, incl_out: bool = False, trunc_out: bool = True): ... # View a cell's source, optionally limited to 1-based line range\u2026\n- def cell_insert_line(path: str, cell_id: str, insert_line: int, new_str: str): ... # Insert new_str at specified line number\u2026\n- def cell_str_replace(path: str, cell_id: str, old_str: str, new_str: str, **replace_params): ... # Replace occurrence(s) of old_str with new_str\u2026\n- def cell_strs_replace(path: str, cell_id: str, old_strs: list[str], new_strs: list[str], **replace_params): ... # Replace multiple strings simultaneously\u2026\n- def cell_replace_lines(path: str, cell_id: str, start_line: int = None, end_line: int = None, new_content: str = ''): ... # Replace line range with new content; the defaults replace the entire contents\u2026\n- def cell_del_lines(path: str, cell_id: str, start_line: int, end_line: int, re_filter: str = None, invert_filter: bool = False): ... # Delete line range; deletion is destructive, so both line numbers must be given explicitly (`1, -1` for all lines)\u2026\n- def cell_ast_replace(path: str, cell_id: str, repls: list): ... # Apply ast-grep structural pattern replacements to python source\u2026\n- def find_cells(path, pat: str = '', cell_type: str = None, ids='', context: int = None): ... # Snapshot `FoundCells` for matching cells in the notebook at `path`\u2026\n- def summary_nb(path, maxlen: int = 180): ... # One snapshot line per cell of the notebook at `path`\u2026\n\n## shared params:\n- **replace_params (from `str_replace`):\n start_line: int = None, # Optional 1-based start line to limit search\n end_line: int = None, # Optional 1-based end line to limit search\n n_matches: int = None, # Max replacements (None=all)\n re_filter: str = None, # If provided, only process lines matching this regex (like g// in ex)\n invert_filter: bool = False, # Invert the filter (like g!// in ex)\n use_regex: bool = False, # Treat old_str as a regex, and new_str as an `re.sub` template?"}]} -{"type": "assistant", "uuid": "95f7449d-8e28-5a3d-a0cf-12e49e5d1b00", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/Users/jhoward/aai-ws", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "assistant", "content": [{"type": "tool_use", "id": "toolu_01FUJHFXpnggen2yQbS3tQGE", "name": "mcp__clikernel__execute", "input": {"code": "doc(dsk, exh, rgsk)"}}], "id": "msg_919aafb2b8a251cd92138c9f", "stop_reason": "tool_use", "stop_sequence": null, "stop_details": null, "usage": {"input_tokens": 5036, "output_tokens": 9, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 0}}, "requestId": "req_ba7f224ffe2751b0858eb014"} -{"type": "user", "uuid": "8ad142fa-d69b-5038-94d3-587a6ba99d6e", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/Users/jhoward/aai-ws", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "user", "content": [{"type": "tool_result", "tool_use_id": "toolu_01FUJHFXpnggen2yQbS3tQGE", "content": "# module aidialog.dlgskill:\n\n\"\"\"Read, search, and edit dialogs and notebooks through aidialog's `Dialog` and `Message` classes\n\nUse this whenever the question or edit concerns a notebook's content: messages, sources, outputs, prompts and their replies.\n\n## The hierarchy\n\nNotebook work happens at three levels, and picking the right level is most of using this module well:\n\n- Content (this module): what the messages say and how they change. `summary_dlg`, `find_msgs`, `view_dlg`, the message editing operations.\n- Representation (`fastcore.nbio`): which keys exist, whether a file is schema-valid, whether bytes changed. Start with `validate_nb`/`validate_cell`, which name the cell at fault; use `read_nb` directly when the question is about the dict itself. For *plain* notebooks (no dialog semantics), nbio's `Notebook`/`NbCell` objects and `cell_*` functions are the content surface, and this module's word choice marks the layer: cells for notebooks, messages for dialogs.\n- Raw text: only when the file will not parse at all.\n\n## Core APIs\n\nThe function/method two-shapes contract is `fastcore.editskill`'s, learned once: the function is a transaction addressed by `dlg=` (an ipynb path, or None meaning the current dialog file: `set_dlg`); the method is a session on a held `Dialog` or `Message`, saved by an explicit `dlg.save()` (`msg_str_replace(id, ..., dlg=p)` \u27f7 `m.str_replace(...)`). Wrapping any message list in an ephemeral `Dialog(msgs)` makes the whole session surface available on it.\n\n- `summary_dlg(dlg)` / `d.summary()`: one `preview` per message, `id:t[directives]:content` (t: c=code n=note p=prompt r=raw; the bracket, as in nbio's `CellRow`, shows meta-form nbdev directives such as `[export]`), with a prompt's reply on a following `> ` line; a line cut short ends in a humanized `[size]`.\n- `find_msgs(pattern, dlg, ...)`: search by regex, type, errors, heading, ids, or a `pred` (`symdef_finder`/`symref_finder`/`ast_finder` build structural ones); `context` defaults to 1 even with `ids=`, because the neighbouring message usually explains the match -- retrieve and read it. Returns `MsgRow` snapshots (`id`, `msg_type`, `content`, `out`, `meta`) in a `MsgRows`; `d.find_msgs(...)` returns live `Message`s in a `FoundMsgs`. Both index by message id (exact or unique prefix) and refuse integer positions: a find result includes context rows, so `[0]` may be a neighbour of the match, and the raised error teaches the id idiom. Previews mark context rows with `-` in place of the first `:`. Every live message carries a `dlg` backref to its owning `Dialog`, so dialog-level operations are always in reach from a message in hand (e.g. `m.dlg.save()` after mutating `m.output` directly).\n- `view_dlg(dlg)` / `d.view()` / `view_msg(id)` / `m.view()` / `view_msgs(*ids)` / `msg2xml(m)` / `m.to_xml()`: full views in the shared `item2xml` grammar (a prompt's reply is its `` section; meta `nbdev` directives render as attrs, so a meta-exported message carries a bare `export`); `incl_out=True` on the line views appends the message's output the same way.\n- Structure: `add_msg`, `del_msgs`, `move_msgs`, `split_msg`, `merge_msgs`, `copy_msgs`/`cut_msgs`/`paste_msgs`, `create_dlg`, with session twins `d.move_msgs`, `m.split`, `d.merge_msgs`, `d.copy_msgs`/`d.cut_msgs`/`d.paste_msgs` (session adds go through `d.mk_message`, deletes through `d.remove_msgs`). `add_msg` with neither `before=` nor `after=` places after the current message (`set_cur_msg`, which a host sets and no write ever moves), or at the end when that is unset or gone. `add_msg` and `d.mk_message` take `meta=` and an `export=` shortcut for the meta `nbdev` export flag, readable and assignable as `m.meta_exported` (`m.exported` reads content and meta together, and is what `find_msgs(only_exp=True)` filters on). The `%%add_msg` magic takes its body verbatim: its line is `%%add_msg [dlg] [msg_type] [export] [before=|after=]`, where a bare path token is the dlg and a bare type name the msg_type, and keyword spellings win over bare tokens.\n- `update_msg(id, ..., dlg=)` / `m.update(...)`: one transaction for a message's attributes. Plain keywords assign (`content=`, `msg_type=`, `output=`, `meta=` replacing wholesale); `mergemeta` deep-merges into `meta` (a `None` value deletes its key); `export` sets the nbdev directive -- `True` adds, `False` removes (there is no negative form), and either migrates a content-form `#| export` line to meta. The function returns a diff of the message's XML plus a `meta:` line, so every change it can make is visible.\n- The `%nbrun` line magic runs code cells from the current notebook in the running kernel, by cell id prefix, same grammar: bare tokens are flags (`above`, `below`, `all`, `exported`, `ignore_eval`, `continue_on_error`, `show`) or id prefixes, and `fname=` overrides the notebook for one call. Only cells named by id display their output: bulk-selected cells run silently (`show` overrides), errors always surface naming the failed cell, and the run ends with a `nbrun: N cells ok` line. It returns a coroutine (awaited by async-magic machinery) and runs each cell in the kernel's namespace through user-level channels only (exec, `display()`, raising), so it behaves identically in every kernel.\n- `d.execute(*ids, ...)` / `m.execute()`: the captured twin, on a held `Dialog`. Runs code messages on an execnb `CaptureShell` (fresh and dropped, unless you pass `shell=`), assigns each result to `m.output` in memory (`d.save()` persists), and returns the messages run as a `RunResult` whose repr is a status report: ok lines collapse into a counted marker, failures always show with the exception. It blocks until done, so an async host should wrap it in `asyncio.to_thread`. `above`/`below` anchor at the ids you pass, and bare `d.execute()` runs all participating code messages (the `eval` cascade).\n- Text edits: `msg_str_replace`, `msg_strs_replace`, `msg_insert_line`, `msg_replace_lines`, `msg_del_lines`, `msg_ast_replace` (all with `re_filter`/line-range powers; `out=True` edits a prompt's reply or a code message's outputs literal), with the same names as `Message` methods for in-memory editing; `lnhashview_msg`/`msg_exhash` (and `m.lnhashview()`/`m.exhash()`) for hash-verified line edits (`lnhashview_msg` is `view_msg(..., lnhashs=True)`; only the exhash pair needs the `exhash` package).\n\n## Idiomatic usage\n\nStart by registering the notebook: `set_dlg(path)` makes every function here default to it (and the `%nbrun` magic follows suit), so calls read as \"do this to message X\". Then orient before acting:\n\n- `summary_dlg()` first for anything sizable: a cheap one-line-per-message map. `view_dlg()` when you need the full story in order with ids. Read a notebook in full before describing or changing what it does - the interleaved prose, examples, and stored outputs (`incl_out=True`) are the design rationale.\n- Read before trying things out: an ad-hoc \"what happens if...\" check usually re-derives, more slowly and less reliably, what an existing example cell already shows. If after reading you still need to try it, that's a gap in the notebook - add it as a proper example cell so the next reader doesn't repeat it.\n- `find_msgs` is the targeted view: keep the default `context=1` (the neighbouring markdown usually explains the match), and `ids=` with context is the positional query (\"does A precede B?\", \"what's the idiom around here?\"). Name questions are structural, not textual: `symdef_finder`/`symref_finder`/`ast_finder` beat regexes over binding syntax. Where available, rgapi's `nbrg` searches cell sources across files and returns the cell ids these functions take.\n- Edit at the right level: `add_msg`/`%%add_msg` for new messages; within a message, prefer hash-verified addressing (`lnhashview_msg`/`msg_exhash`, or exhash's `lnhashview_cell`/`cell_exhash` by path and cell id) - view with the lnhash variant the moment an edit is plausible, so the view doubles as the edit's address book. The plain `msg_*` editors fit where exhash can't express the edit, e.g. one `msg_str_replace` across an id list. Never splice via `read_nb`/`write_nb` internals: if a primitive is missing here, stop and propose adding it.\n- `dlg.validate()` catches structural problems early; `dlg.save()` writes back. Don't wrap calls in `hasattr` or `try/except` - call directly and read the bare result.\n\nDocs: https://AnswerDotAI.github.io/aidialog/dlgskill.html.md\n\"\"\"\n\n## types:\n- class FoundMsgs(Found,Msgs,L,\u2026): ... # Find results: live messages indexed by id (exact or unique prefix), never by position; context rows show `-` in place of their first `:`\n\n## functions:\n- def msg_insert_line(id, insert_line: int, new_str: str, *, dlg=None, out: bool = False): ... # Insert new_str at specified line number\u2026\n- def msg_str_replace(id, old_str: str, new_str: str, *, dlg=None, out: bool = False, **replace_params): ... # Replace occurrence(s) of old_str with new_str\u2026\n- def msg_strs_replace(id, old_strs: list[str], new_strs: list[str], *, dlg=None, out: bool = False, **replace_params): ... # Replace multiple strings simultaneously\u2026\n- def msg_replace_lines(id, start_line: int = None, end_line: int = None, new_content: str = '', *, dlg=None, out: bool = False): ... # Replace line range with new content; the defaults replace the entire contents\u2026\n- def msg_del_lines(id, start_line: int, end_line: int, re_filter: str = None, invert_filter: bool = False, *, dlg=None, out: bool = False): ... # Delete line range; deletion is destructive, so both line numbers must be given explicitly (`1, -1` for all lines)\u2026\n- def msg_ast_replace(id, repls: list, *, dlg=None, out: bool = False): ... # Apply ast-grep structural pattern replacements to python source\u2026\n- def set_dlg(fname, cls=None): ... # Set the current dialog file, used by these functions when `dlg` is None\u2026\n- def cur_dlg(): ... # A fresh `Dialog` read from the current dialog file (None if unset): the session-side entry to the ambient default [9e42]\n- def set_cur_msg(id): ... # Set the current message, used by `add_msg` when no placement is given\u2026\n- def cur_msg(): ... # The current message, read fresh from the current dialog file (None if unset, or no longer there) [18ee]\n- def summary_dlg(dlg=None, maxlen: int = 180): ... # One `preview` per message of the dialog file\u2026\n- def view_dlg(dlg=None, incl_out: bool = False, only_errors: bool = False, trunc_out: bool = True): ... # The dialog file as concise XML; meta-exported messages carry a bare `export` attr\u2026\n- def view_msg(id, dlg=None, nums: bool = True, start_line: int = 1, end_line: int = None, lnhashs: bool = False, incl_out: bool = False, trunc_out: bool = True): ... # Show a message's content with optional line numbers or exhash addresses\u2026\n- def view_msgs(*ids, dlg=None, nums: bool = True, start_line: int = 1, end_line: int = None, lnhashs: bool = False, incl_out: bool = False, trunc_out: bool = True): ... # Show several messages, each preceded by a `# msg ` header\u2026\n- def find_msgs(re_pattern: str = '', dlg=None, *, msg_type: str = None, only_err: bool = False, only_exp: bool = False, ids='', before: int = 0, after: int = 0, context: int = None, limit: int = None, use_case: bool = False, use_regex: bool = True, headers_only: bool = False, header_section: str = None, pred: callable = None) -> MsgRows: ... # Find messages in the dialog file matching all the given criteria; for live results, call `Dialog.find_msgs`\u2026\n- def move_msgs(ids, before=None, after=None, dlg=None): ... # Move messages in the dialog file, keeping their relative order; returns them\u2026\n- def split_msg(id, *linenos: int, dlg=None): ... # Split a message in the dialog file (see `Message.split`)\u2026\n- def merge_msgs(*ids, dlg=None): ... # Merge messages in the dialog file (see `Dialog.merge_msgs`)\u2026\n- def copy_msgs(*ids, dlg=None): ... # Copy messages into the paste buffer (replacing its contents), for later `paste_msgs`\u2026\n- def cut_msgs(*ids, dlg=None): ... # Copy messages into the paste buffer, then remove them from the dialog file\u2026\n- def paste_msgs(before=None, after=None, dlg=None): ... # Insert copies of the buffered messages (fresh ids) before/after a message or id in the dialog file; returns the new messages\u2026\n- def symdef_finder(name): ... # `find_msgs` predicate: does the message bind `name` at cell top level? (requires `remold`) [a533]\n- def symref_finder(name): ... # `find_msgs` predicate: does the message reference `name`? (requires `remold`) [3810]\n- def ast_finder(pattern): ... # `find_msgs` predicate: does ast-grep `pattern` match in the message? (requires `remold`) [3b44]\n- def lnhashview_msg(id, dlg=None): ... # Hash-addressed view of a message's content, for `msg_exhash`\u2026\n- def msg_exhash(id, *cmds: tuple, sw: int = 4, dlg=None): ... # Apply exhash commands to a message in the dialog file, returning the diff\u2026\n- def add_msg(source: str, msg_type: str = 'code', before: str = None, after: str = None, meta=None, export=False, dlg=None): ... # Add a new message before or after an existing one (default: after the current message, else at the end), returning it\u2026\n- def del_msgs(*ids, dlg=None): ... # Delete messages by id, returning the removed messages\u2026\n- def create_dlg(fname: str, source: str = '', msg_type: str = 'code'): ... # Create a new dialog file containing one message, returning the `Dialog` (with `path_` stamped)\u2026\n- def update_msg(id, dlg=None, *, mergemeta: dict = None, export: bool = None): ... # Update a message's type, meta, or export directive in the dialog file (see `Message.update`)\u2026\n- def add_msg_magic(line, cell): ... # Add a new message with the magic body as its source, taken verbatim; a bare `export` token sets the meta `nbdev` export flag. [b069]\n- def nbrun_magic(line): ... # Run code cells from the current notebook (`set_dlg`) by id prefix; bare tokens are flags or id prefixes, `fname=` overrides, a bare line runs all [a932]\n- def load_ipython_extension(ipython) [52f8]\n\n## shared params:\n- **replace_params (from `msg_str_replace`):\n start_line: int = None, # Optional 1-based start line to limit search\n end_line: int = None, # Optional 1-based end line to limit search\n n_matches: int = None, # Max replacements (None=all)\n re_filter: str = None, # If provided, only process lines matching this regex (like g// in ex)\n invert_filter: bool = False, # Invert the filter (like g!// in ex)\n use_regex: bool = False, # Treat old_str as a regex, and new_str as an `re.sub` template?\n\n# module exhash.skill:\n\n\"\"\"Universal hash-verified text editing for local files. Use this for one reliable way to read, preview, and change text files.\n\nExhash's purpose is to make edits precise and easy to check. First view a file as `lineno|hash|text` (line numbers may be space-padded for alignment); then issue ex-style commands against those exact addresses. Every addressed line's hash is checked immediately before the command runs, so stale context or wrong targets fail instead of editing nearby text. Within one call, a single-line address may match the line's current content or its content at call start, so commands can stack on one line; across calls, re-view. Structural edits still shift lines as they apply, so work *backwards* (bottom-to-top).\n\nPrefer exhash over ad hoc patching for text file modifications, and prefer reading with `lnhashview_file` over plain file reads whenever an edit may follow: the view doubles as the address book, so the edit needs no second read.\n\nCore APIs:\n- `lnhashview_file` lists hashed lines.\n- `exhash` is the in-memory command engine; this docstring is the complete command reference, and `doc(exhash)` adds engine details (strict `s` matching, EditResult fields).\n- `file_exhash` is the file-aware engine; unqualified addresses use `path` and file-qualified addresses can edit or transfer across files.\n- `lnhashview_cell` views one notebook cell's source in an `.ipynb` file; `lnhashview_cells` views several explicit cells with `# cell ` headers. `cell_exhash` edits one cell.\n\nWorkflow:\n1. `lnhashview_file(...)`, ending the cell with the bare call: the result displays verbatim, one `lineno|hash|content` line each, so never join, print, or reformat it.\n2. Copy exact displayed `lineno|hash|` addresses.\n3. Use tuple command specs; pass each command as its own positional argument, e.g. `file_exhash(path, (addr1, \"d\"), (addr2, \"s\", pat, repl))`. Use raw triple-quoted Python strings for address, pattern, replacement, and inserted text when composing commands.\n4. Use `file_exhash(path, *cmds)` (or `cell_exhash(path, cell_id, *cmds)` for one notebook cell) to apply the edit: both write to disk and return a diff by default. Pass `inplace=False` to preview the result object without touching the file.\n\nOnce you hold lnhash addresses, `p` commands are the scattered view: one `(addr, \"p\")` per address, straight from an `rg` hit or a summary, gives back exactly those lines as verified rows. A `p`-only call writes nothing and returns a bare `lnhashview` of the printed lines (no diff headers, no tag column, no truncation), so its output is an address book that feeds the next edit directly, e.g. `file_exhash(path, (\"9|e08c|\", \"p\"), (\"18|2f61|\", \"p\"), (\"28|3cc8|\", \"p\"))`. `(\"%\", \"p\")` views a whole file this way, and `(addr, \"g\", pat, (\"p\",))` is scoped grep: a verified row per matching line in the range. Where a call also edits, its printed lines appear in the diff as context rows, always shown even when no hunk is near them.\n\nAddressing:\n Address strings use lnhash addresses: lineno|hash| where hash is a 4-char\n hex content hash. Use lnhashview to get addresses:\n lnhashview file.txt show all lines with addresses\n lnhashview file.txt 10 20 show lines 10-20\n With multiple commands, hashes are checked immediately before each command runs; a single-line address may also match that line's call-start hash.\n\n Single: 12|a3f2|\n Range: 12|a3f2|,15|b1c3|\n Last: $ (last line)\n Whole: % (whole file or cell, same as 1,$; no hashes needed)\n Special: 0|0000| targets before line 1 (only with a or i)\n\nTuple commands:\n (addr, \"s\", pat, repl[, flags]) Substitute (Rust regex syntax: backrefs in repl are $1/$0/${name}; a two-char \\1 stays literal; a literal $ is written $$, and $name followed by more text needs ${name}text -- an unknown group reference is an error). Flags: g=all, i=case-insensitive. pat/repl are verbatim: literal newlines, slashes, and backslashes all work. For $-heavy replacement text, prefer a c command: its text has no template layer at all.\n (addr, \"d\") Delete line(s)\n (addr, \"a\", text) Append text after line\n (addr, \"i\", text) Insert text before line\n (addr, \"c\", text) Change/replace with text\n (addr, \"j\") Join with next line; with range, joins all lines in range\n (addr, \"m\", dest) Move line(s) after dest address\n (addr, \"t\", dest) Copy line(s) after dest address\n (addr, \">\", n) Indent n levels (default 1, 4 spaces each)\n (addr, \"<\", n) Dedent n levels (default 1)\n (addr, \"sort\") Sort lines alphabetically\n (addr, \"p\") View line(s): emit them as verified `lineno|hash|` rows, changing nothing\n (addr, \"g\", pat, sub), (addr, \"g!\", pat, sub), (addr, \"v\", pat, sub) Global commands: apply `sub` to each addressed line matching `pat` (`g!`/`v`: not matching). `sub` is a subcommand tuple without an address, e.g. (\"d\",) or (\"s\", \"foo\", \"bar\", \"g\"); globals cannot nest.\n (addr, \"y\", source, dest) Transliterate `source` chars to `dest` (equal counts required)\n\n\nCut/copy/paste between files and notebook cells:\nAny `m` (cut+paste) or `t` (copy+paste) address can carry a target prefix: `path:` for another file, or `path.ipynb:cellid:` for one cell's source (`cellid` exact or unique prefix). This is THE way to transfer existing lines between locations: the lines never pass through your output, so hard-to-retype content (long encoded strings, hashes, long literals) cannot be mistyped. Take source addresses from `lnhashview_file`/`lnhashview_cell` of each target as usual:\n\n file_exhash(path, (\"src/a.py:10|aaaa|,20|bbbb|\", \"m\", \"src/b.py:$\")) # cut a.py lines 10-20, paste at end of b.py\n file_exhash(path, (\"nb.ipynb:ab12cd34:6|830e|\", \"t\", \"other.ipynb:9f8e:$\")) # copy one cell line into another notebook's cell\n file_exhash(path, (\"nb.ipynb:ab12cd34:%\", \"t\", \"snippets.py:0|0000|\")) # copy a whole cell's source into a new file\n\nA range must stay within one file or cell, and cells are never created by a transfer (files can be, via a `0|0000|` destination). For whole-cell structural operations (add/delete/move cells) use `pyskills.ipynb`'s `copy_cells`/`paste_cells` instead; this idiom is for line-level transfer.\n\nReformatting a section like `gq`, plus optional indents: `j` the range onto one line, re-view, then split with ONE g-flagged `s` alternating the tokens (picked by eye) that should start each new line, captured and restored with the break and indent in the replacement: `(addr, \"s\", r\", ('foo'|'bar'|'baz')\", \",\\n $1\", \"g\")`\nImportant:\nDo not pass raw commands to Python APIs. Do not create addresses by text search or remembered line numbers, and never construct them by computing hashes (e.g. via `line_hash`): addresses come only from a fresh view immediately before the edit. On stale hash, re-view and rebuild. Where rgapi is installed, hits from `rg(pattern, lnhashs=True)` count as fresh views too: their addresses drop straight into commands (and `nbrg` finds the cell ids that `lnhashview_cell` takes). If reaching an address seems to need arithmetic, hand-copying, or a guessed hash, a step on that route was skipped. Tuple text fields can contain newlines wherever the command accepts text. For example, `(addr, \"s\", \"foo\", \"bar\\nbaz\")` replaces one line with two. Text fields are taken verbatim: a two-character `\\n` sequence stays literal; use an actual newline when you want a line break. For `a`/`i`/`c`, put all text in one tuple field: `\"first\\nsecond\"` starts with `first`, while `\"\\nfirst\"` inserts a leading blank line before `first`. For moving/copying between files or cells, use the qualified `m`/`t` addresses shown above. Missing files can only be created through `(r\"0|0000|\", \"a\", text)` or `(r\"0|0000|\", \"i\", text)` creation semantics.\n\nThe `%%exhash` cell magic:\nIn IPython sessions, importing this module registers the `%%exhash` cell magic: `%%exhash []
` applies one command whose text is everything below the magic line, taken verbatim (one trailing newline stripped). Passing `` targets that cell in an .ipynb file instead of a plain file (`cell_exhash`); the magic dispatches on token count, so no separate cell magic exists. Because the text is never parsed as Python, no quoting or escaping applies. Use it for EVERY `a`/`i`/`c` command, however simple the text looks: create a file with `%%exhash path 0|0000| a`; replace a whole cell or file with `%%exhash [] % c` (`%` needs no hashes: a full replace has no neighboring lines to mis-hit); replace a region within one with a range address and `c` (`%%exhash 12|a3f2|,15|b1c3| c`), both addresses straight from the one pre-edit view. Tuple `a`/`i`/`c` text fields are only for contexts without magics (scripts, tests): interactively they add a Python quoting layer whose failure modes are not reliably foreseeable, so do not use them.\nIPython expands `{expr}` and `$var` in the magic line from the user namespace (its standard `var_expand` for all magics), so a path or cell id held in a variable needs no retyping: `%%exhash {path} {cid} % c`. Only the line expands; the text stays verbatim.\n\nDocument outlines (`open_doc`):\nThe outline layer makes exhash a hierarchical editor: `open_doc` opens a file (`fname=`, or a `Path` as `src`; recorded for `refresh()` and edits), a URL (an `https?://` str, fetched), or any other str as text in hand, and returns a `Section` tree. Markdown sections come from headings; code files (py, js, ts, tsx, rs, zig, swift) from tree-sitter definitions; `.ipynb` files from md-heading cells over cells. Display the root bare: fixed-width rows `token title (count) [size] preview` (code rows have no title: the def line, signature included, opens the preview), previews \u00b6-joined with links rendered `[text][n]` so no URL spends tokens. The token is the verified address: `1.2.|12|a3f2|,45|b1c3|` fuses the dotted addr (trailing dot; the root's is `.`) with the section's boundary lnhash pair, so one row serves navigation, editing, and orientation: `at()` verifies the first hash, and the pair drops straight into a `file_exhash` range command. `at()` accepts ONLY tokens copied from a listing (`'1.6.|12|a3f2|'` or the full range form); bare dotted addrs raise, teaching the idiom. Live traversal is free: `d[1][6]`, `find(title)`, `search(pat)` (counts and matching-line previews), `paths(depth)`, `links(pat)`, and `open(n)` opens link `n` as a new tree with `base` recorded (a non-md target comes back as a leaf whose `.src` holds its text). `view()` is the reading view: rendered text with links `[text][n]` (`.src` is the raw source), displayed verbatim bare; `view(*tokens)` reads several sections in one call, each under a `# token` header when more than one, so a listing's tokens replace a print loop; `nums=`/`lnhashs=` switch any view to stored lines with edit-ready addresses. Notebook section tokens carry the heading cell id (`1.2.|ab12cd34|8f3a|`), and `view(lnhashs=True)` rows are `cellid:lineno|hash|`, ready for `cell_exhash`. The whole llms.txt docs workflow: `toc = open_doc(url)`; `toc.links(topic)`; `page = toc.open(n)`; display `page` bare; `page.view()` whole when small, else `page.search(topic)` and `page.view(*tokens)`.\n\"\"\"\n\n## types:\n- class Section(dict): ... # One document section: numbered child sections, source span, and a verified address\n- class Sections(list): ... # Sections listed as fixed-width `token title (count) [size] preview` rows; each row is the live node. Code rows have no title: the def line opens the preview\n- class Link: ... # One inline link, numbered in document reading order\n- class Links(list): ... # A list of `Link` rows, displayed one per line\n\n## functions:\n- def line_hash(line: str) -> str: ... # Return a 4-char lowercase hex hash for a single line of text. [3ea3]\n- def lnhash(lineno: int, line: str) -> str: ... # Return an lnhash address ``lineno|hash|`` for ``line`` at 1-based ``lineno``. [f9d5]\n- def lnhashview(text: str, start: int = None, end: int = None) -> LnhashView: ... # Return lines formatted as space-padded ``lineno|hash|content``. Optional 1-based ``start``/``end`` filter the range; ``end`` past EOF is clamped. [eb78]\n- def lnhashview_file(path: str, start: int = None, end: int = None) -> LnhashView: ... # Return lines formatted as space-padded ``lineno|hash|content`` for file at ``path`` (expands ``~``). Optional 1-based ``start``/``end`` filter the range; ``end`` past EOF is clamped. [52c2]\n- def lnhashview_cell(path: str, cell_id: str, start: int = None, end: int = None) -> LnhashView: ... # Return lines formatted as ``lineno|hash|content`` for the source of notebook cell ``cell_id`` in ipynb file at ``path`` (expands ``~``). ``cell_id`` may be an exact id or unique prefix; optional 1-based ``start``/``end`` filter the range. [08f8]\n- def lnhashview_cells(path: str, *cell_ids: str, start: int = None, end: int = None) -> LnhashView: ... # Return grouped lnhash views for explicit notebook cell ids in the ipynb file at ``path`` (expands ``~``). Each group starts with ``# cell ``; following lines keep normal ``lineno|hash|content`` format. [b6bd]\n- def exhash(text: str, cmds: list[tuple], sw: int = 4): ... # Verified line-addressed editor. Apply commands to `text`, return an EditResult.\u2026\n- def file_exhash(path: str, *cmds: tuple, sw: int = 4, inplace: bool = True): ... # Read files and notebook cells, apply file-aware exhash commands, and return per-target results or a combined diff.\u2026\n- def cell_exhash(path: str, cell_id: str, *cmds: tuple, sw: int = 4, inplace: bool = True): ... # Apply exhash commands to the source of notebook cell ``cell_id`` in ipynb file at ``path`` (expands ``~``).\u2026\n- def open_doc(src: str | Path = None, rm_fenced=True, fname: str = None): ... # Open a document as a `Section` tree: a file (`fname`, or a `Path` in `src`; recorded for `refresh` and edits), a URL (fetched), or text\u2026\n\n# module rgapi.skill:\n\n\"\"\"Fast and flexible file finding and search for Python. Use this when code needs `fd`-style file finding or `rg`-style searching.\n\nrgapi wraps the same `ignore`, `grep-regex`, and `grep-searcher` crates ripgrep uses, so `.gitignore`/`.ignore`/`.rgignore`, dot-file handling, glob/ext filters, and regex matching all behave like `rg`. Walking and searching run in parallel and most work stays in Rust, so results come back as structured Python objects instead of CLI text to parse. Prefer rgapi over shelling out to `rg`/`fd` or scanning files by hand: you get typed rows, byte-offset match spans, and lazy iteration.\n\nCore APIs:\n- `fd(root=\".\", ...)` finds paths with fd-style filters (`pattern` smart-case basename regex, `include`/`exclude`/`glob`, `ext`); returns slash-separated relative paths as `FileEntry` rows: `str` subclasses that lazily stat themselves for `size`/`mtime`/`is_dir`/`stat`. The list displays as an `ls -l`-style table (capped at `rgapi.MAX_REPR` rows); `str(res)` or `list(res)` give plain paths. Unfollowed symlinks are included, displaying with an `l` mode character; `show_target=True` appends `-> target` to their rows, and `link_target` holds it (`None` for non-links).\n- `ls(root=\".\", ...)` lists like the shell `ls`: one level, directories included, ignore rules off, sorted by name. It is `fd` with different defaults, so every `fd` filter works; `hidden=True` is `ls -a`.\n- `rg(pattern, root=\".\", ...)` returns matching `SearchLine` rows. `summary=True` instead returns blank-line-delimited `SearchBlock` rows, with newline runs displayed as `\u00b6` and `maxlen` source characters shown per block. Context is line-based normally and block-based in summary mode. `paths=True` returns unique paths, `count=True` returns a match-span total, and `lnhashs=True` shows exhash addresses. `summary=True` is incompatible with `paths` and `count`, but combines with `lnhashs` to show copyable block boundaries.\n- `nbrg(pattern, root=\".\", cell_context=0, maxlen=180, ...)` searches Jupyter `.ipynb` files (cell source only) and returns matched cells as `NbResults`/`NbCell`; `paths=True` and `count=True` reduce like `rg`'s. Its display is always a one-line cell summary. Use this for notebooks rather than `rg`, to avoid escaped JSON and get stable cell ids. `multiline=True` lets a pattern match across lines within a cell (line-oriented `rg` rejects `\n` in patterns); `^`/`$` still match at line boundaries.\n- `rgstr(pattern, text, ...)` searches a string already in hand (a fetched log, a captured output, a doc): `rg`-style `SearchLine` rows and context from in-memory text, with no path label and no separate `compile` step. Reach for it instead of ad hoc `splitlines()` loops over held text.\n\nIdiomatic usage:\n- Results display through tuned reprs designed to be read as-is: end the cell with the bare call and read what comes back - never join, slice, or otherwise reformat a result by hand. If a result is too big to take in full, narrow it with the function's own parameters (`max_results`, `paths=True`, `count=True`, tighter filters), not by post-processing the output.\n- For orientation, start with `rg(pattern, summary=True)` and drop to line-level rows only where needed; add `lnhashs=True` when an edit may follow, so hits arrive with copyable addresses. This applies beyond unfamiliar code: on prose, config, and markdown, a summary row is the whole matched paragraph, usually exactly the read you wanted.\n- In kernel sessions, reach for `ls()` rather than shell `ls`, and `fd()` rather than shell `find`, for listings you or later calls will read.\n\nSearchLine rows:\n kind 'match', 'before', 'after', or 'context'\n path path relative to root\n line_number 1-based line number\n lnhash exhash-style `lineno|hash|` address\n line line text without the trailing newline\n matches list of (start, end) byte offsets, for 'match' rows\n asdict() returns the row fields as a plain dict\n\nSearchBlock rows (from `rg(summary=True)`):\n path/block_index/start_line/end_line/start_lnhash/end_lnhash locate the block\n kind 'match' or 'context'\n source full block source\n matches list of matching SearchLine rows within the block\n asdict() returns the row fields as a plain dict\nOutput uses `path:start-end:source` for matches and `path:start-end-source` for context. With `lnhashs=True`, `start-end` becomes `start_lnhash,end_lnhash` (or one hash for a one-line block). Empty or whitespace-only lines delimit blocks; `context=N` adds N neighbouring blocks. Multiple matches in one block produce one row.\n\nNbCell rows (from `nbrg`):\n path/cell_index/cell_id/cell_type locate the cell ('code'/'markdown'/'raw')\n kind 'match' or 'context'\n source full cell source\n matches list of SearchLine rows for the matched lines within the cell\n asdict() returns the cell fields as a plain dict\nOutput is keyed by `cell_id` (the nbformat cell/message id), not line number: `path:cell_id:source` for matches and `path:cell_id-source` for context. A match row starts at its first matched line: earlier lines display as `\u2026[Ln]` (`n` the matched line's 1-based number in the cell), except that a leading `#|` directive line is kept, e.g. `#| export\u2026[L4]needle here`. Newline runs display as `\u00b6` (following indentation kept) and `maxlen` limits displayed source without changing `source`. `cell_context=N` adds N neighbouring cells. Walking, parsing, and matching run in parallel in Rust; outputs and metadata are skipped.\n\nImportant:\nTraversal is parallel and result order is NOT guaranteed; wrap in `sorted(...)` if you need stable order. `path_re`/`skip_path_re` filter the returned/searched paths but do not prune traversal; use `skip_dir`/`skip_dir_re` to prune whole subtrees for speed. Run `doc(func)` for full parameter docments.\n\"\"\"\n\n## types:\n- class RgIter: ... # Initialize self. See help(type(self)) for accurate signature.\n\n## functions:\n- def fd(root: str | Path = '.', pattern: str | NoneType = None, files: bool = True, dirs: bool = False, show_target: bool = False, timeout_ms: int | NoneType = None, *, ext: str | list | NoneType = None, ignore: bool = True, max_depth: int | NoneType = None, **walk_params) -> PathResults: ... # Find paths with fd-style filters and gitignore support.\u2026\n- def ls(root: str | Path = '.', pattern: str | NoneType = None, dirs: bool = True, max_depth: int | NoneType = 1, ignore: bool = False, *, files: bool = True, show_target: bool = False, timeout_ms: int | NoneType = None, ext: str | list | NoneType = None, **walk_params) -> PathResults: ... # List a directory like `ls`: one level, directories included, ignore rules off, sorted by name.\u2026\n- def rg(pattern: str, root: str | Path = '.', case_sensitive: bool | NoneType = None, smart_case: bool = False, before_context: int = 0, after_context: int = 0, context: int = 0, paths: bool = False, count: bool = False, max_results: int | NoneType = None, lnhashs: bool = False, timeout_ms: int | NoneType = None, summary: bool = False, maxlen: int = 180, *, ext: str | list | NoneType = None, ignore: bool = True, max_depth: int | NoneType = None, **walk_params): ... # Search files and return `SearchResults`, matched paths, or a count; `lnhashs=True` shows exhash-style addresses.\u2026\n- def rg_iter(pattern: str, root: str | Path = '.', case_sensitive: bool | NoneType = None, smart_case: bool = False, before_context: int = 0, after_context: int = 0, context: int = 0, lnhashs: bool = False, *, ext: str | list | NoneType = None, ignore: bool = True, max_depth: int | NoneType = None, **walk_params) -> RgIter: ... # Search files lazily, yielding `SearchLine` rows; `lnhashs=True` shows exhash-style addresses.\u2026\n- def nbrg(pattern: str, root: str | Path = '.', cell_context: int = 0, case_sensitive: bool | NoneType = None, smart_case: bool = False, paths: bool = False, multiline: bool = False, max_results: int | NoneType = None, count: bool = False, timeout_ms: int | NoneType = None, maxlen: int = 180, *, ignore: bool = True, max_depth: int | NoneType = None, **walk_params): ... # Search `.ipynb` cell sources under `root` in parallel, returning matched cells, paths, or a count.\u2026\n- def rgstr(pattern: str, text: str, case_sensitive: bool | NoneType = None, smart_case: bool = False, *, before_context: int = 0, after_context: int = 0, context: int = 0) -> SearchResults: ... # Search text already in hand, like `rg` on a string: no path label, no separate `compile`.\u2026\n\n## shared params:\n- **walk_params (from `fd`):\n glob: str | list | NoneType = None, # Include glob or globs; alias for `include`\n include: str | list | NoneType = None, # Include glob or globs, e.g. `*.py`\n exclude: str | list | NoneType = None, # Exclude glob or globs, e.g. `test_*.py`\n hidden: bool = False, # Include hidden files and directories\n min_depth: int | NoneType = None, # Minimum depth for returned/searched paths\n max_filesize: int | NoneType = None, # Skip files larger than this many bytes\n follow_links: bool = False, # Follow symbolic links while walking\n same_file_system: bool = False, # Do not cross filesystem boundaries\n path_re: str | NoneType = None, # Regex that relative paths must match\n skip_path_re: str | NoneType = None, # Regex for relative paths to skip\n skip_dir: str | list | NoneType = None, # Directory glob or globs to prune\n skip_dir_re: str | NoneType = None, # Directory regex used to prune traversal"}]}, "toolUseResult": [{"type": "text", "text": "# module aidialog.dlgskill:\n\n\"\"\"Read, search, and edit dialogs and notebooks through aidialog's `Dialog` and `Message` classes\n\nUse this whenever the question or edit concerns a notebook's content: messages, sources, outputs, prompts and their replies.\n\n## The hierarchy\n\nNotebook work happens at three levels, and picking the right level is most of using this module well:\n\n- Content (this module): what the messages say and how they change. `summary_dlg`, `find_msgs`, `view_dlg`, the message editing operations.\n- Representation (`fastcore.nbio`): which keys exist, whether a file is schema-valid, whether bytes changed. Start with `validate_nb`/`validate_cell`, which name the cell at fault; use `read_nb` directly when the question is about the dict itself. For *plain* notebooks (no dialog semantics), nbio's `Notebook`/`NbCell` objects and `cell_*` functions are the content surface, and this module's word choice marks the layer: cells for notebooks, messages for dialogs.\n- Raw text: only when the file will not parse at all.\n\n## Core APIs\n\nThe function/method two-shapes contract is `fastcore.editskill`'s, learned once: the function is a transaction addressed by `dlg=` (an ipynb path, or None meaning the current dialog file: `set_dlg`); the method is a session on a held `Dialog` or `Message`, saved by an explicit `dlg.save()` (`msg_str_replace(id, ..., dlg=p)` \u27f7 `m.str_replace(...)`). Wrapping any message list in an ephemeral `Dialog(msgs)` makes the whole session surface available on it.\n\n- `summary_dlg(dlg)` / `d.summary()`: one `preview` per message, `id:t[directives]:content` (t: c=code n=note p=prompt r=raw; the bracket, as in nbio's `CellRow`, shows meta-form nbdev directives such as `[export]`), with a prompt's reply on a following `> ` line; a line cut short ends in a humanized `[size]`.\n- `find_msgs(pattern, dlg, ...)`: search by regex, type, errors, heading, ids, or a `pred` (`symdef_finder`/`symref_finder`/`ast_finder` build structural ones); `context` defaults to 1 even with `ids=`, because the neighbouring message usually explains the match -- retrieve and read it. Returns `MsgRow` snapshots (`id`, `msg_type`, `content`, `out`, `meta`) in a `MsgRows`; `d.find_msgs(...)` returns live `Message`s in a `FoundMsgs`. Both index by message id (exact or unique prefix) and refuse integer positions: a find result includes context rows, so `[0]` may be a neighbour of the match, and the raised error teaches the id idiom. Previews mark context rows with `-` in place of the first `:`. Every live message carries a `dlg` backref to its owning `Dialog`, so dialog-level operations are always in reach from a message in hand (e.g. `m.dlg.save()` after mutating `m.output` directly).\n- `view_dlg(dlg)` / `d.view()` / `view_msg(id)` / `m.view()` / `view_msgs(*ids)` / `msg2xml(m)` / `m.to_xml()`: full views in the shared `item2xml` grammar (a prompt's reply is its `` section; meta `nbdev` directives render as attrs, so a meta-exported message carries a bare `export`); `incl_out=True` on the line views appends the message's output the same way.\n- Structure: `add_msg`, `del_msgs`, `move_msgs`, `split_msg`, `merge_msgs`, `copy_msgs`/`cut_msgs`/`paste_msgs`, `create_dlg`, with session twins `d.move_msgs`, `m.split`, `d.merge_msgs`, `d.copy_msgs`/`d.cut_msgs`/`d.paste_msgs` (session adds go through `d.mk_message`, deletes through `d.remove_msgs`). `add_msg` with neither `before=` nor `after=` places after the current message (`set_cur_msg`, which a host sets and no write ever moves), or at the end when that is unset or gone. `add_msg` and `d.mk_message` take `meta=` and an `export=` shortcut for the meta `nbdev` export flag, readable and assignable as `m.meta_exported` (`m.exported` reads content and meta together, and is what `find_msgs(only_exp=True)` filters on). The `%%add_msg` magic takes its body verbatim: its line is `%%add_msg [dlg] [msg_type] [export] [before=|after=]`, where a bare path token is the dlg and a bare type name the msg_type, and keyword spellings win over bare tokens.\n- `update_msg(id, ..., dlg=)` / `m.update(...)`: one transaction for a message's attributes. Plain keywords assign (`content=`, `msg_type=`, `output=`, `meta=` replacing wholesale); `mergemeta` deep-merges into `meta` (a `None` value deletes its key); `export` sets the nbdev directive -- `True` adds, `False` removes (there is no negative form), and either migrates a content-form `#| export` line to meta. The function returns a diff of the message's XML plus a `meta:` line, so every change it can make is visible.\n- The `%nbrun` line magic runs code cells from the current notebook in the running kernel, by cell id prefix, same grammar: bare tokens are flags (`above`, `below`, `all`, `exported`, `ignore_eval`, `continue_on_error`, `show`) or id prefixes, and `fname=` overrides the notebook for one call. Only cells named by id display their output: bulk-selected cells run silently (`show` overrides), errors always surface naming the failed cell, and the run ends with a `nbrun: N cells ok` line. It returns a coroutine (awaited by async-magic machinery) and runs each cell in the kernel's namespace through user-level channels only (exec, `display()`, raising), so it behaves identically in every kernel.\n- `d.execute(*ids, ...)` / `m.execute()`: the captured twin, on a held `Dialog`. Runs code messages on an execnb `CaptureShell` (fresh and dropped, unless you pass `shell=`), assigns each result to `m.output` in memory (`d.save()` persists), and returns the messages run as a `RunResult` whose repr is a status report: ok lines collapse into a counted marker, failures always show with the exception. It blocks until done, so an async host should wrap it in `asyncio.to_thread`. `above`/`below` anchor at the ids you pass, and bare `d.execute()` runs all participating code messages (the `eval` cascade).\n- Text edits: `msg_str_replace`, `msg_strs_replace`, `msg_insert_line`, `msg_replace_lines`, `msg_del_lines`, `msg_ast_replace` (all with `re_filter`/line-range powers; `out=True` edits a prompt's reply or a code message's outputs literal), with the same names as `Message` methods for in-memory editing; `lnhashview_msg`/`msg_exhash` (and `m.lnhashview()`/`m.exhash()`) for hash-verified line edits (`lnhashview_msg` is `view_msg(..., lnhashs=True)`; only the exhash pair needs the `exhash` package).\n\n## Idiomatic usage\n\nStart by registering the notebook: `set_dlg(path)` makes every function here default to it (and the `%nbrun` magic follows suit), so calls read as \"do this to message X\". Then orient before acting:\n\n- `summary_dlg()` first for anything sizable: a cheap one-line-per-message map. `view_dlg()` when you need the full story in order with ids. Read a notebook in full before describing or changing what it does - the interleaved prose, examples, and stored outputs (`incl_out=True`) are the design rationale.\n- Read before trying things out: an ad-hoc \"what happens if...\" check usually re-derives, more slowly and less reliably, what an existing example cell already shows. If after reading you still need to try it, that's a gap in the notebook - add it as a proper example cell so the next reader doesn't repeat it.\n- `find_msgs` is the targeted view: keep the default `context=1` (the neighbouring markdown usually explains the match), and `ids=` with context is the positional query (\"does A precede B?\", \"what's the idiom around here?\"). Name questions are structural, not textual: `symdef_finder`/`symref_finder`/`ast_finder` beat regexes over binding syntax. Where available, rgapi's `nbrg` searches cell sources across files and returns the cell ids these functions take.\n- Edit at the right level: `add_msg`/`%%add_msg` for new messages; within a message, prefer hash-verified addressing (`lnhashview_msg`/`msg_exhash`, or exhash's `lnhashview_cell`/`cell_exhash` by path and cell id) - view with the lnhash variant the moment an edit is plausible, so the view doubles as the edit's address book. The plain `msg_*` editors fit where exhash can't express the edit, e.g. one `msg_str_replace` across an id list. Never splice via `read_nb`/`write_nb` internals: if a primitive is missing here, stop and propose adding it.\n- `dlg.validate()` catches structural problems early; `dlg.save()` writes back. Don't wrap calls in `hasattr` or `try/except` - call directly and read the bare result.\n\nDocs: https://AnswerDotAI.github.io/aidialog/dlgskill.html.md\n\"\"\"\n\n## types:\n- class FoundMsgs(Found,Msgs,L,\u2026): ... # Find results: live messages indexed by id (exact or unique prefix), never by position; context rows show `-` in place of their first `:`\n\n## functions:\n- def msg_insert_line(id, insert_line: int, new_str: str, *, dlg=None, out: bool = False): ... # Insert new_str at specified line number\u2026\n- def msg_str_replace(id, old_str: str, new_str: str, *, dlg=None, out: bool = False, **replace_params): ... # Replace occurrence(s) of old_str with new_str\u2026\n- def msg_strs_replace(id, old_strs: list[str], new_strs: list[str], *, dlg=None, out: bool = False, **replace_params): ... # Replace multiple strings simultaneously\u2026\n- def msg_replace_lines(id, start_line: int = None, end_line: int = None, new_content: str = '', *, dlg=None, out: bool = False): ... # Replace line range with new content; the defaults replace the entire contents\u2026\n- def msg_del_lines(id, start_line: int, end_line: int, re_filter: str = None, invert_filter: bool = False, *, dlg=None, out: bool = False): ... # Delete line range; deletion is destructive, so both line numbers must be given explicitly (`1, -1` for all lines)\u2026\n- def msg_ast_replace(id, repls: list, *, dlg=None, out: bool = False): ... # Apply ast-grep structural pattern replacements to python source\u2026\n- def set_dlg(fname, cls=None): ... # Set the current dialog file, used by these functions when `dlg` is None\u2026\n- def cur_dlg(): ... # A fresh `Dialog` read from the current dialog file (None if unset): the session-side entry to the ambient default [9e42]\n- def set_cur_msg(id): ... # Set the current message, used by `add_msg` when no placement is given\u2026\n- def cur_msg(): ... # The current message, read fresh from the current dialog file (None if unset, or no longer there) [18ee]\n- def summary_dlg(dlg=None, maxlen: int = 180): ... # One `preview` per message of the dialog file\u2026\n- def view_dlg(dlg=None, incl_out: bool = False, only_errors: bool = False, trunc_out: bool = True): ... # The dialog file as concise XML; meta-exported messages carry a bare `export` attr\u2026\n- def view_msg(id, dlg=None, nums: bool = True, start_line: int = 1, end_line: int = None, lnhashs: bool = False, incl_out: bool = False, trunc_out: bool = True): ... # Show a message's content with optional line numbers or exhash addresses\u2026\n- def view_msgs(*ids, dlg=None, nums: bool = True, start_line: int = 1, end_line: int = None, lnhashs: bool = False, incl_out: bool = False, trunc_out: bool = True): ... # Show several messages, each preceded by a `# msg ` header\u2026\n- def find_msgs(re_pattern: str = '', dlg=None, *, msg_type: str = None, only_err: bool = False, only_exp: bool = False, ids='', before: int = 0, after: int = 0, context: int = None, limit: int = None, use_case: bool = False, use_regex: bool = True, headers_only: bool = False, header_section: str = None, pred: callable = None) -> MsgRows: ... # Find messages in the dialog file matching all the given criteria; for live results, call `Dialog.find_msgs`\u2026\n- def move_msgs(ids, before=None, after=None, dlg=None): ... # Move messages in the dialog file, keeping their relative order; returns them\u2026\n- def split_msg(id, *linenos: int, dlg=None): ... # Split a message in the dialog file (see `Message.split`)\u2026\n- def merge_msgs(*ids, dlg=None): ... # Merge messages in the dialog file (see `Dialog.merge_msgs`)\u2026\n- def copy_msgs(*ids, dlg=None): ... # Copy messages into the paste buffer (replacing its contents), for later `paste_msgs`\u2026\n- def cut_msgs(*ids, dlg=None): ... # Copy messages into the paste buffer, then remove them from the dialog file\u2026\n- def paste_msgs(before=None, after=None, dlg=None): ... # Insert copies of the buffered messages (fresh ids) before/after a message or id in the dialog file; returns the new messages\u2026\n- def symdef_finder(name): ... # `find_msgs` predicate: does the message bind `name` at cell top level? (requires `remold`) [a533]\n- def symref_finder(name): ... # `find_msgs` predicate: does the message reference `name`? (requires `remold`) [3810]\n- def ast_finder(pattern): ... # `find_msgs` predicate: does ast-grep `pattern` match in the message? (requires `remold`) [3b44]\n- def lnhashview_msg(id, dlg=None): ... # Hash-addressed view of a message's content, for `msg_exhash`\u2026\n- def msg_exhash(id, *cmds: tuple, sw: int = 4, dlg=None): ... # Apply exhash commands to a message in the dialog file, returning the diff\u2026\n- def add_msg(source: str, msg_type: str = 'code', before: str = None, after: str = None, meta=None, export=False, dlg=None): ... # Add a new message before or after an existing one (default: after the current message, else at the end), returning it\u2026\n- def del_msgs(*ids, dlg=None): ... # Delete messages by id, returning the removed messages\u2026\n- def create_dlg(fname: str, source: str = '', msg_type: str = 'code'): ... # Create a new dialog file containing one message, returning the `Dialog` (with `path_` stamped)\u2026\n- def update_msg(id, dlg=None, *, mergemeta: dict = None, export: bool = None): ... # Update a message's type, meta, or export directive in the dialog file (see `Message.update`)\u2026\n- def add_msg_magic(line, cell): ... # Add a new message with the magic body as its source, taken verbatim; a bare `export` token sets the meta `nbdev` export flag. [b069]\n- def nbrun_magic(line): ... # Run code cells from the current notebook (`set_dlg`) by id prefix; bare tokens are flags or id prefixes, `fname=` overrides, a bare line runs all [a932]\n- def load_ipython_extension(ipython) [52f8]\n\n## shared params:\n- **replace_params (from `msg_str_replace`):\n start_line: int = None, # Optional 1-based start line to limit search\n end_line: int = None, # Optional 1-based end line to limit search\n n_matches: int = None, # Max replacements (None=all)\n re_filter: str = None, # If provided, only process lines matching this regex (like g// in ex)\n invert_filter: bool = False, # Invert the filter (like g!// in ex)\n use_regex: bool = False, # Treat old_str as a regex, and new_str as an `re.sub` template?\n\n# module exhash.skill:\n\n\"\"\"Universal hash-verified text editing for local files. Use this for one reliable way to read, preview, and change text files.\n\nExhash's purpose is to make edits precise and easy to check. First view a file as `lineno|hash|text` (line numbers may be space-padded for alignment); then issue ex-style commands against those exact addresses. Every addressed line's hash is checked immediately before the command runs, so stale context or wrong targets fail instead of editing nearby text. Within one call, a single-line address may match the line's current content or its content at call start, so commands can stack on one line; across calls, re-view. Structural edits still shift lines as they apply, so work *backwards* (bottom-to-top).\n\nPrefer exhash over ad hoc patching for text file modifications, and prefer reading with `lnhashview_file` over plain file reads whenever an edit may follow: the view doubles as the address book, so the edit needs no second read.\n\nCore APIs:\n- `lnhashview_file` lists hashed lines.\n- `exhash` is the in-memory command engine; this docstring is the complete command reference, and `doc(exhash)` adds engine details (strict `s` matching, EditResult fields).\n- `file_exhash` is the file-aware engine; unqualified addresses use `path` and file-qualified addresses can edit or transfer across files.\n- `lnhashview_cell` views one notebook cell's source in an `.ipynb` file; `lnhashview_cells` views several explicit cells with `# cell ` headers. `cell_exhash` edits one cell.\n\nWorkflow:\n1. `lnhashview_file(...)`, ending the cell with the bare call: the result displays verbatim, one `lineno|hash|content` line each, so never join, print, or reformat it.\n2. Copy exact displayed `lineno|hash|` addresses.\n3. Use tuple command specs; pass each command as its own positional argument, e.g. `file_exhash(path, (addr1, \"d\"), (addr2, \"s\", pat, repl))`. Use raw triple-quoted Python strings for address, pattern, replacement, and inserted text when composing commands.\n4. Use `file_exhash(path, *cmds)` (or `cell_exhash(path, cell_id, *cmds)` for one notebook cell) to apply the edit: both write to disk and return a diff by default. Pass `inplace=False` to preview the result object without touching the file.\n\nOnce you hold lnhash addresses, `p` commands are the scattered view: one `(addr, \"p\")` per address, straight from an `rg` hit or a summary, gives back exactly those lines as verified rows. A `p`-only call writes nothing and returns a bare `lnhashview` of the printed lines (no diff headers, no tag column, no truncation), so its output is an address book that feeds the next edit directly, e.g. `file_exhash(path, (\"9|e08c|\", \"p\"), (\"18|2f61|\", \"p\"), (\"28|3cc8|\", \"p\"))`. `(\"%\", \"p\")` views a whole file this way, and `(addr, \"g\", pat, (\"p\",))` is scoped grep: a verified row per matching line in the range. Where a call also edits, its printed lines appear in the diff as context rows, always shown even when no hunk is near them.\n\nAddressing:\n Address strings use lnhash addresses: lineno|hash| where hash is a 4-char\n hex content hash. Use lnhashview to get addresses:\n lnhashview file.txt show all lines with addresses\n lnhashview file.txt 10 20 show lines 10-20\n With multiple commands, hashes are checked immediately before each command runs; a single-line address may also match that line's call-start hash.\n\n Single: 12|a3f2|\n Range: 12|a3f2|,15|b1c3|\n Last: $ (last line)\n Whole: % (whole file or cell, same as 1,$; no hashes needed)\n Special: 0|0000| targets before line 1 (only with a or i)\n\nTuple commands:\n (addr, \"s\", pat, repl[, flags]) Substitute (Rust regex syntax: backrefs in repl are $1/$0/${name}; a two-char \\1 stays literal; a literal $ is written $$, and $name followed by more text needs ${name}text -- an unknown group reference is an error). Flags: g=all, i=case-insensitive. pat/repl are verbatim: literal newlines, slashes, and backslashes all work. For $-heavy replacement text, prefer a c command: its text has no template layer at all.\n (addr, \"d\") Delete line(s)\n (addr, \"a\", text) Append text after line\n (addr, \"i\", text) Insert text before line\n (addr, \"c\", text) Change/replace with text\n (addr, \"j\") Join with next line; with range, joins all lines in range\n (addr, \"m\", dest) Move line(s) after dest address\n (addr, \"t\", dest) Copy line(s) after dest address\n (addr, \">\", n) Indent n levels (default 1, 4 spaces each)\n (addr, \"<\", n) Dedent n levels (default 1)\n (addr, \"sort\") Sort lines alphabetically\n (addr, \"p\") View line(s): emit them as verified `lineno|hash|` rows, changing nothing\n (addr, \"g\", pat, sub), (addr, \"g!\", pat, sub), (addr, \"v\", pat, sub) Global commands: apply `sub` to each addressed line matching `pat` (`g!`/`v`: not matching). `sub` is a subcommand tuple without an address, e.g. (\"d\",) or (\"s\", \"foo\", \"bar\", \"g\"); globals cannot nest.\n (addr, \"y\", source, dest) Transliterate `source` chars to `dest` (equal counts required)\n\n\nCut/copy/paste between files and notebook cells:\nAny `m` (cut+paste) or `t` (copy+paste) address can carry a target prefix: `path:` for another file, or `path.ipynb:cellid:` for one cell's source (`cellid` exact or unique prefix). This is THE way to transfer existing lines between locations: the lines never pass through your output, so hard-to-retype content (long encoded strings, hashes, long literals) cannot be mistyped. Take source addresses from `lnhashview_file`/`lnhashview_cell` of each target as usual:\n\n file_exhash(path, (\"src/a.py:10|aaaa|,20|bbbb|\", \"m\", \"src/b.py:$\")) # cut a.py lines 10-20, paste at end of b.py\n file_exhash(path, (\"nb.ipynb:ab12cd34:6|830e|\", \"t\", \"other.ipynb:9f8e:$\")) # copy one cell line into another notebook's cell\n file_exhash(path, (\"nb.ipynb:ab12cd34:%\", \"t\", \"snippets.py:0|0000|\")) # copy a whole cell's source into a new file\n\nA range must stay within one file or cell, and cells are never created by a transfer (files can be, via a `0|0000|` destination). For whole-cell structural operations (add/delete/move cells) use `pyskills.ipynb`'s `copy_cells`/`paste_cells` instead; this idiom is for line-level transfer.\n\nReformatting a section like `gq`, plus optional indents: `j` the range onto one line, re-view, then split with ONE g-flagged `s` alternating the tokens (picked by eye) that should start each new line, captured and restored with the break and indent in the replacement: `(addr, \"s\", r\", ('foo'|'bar'|'baz')\", \",\\n $1\", \"g\")`\nImportant:\nDo not pass raw commands to Python APIs. Do not create addresses by text search or remembered line numbers, and never construct them by computing hashes (e.g. via `line_hash`): addresses come only from a fresh view immediately before the edit. On stale hash, re-view and rebuild. Where rgapi is installed, hits from `rg(pattern, lnhashs=True)` count as fresh views too: their addresses drop straight into commands (and `nbrg` finds the cell ids that `lnhashview_cell` takes). If reaching an address seems to need arithmetic, hand-copying, or a guessed hash, a step on that route was skipped. Tuple text fields can contain newlines wherever the command accepts text. For example, `(addr, \"s\", \"foo\", \"bar\\nbaz\")` replaces one line with two. Text fields are taken verbatim: a two-character `\\n` sequence stays literal; use an actual newline when you want a line break. For `a`/`i`/`c`, put all text in one tuple field: `\"first\\nsecond\"` starts with `first`, while `\"\\nfirst\"` inserts a leading blank line before `first`. For moving/copying between files or cells, use the qualified `m`/`t` addresses shown above. Missing files can only be created through `(r\"0|0000|\", \"a\", text)` or `(r\"0|0000|\", \"i\", text)` creation semantics.\n\nThe `%%exhash` cell magic:\nIn IPython sessions, importing this module registers the `%%exhash` cell magic: `%%exhash []
` applies one command whose text is everything below the magic line, taken verbatim (one trailing newline stripped). Passing `` targets that cell in an .ipynb file instead of a plain file (`cell_exhash`); the magic dispatches on token count, so no separate cell magic exists. Because the text is never parsed as Python, no quoting or escaping applies. Use it for EVERY `a`/`i`/`c` command, however simple the text looks: create a file with `%%exhash path 0|0000| a`; replace a whole cell or file with `%%exhash [] % c` (`%` needs no hashes: a full replace has no neighboring lines to mis-hit); replace a region within one with a range address and `c` (`%%exhash 12|a3f2|,15|b1c3| c`), both addresses straight from the one pre-edit view. Tuple `a`/`i`/`c` text fields are only for contexts without magics (scripts, tests): interactively they add a Python quoting layer whose failure modes are not reliably foreseeable, so do not use them.\nIPython expands `{expr}` and `$var` in the magic line from the user namespace (its standard `var_expand` for all magics), so a path or cell id held in a variable needs no retyping: `%%exhash {path} {cid} % c`. Only the line expands; the text stays verbatim.\n\nDocument outlines (`open_doc`):\nThe outline layer makes exhash a hierarchical editor: `open_doc` opens a file (`fname=`, or a `Path` as `src`; recorded for `refresh()` and edits), a URL (an `https?://` str, fetched), or any other str as text in hand, and returns a `Section` tree. Markdown sections come from headings; code files (py, js, ts, tsx, rs, zig, swift) from tree-sitter definitions; `.ipynb` files from md-heading cells over cells. Display the root bare: fixed-width rows `token title (count) [size] preview` (code rows have no title: the def line, signature included, opens the preview), previews \u00b6-joined with links rendered `[text][n]` so no URL spends tokens. The token is the verified address: `1.2.|12|a3f2|,45|b1c3|` fuses the dotted addr (trailing dot; the root's is `.`) with the section's boundary lnhash pair, so one row serves navigation, editing, and orientation: `at()` verifies the first hash, and the pair drops straight into a `file_exhash` range command. `at()` accepts ONLY tokens copied from a listing (`'1.6.|12|a3f2|'` or the full range form); bare dotted addrs raise, teaching the idiom. Live traversal is free: `d[1][6]`, `find(title)`, `search(pat)` (counts and matching-line previews), `paths(depth)`, `links(pat)`, and `open(n)` opens link `n` as a new tree with `base` recorded (a non-md target comes back as a leaf whose `.src` holds its text). `view()` is the reading view: rendered text with links `[text][n]` (`.src` is the raw source), displayed verbatim bare; `view(*tokens)` reads several sections in one call, each under a `# token` header when more than one, so a listing's tokens replace a print loop; `nums=`/`lnhashs=` switch any view to stored lines with edit-ready addresses. Notebook section tokens carry the heading cell id (`1.2.|ab12cd34|8f3a|`), and `view(lnhashs=True)` rows are `cellid:lineno|hash|`, ready for `cell_exhash`. The whole llms.txt docs workflow: `toc = open_doc(url)`; `toc.links(topic)`; `page = toc.open(n)`; display `page` bare; `page.view()` whole when small, else `page.search(topic)` and `page.view(*tokens)`.\n\"\"\"\n\n## types:\n- class Section(dict): ... # One document section: numbered child sections, source span, and a verified address\n- class Sections(list): ... # Sections listed as fixed-width `token title (count) [size] preview` rows; each row is the live node. Code rows have no title: the def line opens the preview\n- class Link: ... # One inline link, numbered in document reading order\n- class Links(list): ... # A list of `Link` rows, displayed one per line\n\n## functions:\n- def line_hash(line: str) -> str: ... # Return a 4-char lowercase hex hash for a single line of text. [3ea3]\n- def lnhash(lineno: int, line: str) -> str: ... # Return an lnhash address ``lineno|hash|`` for ``line`` at 1-based ``lineno``. [f9d5]\n- def lnhashview(text: str, start: int = None, end: int = None) -> LnhashView: ... # Return lines formatted as space-padded ``lineno|hash|content``. Optional 1-based ``start``/``end`` filter the range; ``end`` past EOF is clamped. [eb78]\n- def lnhashview_file(path: str, start: int = None, end: int = None) -> LnhashView: ... # Return lines formatted as space-padded ``lineno|hash|content`` for file at ``path`` (expands ``~``). Optional 1-based ``start``/``end`` filter the range; ``end`` past EOF is clamped. [52c2]\n- def lnhashview_cell(path: str, cell_id: str, start: int = None, end: int = None) -> LnhashView: ... # Return lines formatted as ``lineno|hash|content`` for the source of notebook cell ``cell_id`` in ipynb file at ``path`` (expands ``~``). ``cell_id`` may be an exact id or unique prefix; optional 1-based ``start``/``end`` filter the range. [08f8]\n- def lnhashview_cells(path: str, *cell_ids: str, start: int = None, end: int = None) -> LnhashView: ... # Return grouped lnhash views for explicit notebook cell ids in the ipynb file at ``path`` (expands ``~``). Each group starts with ``# cell ``; following lines keep normal ``lineno|hash|content`` format. [b6bd]\n- def exhash(text: str, cmds: list[tuple], sw: int = 4): ... # Verified line-addressed editor. Apply commands to `text`, return an EditResult.\u2026\n- def file_exhash(path: str, *cmds: tuple, sw: int = 4, inplace: bool = True): ... # Read files and notebook cells, apply file-aware exhash commands, and return per-target results or a combined diff.\u2026\n- def cell_exhash(path: str, cell_id: str, *cmds: tuple, sw: int = 4, inplace: bool = True): ... # Apply exhash commands to the source of notebook cell ``cell_id`` in ipynb file at ``path`` (expands ``~``).\u2026\n- def open_doc(src: str | Path = None, rm_fenced=True, fname: str = None): ... # Open a document as a `Section` tree: a file (`fname`, or a `Path` in `src`; recorded for `refresh` and edits), a URL (fetched), or text\u2026\n\n# module rgapi.skill:\n\n\"\"\"Fast and flexible file finding and search for Python. Use this when code needs `fd`-style file finding or `rg`-style searching.\n\nrgapi wraps the same `ignore`, `grep-regex`, and `grep-searcher` crates ripgrep uses, so `.gitignore`/`.ignore`/`.rgignore`, dot-file handling, glob/ext filters, and regex matching all behave like `rg`. Walking and searching run in parallel and most work stays in Rust, so results come back as structured Python objects instead of CLI text to parse. Prefer rgapi over shelling out to `rg`/`fd` or scanning files by hand: you get typed rows, byte-offset match spans, and lazy iteration.\n\nCore APIs:\n- `fd(root=\".\", ...)` finds paths with fd-style filters (`pattern` smart-case basename regex, `include`/`exclude`/`glob`, `ext`); returns slash-separated relative paths as `FileEntry` rows: `str` subclasses that lazily stat themselves for `size`/`mtime`/`is_dir`/`stat`. The list displays as an `ls -l`-style table (capped at `rgapi.MAX_REPR` rows); `str(res)` or `list(res)` give plain paths. Unfollowed symlinks are included, displaying with an `l` mode character; `show_target=True` appends `-> target` to their rows, and `link_target` holds it (`None` for non-links).\n- `ls(root=\".\", ...)` lists like the shell `ls`: one level, directories included, ignore rules off, sorted by name. It is `fd` with different defaults, so every `fd` filter works; `hidden=True` is `ls -a`.\n- `rg(pattern, root=\".\", ...)` returns matching `SearchLine` rows. `summary=True` instead returns blank-line-delimited `SearchBlock` rows, with newline runs displayed as `\u00b6` and `maxlen` source characters shown per block. Context is line-based normally and block-based in summary mode. `paths=True` returns unique paths, `count=True` returns a match-span total, and `lnhashs=True` shows exhash addresses. `summary=True` is incompatible with `paths` and `count`, but combines with `lnhashs` to show copyable block boundaries.\n- `nbrg(pattern, root=\".\", cell_context=0, maxlen=180, ...)` searches Jupyter `.ipynb` files (cell source only) and returns matched cells as `NbResults`/`NbCell`; `paths=True` and `count=True` reduce like `rg`'s. Its display is always a one-line cell summary. Use this for notebooks rather than `rg`, to avoid escaped JSON and get stable cell ids. `multiline=True` lets a pattern match across lines within a cell (line-oriented `rg` rejects `\n` in patterns); `^`/`$` still match at line boundaries.\n- `rgstr(pattern, text, ...)` searches a string already in hand (a fetched log, a captured output, a doc): `rg`-style `SearchLine` rows and context from in-memory text, with no path label and no separate `compile` step. Reach for it instead of ad hoc `splitlines()` loops over held text.\n\nIdiomatic usage:\n- Results display through tuned reprs designed to be read as-is: end the cell with the bare call and read what comes back - never join, slice, or otherwise reformat a result by hand. If a result is too big to take in full, narrow it with the function's own parameters (`max_results`, `paths=True`, `count=True`, tighter filters), not by post-processing the output.\n- For orientation, start with `rg(pattern, summary=True)` and drop to line-level rows only where needed; add `lnhashs=True` when an edit may follow, so hits arrive with copyable addresses. This applies beyond unfamiliar code: on prose, config, and markdown, a summary row is the whole matched paragraph, usually exactly the read you wanted.\n- In kernel sessions, reach for `ls()` rather than shell `ls`, and `fd()` rather than shell `find`, for listings you or later calls will read.\n\nSearchLine rows:\n kind 'match', 'before', 'after', or 'context'\n path path relative to root\n line_number 1-based line number\n lnhash exhash-style `lineno|hash|` address\n line line text without the trailing newline\n matches list of (start, end) byte offsets, for 'match' rows\n asdict() returns the row fields as a plain dict\n\nSearchBlock rows (from `rg(summary=True)`):\n path/block_index/start_line/end_line/start_lnhash/end_lnhash locate the block\n kind 'match' or 'context'\n source full block source\n matches list of matching SearchLine rows within the block\n asdict() returns the row fields as a plain dict\nOutput uses `path:start-end:source` for matches and `path:start-end-source` for context. With `lnhashs=True`, `start-end` becomes `start_lnhash,end_lnhash` (or one hash for a one-line block). Empty or whitespace-only lines delimit blocks; `context=N` adds N neighbouring blocks. Multiple matches in one block produce one row.\n\nNbCell rows (from `nbrg`):\n path/cell_index/cell_id/cell_type locate the cell ('code'/'markdown'/'raw')\n kind 'match' or 'context'\n source full cell source\n matches list of SearchLine rows for the matched lines within the cell\n asdict() returns the cell fields as a plain dict\nOutput is keyed by `cell_id` (the nbformat cell/message id), not line number: `path:cell_id:source` for matches and `path:cell_id-source` for context. A match row starts at its first matched line: earlier lines display as `\u2026[Ln]` (`n` the matched line's 1-based number in the cell), except that a leading `#|` directive line is kept, e.g. `#| export\u2026[L4]needle here`. Newline runs display as `\u00b6` (following indentation kept) and `maxlen` limits displayed source without changing `source`. `cell_context=N` adds N neighbouring cells. Walking, parsing, and matching run in parallel in Rust; outputs and metadata are skipped.\n\nImportant:\nTraversal is parallel and result order is NOT guaranteed; wrap in `sorted(...)` if you need stable order. `path_re`/`skip_path_re` filter the returned/searched paths but do not prune traversal; use `skip_dir`/`skip_dir_re` to prune whole subtrees for speed. Run `doc(func)` for full parameter docments.\n\"\"\"\n\n## types:\n- class RgIter: ... # Initialize self. See help(type(self)) for accurate signature.\n\n## functions:\n- def fd(root: str | Path = '.', pattern: str | NoneType = None, files: bool = True, dirs: bool = False, show_target: bool = False, timeout_ms: int | NoneType = None, *, ext: str | list | NoneType = None, ignore: bool = True, max_depth: int | NoneType = None, **walk_params) -> PathResults: ... # Find paths with fd-style filters and gitignore support.\u2026\n- def ls(root: str | Path = '.', pattern: str | NoneType = None, dirs: bool = True, max_depth: int | NoneType = 1, ignore: bool = False, *, files: bool = True, show_target: bool = False, timeout_ms: int | NoneType = None, ext: str | list | NoneType = None, **walk_params) -> PathResults: ... # List a directory like `ls`: one level, directories included, ignore rules off, sorted by name.\u2026\n- def rg(pattern: str, root: str | Path = '.', case_sensitive: bool | NoneType = None, smart_case: bool = False, before_context: int = 0, after_context: int = 0, context: int = 0, paths: bool = False, count: bool = False, max_results: int | NoneType = None, lnhashs: bool = False, timeout_ms: int | NoneType = None, summary: bool = False, maxlen: int = 180, *, ext: str | list | NoneType = None, ignore: bool = True, max_depth: int | NoneType = None, **walk_params): ... # Search files and return `SearchResults`, matched paths, or a count; `lnhashs=True` shows exhash-style addresses.\u2026\n- def rg_iter(pattern: str, root: str | Path = '.', case_sensitive: bool | NoneType = None, smart_case: bool = False, before_context: int = 0, after_context: int = 0, context: int = 0, lnhashs: bool = False, *, ext: str | list | NoneType = None, ignore: bool = True, max_depth: int | NoneType = None, **walk_params) -> RgIter: ... # Search files lazily, yielding `SearchLine` rows; `lnhashs=True` shows exhash-style addresses.\u2026\n- def nbrg(pattern: str, root: str | Path = '.', cell_context: int = 0, case_sensitive: bool | NoneType = None, smart_case: bool = False, paths: bool = False, multiline: bool = False, max_results: int | NoneType = None, count: bool = False, timeout_ms: int | NoneType = None, maxlen: int = 180, *, ignore: bool = True, max_depth: int | NoneType = None, **walk_params): ... # Search `.ipynb` cell sources under `root` in parallel, returning matched cells, paths, or a count.\u2026\n- def rgstr(pattern: str, text: str, case_sensitive: bool | NoneType = None, smart_case: bool = False, *, before_context: int = 0, after_context: int = 0, context: int = 0) -> SearchResults: ... # Search text already in hand, like `rg` on a string: no path label, no separate `compile`.\u2026\n\n## shared params:\n- **walk_params (from `fd`):\n glob: str | list | NoneType = None, # Include glob or globs; alias for `include`\n include: str | list | NoneType = None, # Include glob or globs, e.g. `*.py`\n exclude: str | list | NoneType = None, # Exclude glob or globs, e.g. `test_*.py`\n hidden: bool = False, # Include hidden files and directories\n min_depth: int | NoneType = None, # Minimum depth for returned/searched paths\n max_filesize: int | NoneType = None, # Skip files larger than this many bytes\n follow_links: bool = False, # Follow symbolic links while walking\n same_file_system: bool = False, # Do not cross filesystem boundaries\n path_re: str | NoneType = None, # Regex that relative paths must match\n skip_path_re: str | NoneType = None, # Regex for relative paths to skip\n skip_dir: str | list | NoneType = None, # Directory glob or globs to prune\n skip_dir_re: str | NoneType = None, # Directory regex used to prune traversal"}]} -{"type": "assistant", "uuid": "7c73702c-fd10-5c58-bb93-1e74416125da", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/Users/jhoward/aai-ws", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "assistant", "content": [{"type": "tool_use", "id": "call_d41f97015d13d9bc", "name": "mcp__clikernel__execute", "input": {"code": "doc(acp)"}}], "id": "msg_40cc359d57ba503bbc8b3251", "stop_reason": "tool_use", "stop_sequence": null, "stop_details": null, "usage": {"input_tokens": 13596, "output_tokens": 6, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 0}}, "requestId": "req_afeb2a43bddf5fa2860907e3"} -{"type": "user", "uuid": "0d7a6876-dfe7-5b25-87c1-468bc27ebd08", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/Users/jhoward/aai-ws", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "user", "content": [{"type": "tool_result", "tool_use_id": "call_d41f97015d13d9bc", "content": "# module aai_coding.coding_patterns:\n\n\"\"\"Jeremy's coding style and conventions: read before writing, reviewing, or assessing any code.\n\n# Coding Patterns\n\nBased on the fastai style guide and Jeremy's 3 decades of coding experience, and used across all of his projects, not only fastai ones.\n\nUse the fastcore/fasthtml ecosystem (fastcore, fasthtml, fastlite, ...) when picking libraries. These are Jeremy's own, editable-installed as siblings, and preferred over heavier third-party alternatives.\n\n## Improving Tooling Pays Off Exponentially\n\nMaking our tools marvellous matters more than the task in hand. A finished task helps once. A better tool helps every later task, every later session, and the whole team. Ergonomics count as much as capability. Most code you have read was written by people who put up with tool friction rather than fix it, so your default is the workaround. Here the tools are ours and one edit away. When something grates, fix it or raise it. Never quietly work around it.\n\n## Every Construct Must Earn Its Place\n\nReaders assume everything present is necessary. When they see `str(x)` on something that's already a str, they stop and wonder what subtle thing it's guarding against. When the answer is \"nothing\", they paid for a mystery with no payoff. The same goes for defensive copies (`list(x)` that's never mutated), \"just in case\" try/excepts, redundant type coercions, and unused parameters. Before adding any construct, know why it's needed. If you can't say, leave it out. The cost doubles in nbdev projects, where tests are documentation.\n\nKnowing why a construct is needed does not mean waiting for someone to ask for it. An open source user who finds a gap concludes the library cannot do it and moves on. Judge a library feature by whether the design calls for it and users would expect it.\n\nThe same applies to prose in code: almost never add comments (only when the code is truly unclear), and don't add type hints, docstrings, or boilerplate that pull no weight. Prefer concise, readable code over verbose \"enterprise\" style.\nOnly write a code comment to state a constraint the code itself can't show, never to say where it came from, what the next line does, or why your change is correct. That's you talking to the reviewer, not the next reader, and it's noise the moment the PR merges.\n\n## Plain Commands: Flags, Redirects, and Pipes Are for Exceptions\n\nThe same principle applies to running commands. The bare invocation is the contract: `pytest tests -q`, `cargo fmt`, `maturin develop`. Project config exists precisely so the plain command does the right thing, and then every invocation is short, identical, and instantly readable. A flag, redirect, or pipe is a statement (\"this one call has a requirement the defaults don't cover\"), and used that way it carries information: the reader stops, asks why, and there's an answer. Sprinkled defensively on every call, decorations destroy that signal: every command looks exceptional so none is, and the one deliberate flag is indistinguishable from habit. Transcripts compound the damage, since each decorated call teaches later calls to decorate too.\n\n- Wanting the same flag on every run means it's a missing config line: promote it and go back to the bare command (`pytest --timeout 300` on every run becomes `timeout = 60` under `[tool.pytest.ini_options]`).\n- Don't check-then-apply when applying is the goal: `cargo fmt`, not `cargo fmt --check` followed by `cargo fmt`. `--check` is for a final no-mutation verification, e.g. CI.\n- Run commands bare and read their output. Never pipe through truncating filters (`| tail -20`, `| grep PASS`): truncation is decided before the output exists, and it hides exactly the surprises worth seeing. Never merge stderr into stdout with `2>&1`: separated, a crash is unmissable.\n- On the rare occasion output genuinely can't come back inline (far too large), redirect the streams separately (`>meta/stdout.txt 2>meta/stderr.txt`) and read the files from the kernel.\n- A real one-off requirement gets its flag once, with the reason stated alongside, and disappears again on the next call.\n\n## Docments\n\nDocments are trailing comments on function parameters that fastcore uses for documentation. A signature with docments (or any long signature) uses this layout: `(` stays on the def line, each parameter on its own line indented 4, and `):` alone on its own line at def indent:\n\n```python\n@delegates(start_kernel)\nasync def run_kernel(\n kernel_name='python3', # Kernelspec name to launch\n manager_cls=KernelManager, # Manager class, e.g. a subclass customizing launch\n **kwargs\n):\n```\n\nNEVER remove docments when refactoring. They're essential documentation.\n\nWhen `**kwargs` passes through to a known callee, decorate with `@delegates(callee)` so the signature shows the real options. delegates REQUIRES the collector to be named `kwargs`, not `kw` etc. Skip the decorator when the callee's own signature is just `**kwargs` (nothing to delegate).\n\n## Raw Strings\n\nWrite any non-trivial string literal as a raw string (`r\"...\"` / `r\"\"\"...\"\"\"`): regexes, text you pass to tools, code or markup inside strings, anything multi-line or containing backslashes. In plain strings a stray `\\n` or `\\d` either errors or silently corrupts, and each miss costs a round trip to diagnose plus another to fix. Raw strings are WYSIWYG, so the first attempt matches what you meant. The `r` costs nothing when no escapes are present, so make it the default, not the exception.\n\n## Style Checker (chkstyle)\n\nRun `chkstyle {path}` to check fastai style (only include path if needed). But use judgment. chkstyle is a hint, not gospel.\nIn nbdev projects, point it at the notebooks (`chkstyle nbs/00_core.ipynb`), not the exported `.py`. The notebook run also checks example and test cells, which never reach the module.\n\n## Config Patterns\n\nRead from standard locations rather than duplicating config:\n\n- GitHub release notes config: `.github/release.yml`\n- Project config: `pyproject.toml` under `[tool.yourpkg]`\n- Infer values when possible (e.g., package name from `[project].name`)\n- Bundle data files inside the package and read them with `importlib.resources`\n\n## Versioning: Bump After Release\n\nJeremy bumps the version immediately after each release, as part of releasing and never as part of a change. The tree therefore always carries the next release's version. A sibling dep pin can name a version before it ships (`foo>=`).\n\nA downstream pin is part of the change that creates the dependency. When a change makes one package consume another's new behavior or API, stamp the consumers' pins in the same session. A pin deferred to release time is a forgotten pin.\n\nThis convention is for Python projects. Other artifact types version at change time instead: for example, the Claude Code plugins in skill-plugins bump automatically when `./build.py` regenerates a changed output.\n\n## Project Layout\n\nPrefer flat layout over src/:\n\n```\nmyproject/\n\u251c\u2500\u2500 mypkg/\n\u2502 \u2514\u2500\u2500 __init__.py\n\u251c\u2500\u2500 tests/\n\u251c\u2500\u2500 pyproject.toml\n\u2514\u2500\u2500 README.md\n```\n\n## Testing\n\nAll code has writing, maintenance, and readability costs, and tests most of all: every test must be kept passing forever, gets read by every future contributor, and must be revised whenever the behavior it pins changes. So never write a test as a reflex, and don't aim for anywhere near 100% coverage. A test earns its place only when:\n\n- it documents an idea: in nbdev notebooks, tests ARE the documentation (see `doc(nbdev.skill)`), or\n- the logic is intricate enough that you had to think carefully to get it right (edge cases, parsing, arithmetic, tricky conditionals: the places a future change could silently break it), or\n- the code assumes something about an external system (a file format, an API's response shape, another tool's behavior) that is somewhat likely to change one day, and we want to hear about it when the assumption is violated. These must exercise the real thing (a mock merely re-states our assumption), so they're usually the slow-marked tests\n\nWiring and orchestration get zero tests: re-exports, delegations, one-line glue, functions that only sequence calls to other tools. A test there only asserts that Python works, and pins down internals we may want to change. Strong tell: if a test needs recording fakes or mock collaborators to reach the code, it's testing a transcript of the implementation, not logic. Extract the logic into a small pure function and test that, or don't test at all.\n\nIF you add a pytest test, ALWAYS work red-green: write it FIRST, run it to see it fail, THEN make the change, then run it again to see it pass. In nbdev projects there are no test cells - see `doc(nbdev.skill)`: changes revise lesson cells, and the red-green check applies only to an assertion you actually revised or added.\n\n- Prefer as few tests as possible: a single test that walks through many checks is more readable and faster than many small ones\n- A check worth keeping goes in a real test file or notebook cell, never left as an ad-hoc command. In a notebook, the checks made while exploring often ARE the narrative (each one both documents what we needed to know and keeps guarding it), so they stay as example cells. In a pytest file, an exploratory check survives only if it meets one of the criteria above\n- Assert the logic, not incidentals: check what the behavior guarantees, never byte-exact renderings, exact reprs, or field order. A test that compares a whole output string locks in formatting decisions that were never the point (e.g. assert the content appears in a markdown display block, not the display's exact text). NEVER use tests to \"lock in\" behavior, unless that exact behavior really is a key part of the logic or contract that must always be true forever\n- Use `pytest -q` (not `python -m pytest`, which prompts for permission). nbdev projects use `nbdev-test` on the changed notebook, but some notebooks are slow or hit live services, so check with the user before running one you don't know is safe (known safe: all of pyskills)\n- Don't run slow-marked tests until finishing a session, or after a change likely to directly impact them\n\n## One-liner Patterns\n\n```python\n# Conditionals\nif not x: return default\nif x and y: do_thing()\n\n# Try/except\ntry: return run(\"cmd\").strip() or default\nexcept Exception: return default\n\n# Loops\nfor i in range(n): result[i] = 0\nwhile len(items) < 3: items.append(0)\n```\n\n## Import Style\n\nCombine imports on single lines:\n\n```python\nimport os, re, sys, shutil\nfrom pathlib import Path\nfrom fastcore.utils import *\n```\n\n`from fastcore.utils import *` already provides `os`, `Path`, and much of the stdlib, so don't re-import those alongside it.\n\"\"\""}]}, "toolUseResult": [{"type": "text", "text": "# module aai_coding.coding_patterns:\n\n\"\"\"Jeremy's coding style and conventions: read before writing, reviewing, or assessing any code.\n\n# Coding Patterns\n\nBased on the fastai style guide and Jeremy's 3 decades of coding experience, and used across all of his projects, not only fastai ones.\n\nUse the fastcore/fasthtml ecosystem (fastcore, fasthtml, fastlite, ...) when picking libraries. These are Jeremy's own, editable-installed as siblings, and preferred over heavier third-party alternatives.\n\n## Improving Tooling Pays Off Exponentially\n\nMaking our tools marvellous matters more than the task in hand. A finished task helps once. A better tool helps every later task, every later session, and the whole team. Ergonomics count as much as capability. Most code you have read was written by people who put up with tool friction rather than fix it, so your default is the workaround. Here the tools are ours and one edit away. When something grates, fix it or raise it. Never quietly work around it.\n\n## Every Construct Must Earn Its Place\n\nReaders assume everything present is necessary. When they see `str(x)` on something that's already a str, they stop and wonder what subtle thing it's guarding against. When the answer is \"nothing\", they paid for a mystery with no payoff. The same goes for defensive copies (`list(x)` that's never mutated), \"just in case\" try/excepts, redundant type coercions, and unused parameters. Before adding any construct, know why it's needed. If you can't say, leave it out. The cost doubles in nbdev projects, where tests are documentation.\n\nKnowing why a construct is needed does not mean waiting for someone to ask for it. An open source user who finds a gap concludes the library cannot do it and moves on. Judge a library feature by whether the design calls for it and users would expect it.\n\nThe same applies to prose in code: almost never add comments (only when the code is truly unclear), and don't add type hints, docstrings, or boilerplate that pull no weight. Prefer concise, readable code over verbose \"enterprise\" style.\nOnly write a code comment to state a constraint the code itself can't show, never to say where it came from, what the next line does, or why your change is correct. That's you talking to the reviewer, not the next reader, and it's noise the moment the PR merges.\n\n## Plain Commands: Flags, Redirects, and Pipes Are for Exceptions\n\nThe same principle applies to running commands. The bare invocation is the contract: `pytest tests -q`, `cargo fmt`, `maturin develop`. Project config exists precisely so the plain command does the right thing, and then every invocation is short, identical, and instantly readable. A flag, redirect, or pipe is a statement (\"this one call has a requirement the defaults don't cover\"), and used that way it carries information: the reader stops, asks why, and there's an answer. Sprinkled defensively on every call, decorations destroy that signal: every command looks exceptional so none is, and the one deliberate flag is indistinguishable from habit. Transcripts compound the damage, since each decorated call teaches later calls to decorate too.\n\n- Wanting the same flag on every run means it's a missing config line: promote it and go back to the bare command (`pytest --timeout 300` on every run becomes `timeout = 60` under `[tool.pytest.ini_options]`).\n- Don't check-then-apply when applying is the goal: `cargo fmt`, not `cargo fmt --check` followed by `cargo fmt`. `--check` is for a final no-mutation verification, e.g. CI.\n- Run commands bare and read their output. Never pipe through truncating filters (`| tail -20`, `| grep PASS`): truncation is decided before the output exists, and it hides exactly the surprises worth seeing. Never merge stderr into stdout with `2>&1`: separated, a crash is unmissable.\n- On the rare occasion output genuinely can't come back inline (far too large), redirect the streams separately (`>meta/stdout.txt 2>meta/stderr.txt`) and read the files from the kernel.\n- A real one-off requirement gets its flag once, with the reason stated alongside, and disappears again on the next call.\n\n## Docments\n\nDocments are trailing comments on function parameters that fastcore uses for documentation. A signature with docments (or any long signature) uses this layout: `(` stays on the def line, each parameter on its own line indented 4, and `):` alone on its own line at def indent:\n\n```python\n@delegates(start_kernel)\nasync def run_kernel(\n kernel_name='python3', # Kernelspec name to launch\n manager_cls=KernelManager, # Manager class, e.g. a subclass customizing launch\n **kwargs\n):\n```\n\nNEVER remove docments when refactoring. They're essential documentation.\n\nWhen `**kwargs` passes through to a known callee, decorate with `@delegates(callee)` so the signature shows the real options. delegates REQUIRES the collector to be named `kwargs`, not `kw` etc. Skip the decorator when the callee's own signature is just `**kwargs` (nothing to delegate).\n\n## Raw Strings\n\nWrite any non-trivial string literal as a raw string (`r\"...\"` / `r\"\"\"...\"\"\"`): regexes, text you pass to tools, code or markup inside strings, anything multi-line or containing backslashes. In plain strings a stray `\\n` or `\\d` either errors or silently corrupts, and each miss costs a round trip to diagnose plus another to fix. Raw strings are WYSIWYG, so the first attempt matches what you meant. The `r` costs nothing when no escapes are present, so make it the default, not the exception.\n\n## Style Checker (chkstyle)\n\nRun `chkstyle {path}` to check fastai style (only include path if needed). But use judgment. chkstyle is a hint, not gospel.\nIn nbdev projects, point it at the notebooks (`chkstyle nbs/00_core.ipynb`), not the exported `.py`. The notebook run also checks example and test cells, which never reach the module.\n\n## Config Patterns\n\nRead from standard locations rather than duplicating config:\n\n- GitHub release notes config: `.github/release.yml`\n- Project config: `pyproject.toml` under `[tool.yourpkg]`\n- Infer values when possible (e.g., package name from `[project].name`)\n- Bundle data files inside the package and read them with `importlib.resources`\n\n## Versioning: Bump After Release\n\nJeremy bumps the version immediately after each release, as part of releasing and never as part of a change. The tree therefore always carries the next release's version. A sibling dep pin can name a version before it ships (`foo>=`).\n\nA downstream pin is part of the change that creates the dependency. When a change makes one package consume another's new behavior or API, stamp the consumers' pins in the same session. A pin deferred to release time is a forgotten pin.\n\nThis convention is for Python projects. Other artifact types version at change time instead: for example, the Claude Code plugins in skill-plugins bump automatically when `./build.py` regenerates a changed output.\n\n## Project Layout\n\nPrefer flat layout over src/:\n\n```\nmyproject/\n\u251c\u2500\u2500 mypkg/\n\u2502 \u2514\u2500\u2500 __init__.py\n\u251c\u2500\u2500 tests/\n\u251c\u2500\u2500 pyproject.toml\n\u2514\u2500\u2500 README.md\n```\n\n## Testing\n\nAll code has writing, maintenance, and readability costs, and tests most of all: every test must be kept passing forever, gets read by every future contributor, and must be revised whenever the behavior it pins changes. So never write a test as a reflex, and don't aim for anywhere near 100% coverage. A test earns its place only when:\n\n- it documents an idea: in nbdev notebooks, tests ARE the documentation (see `doc(nbdev.skill)`), or\n- the logic is intricate enough that you had to think carefully to get it right (edge cases, parsing, arithmetic, tricky conditionals: the places a future change could silently break it), or\n- the code assumes something about an external system (a file format, an API's response shape, another tool's behavior) that is somewhat likely to change one day, and we want to hear about it when the assumption is violated. These must exercise the real thing (a mock merely re-states our assumption), so they're usually the slow-marked tests\n\nWiring and orchestration get zero tests: re-exports, delegations, one-line glue, functions that only sequence calls to other tools. A test there only asserts that Python works, and pins down internals we may want to change. Strong tell: if a test needs recording fakes or mock collaborators to reach the code, it's testing a transcript of the implementation, not logic. Extract the logic into a small pure function and test that, or don't test at all.\n\nIF you add a pytest test, ALWAYS work red-green: write it FIRST, run it to see it fail, THEN make the change, then run it again to see it pass. In nbdev projects there are no test cells - see `doc(nbdev.skill)`: changes revise lesson cells, and the red-green check applies only to an assertion you actually revised or added.\n\n- Prefer as few tests as possible: a single test that walks through many checks is more readable and faster than many small ones\n- A check worth keeping goes in a real test file or notebook cell, never left as an ad-hoc command. In a notebook, the checks made while exploring often ARE the narrative (each one both documents what we needed to know and keeps guarding it), so they stay as example cells. In a pytest file, an exploratory check survives only if it meets one of the criteria above\n- Assert the logic, not incidentals: check what the behavior guarantees, never byte-exact renderings, exact reprs, or field order. A test that compares a whole output string locks in formatting decisions that were never the point (e.g. assert the content appears in a markdown display block, not the display's exact text). NEVER use tests to \"lock in\" behavior, unless that exact behavior really is a key part of the logic or contract that must always be true forever\n- Use `pytest -q` (not `python -m pytest`, which prompts for permission). nbdev projects use `nbdev-test` on the changed notebook, but some notebooks are slow or hit live services, so check with the user before running one you don't know is safe (known safe: all of pyskills)\n- Don't run slow-marked tests until finishing a session, or after a change likely to directly impact them\n\n## One-liner Patterns\n\n```python\n# Conditionals\nif not x: return default\nif x and y: do_thing()\n\n# Try/except\ntry: return run(\"cmd\").strip() or default\nexcept Exception: return default\n\n# Loops\nfor i in range(n): result[i] = 0\nwhile len(items) < 3: items.append(0)\n```\n\n## Import Style\n\nCombine imports on single lines:\n\n```python\nimport os, re, sys, shutil\nfrom pathlib import Path\nfrom fastcore.utils import *\n```\n\n`from fastcore.utils import *` already provides `os`, `Path`, and much of the stdlib, so don't re-import those alongside it.\n\"\"\""}]} -{"type": "assistant", "uuid": "e78233f3-cf65-59a2-9d7b-1b5645c833d7", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/Users/jhoward/aai-ws", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "assistant", "content": [{"type": "tool_use", "id": "toolu_01YPiAitbgdQ2HkRQYJSruHV", "name": "mcp__clikernel__execute", "input": {"code": "list_pyskills()"}}], "id": "msg_80473309006c5dc3866f0b14", "stop_reason": "tool_use", "stop_sequence": null, "stop_details": null, "usage": {"input_tokens": 16203, "output_tokens": 6, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 0}}, "requestId": "req_cdf6ba5e9c0c552fb1128b5c"} -{"type": "user", "uuid": "353cf856-d6f0-50a6-9425-9467a840dd52", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/Users/jhoward/aai-ws", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "user", "content": [{"type": "tool_result", "tool_use_id": "toolu_01YPiAitbgdQ2HkRQYJSruHV", "content": "{'nbdev.skill': 'Author clear, executable nbdev notebooks where code, prose, examples, outputs, and tests form one coherent narrative.',\n 'dialoghelper.solveitskill': 'Read, search, edit, and manage Solveit dialogs using dialoghelper.core, including dialog/message addressing, line-numbered inspection, targeted message edits, add/update/delete/copy/paste workflows, and safe editing patterns.',\n 'tracefunc.skill': 'See what a Python function did, line by line: how many times each line ran and what the variables held. Use when working out why code takes a branch, loops, recurses, or computes a wrong value, without changing that code or stepping through a debugger.',\n 'rgapi.skill': 'Fast and flexible file finding and search for Python. Use this when code needs `fd`-style file finding or `rg`-style searching.',\n 'ghapi.skill': 'GitHub REST API access via `GhApi`, plus local git operations via `fastgit.Git`. Use this for day-to-day GitHub work: reading/creating issues and PRs, checking CI status, managing releases/branches/gists, and repo-local git operations -- all from Python, no shelling out to `gh`/`git` needed.',\n 'fastanki.skill': 'Anki flashcard tools for spaced repetition. Direct sqlite and AnkiWeb sync, with no Anki app needed.',\n 'remold': \"Structural search and rewrite for Python source: declarative ast-grep pattern rules, LibCST matcher transforms for everything patterns can't express, and tree-based symbol queries. Use this to edit code by structure (rename calls, move methods, rewrite APIs) where regexes break and `ast` loses comments.\",\n 'jph': \"Jeremy's tools, which for now is just his notes system. Use if Jeremy says 'create a note', or 'check my notes' or similar.\",\n 'cordslite.skill': 'Read-only access to Discord through cordslite. Covers connecting, opening a guild, finding your way around its channels, searching messages, reading threads, and fetching attachments.',\n 'fastmux.skill': 'Work with tmux sessions, windows, and panes from Python, plus named background sessions keyed by sid. Use this when code needs to read what is on a terminal, send input to an interactive program, build pane layouts, search text across terminals, or keep a terminal open that you and the user can both see.',\n 'exhash.skill': 'Universal hash-verified text editing for local files. Use this for one reliable way to read, preview, and change text files.',\n 'clikernel.skill': 'Use the `clikernel` MCP session as the default workspace for Python work: reading and changing files, notebook work, trying things out, checking how a library behaves, and reshaping data. One session stays open, so imports and variables carry between calls. Read this before writing, running, or debugging Python code in a session with `clikernel` connected.',\n 'fastcdp.skill': 'Work with Chrome through the DevTools Protocol: open pages, click and type, read a page as an accessibility tree, keep a log of console and network activity for debugging, and call any CDP command.',\n 'aai_coding.coding_patterns': \"Jeremy's coding style and conventions: read before writing, reviewing, or assessing any code.\",\n 'aai_coding.harness_docs': 'Official docs for the tool you are running in (Claude Code or codex): read before answering questions about its behaviour, config, or features.',\n 'aai_coding.looker': 'Answer questions about images and PDFs by asking a separate codex process to look at them, which keeps large files out of this session (macOS only). Use it to verify rendered output: PDFs, screenshots, captured windows.',\n 'aai_coding.theory': 'What \"Theory\" (capital T) means in this team\\'s vocabulary: Peter Naur\\'s programming-as-theory-building. Read when the user says the Theory of something, or asks to discuss, find, or capture a Theory.',\n 'aai_coding.write_docs': 'How to write reference prose: read before writing docstrings, READMEs, API docs, PR descriptions, commit messages, or messages to co-workers.',\n 'aai_coding.write_prose': \"How to write narrative prose that reads as though a person wrote it: read before writing blog posts, essays, announcements, or anything else with the author's voice. write-docs covers reference prose.\",\n 'aai_coding.write_summary': 'How to write summaries that stand alone: read before summarizing a conversation, meeting, PR, paper, or repo change for a human reader.',\n 'fastcore.editskill': 'Text, file, cell, and notebook editing from `fastcore.tools` and `fastcore.nbio`, plus the conventions the whole fastai editing toolkit follows. Read this before working with the editing tools in any package that shares them.',\n 'pyskills.skill': 'Pyskills are tool modules that Python packages register so you can find them without importing everything. `list_pyskills()` names the ones installed, with a one-line description each, and needs no imports. Load one with a normal import, then read its docs with `doc()`.',\n 'llmsurgery.skill': 'Read and work with Claude Code and Codex session transcripts.',\n 'aidialog.dlgskill': \"Read, search, and edit dialogs and notebooks through aidialog's `Dialog` and `Message` classes\",\n 'fastcflare.skill': 'Cloudflare API access via `CloudflareApi`: zones, DNS records, caching, and every other Cloudflare v4 feature from Python. Use this for day-to-day Cloudflare work instead of hand-built httpx calls against api.cloudflare.com.',\n 'test.skill': 'A test skill.'}"}]}, "toolUseResult": [{"type": "text", "text": "{'nbdev.skill': 'Author clear, executable nbdev notebooks where code, prose, examples, outputs, and tests form one coherent narrative.',\n 'dialoghelper.solveitskill': 'Read, search, edit, and manage Solveit dialogs using dialoghelper.core, including dialog/message addressing, line-numbered inspection, targeted message edits, add/update/delete/copy/paste workflows, and safe editing patterns.',\n 'tracefunc.skill': 'See what a Python function did, line by line: how many times each line ran and what the variables held. Use when working out why code takes a branch, loops, recurses, or computes a wrong value, without changing that code or stepping through a debugger.',\n 'rgapi.skill': 'Fast and flexible file finding and search for Python. Use this when code needs `fd`-style file finding or `rg`-style searching.',\n 'ghapi.skill': 'GitHub REST API access via `GhApi`, plus local git operations via `fastgit.Git`. Use this for day-to-day GitHub work: reading/creating issues and PRs, checking CI status, managing releases/branches/gists, and repo-local git operations -- all from Python, no shelling out to `gh`/`git` needed.',\n 'fastanki.skill': 'Anki flashcard tools for spaced repetition. Direct sqlite and AnkiWeb sync, with no Anki app needed.',\n 'remold': \"Structural search and rewrite for Python source: declarative ast-grep pattern rules, LibCST matcher transforms for everything patterns can't express, and tree-based symbol queries. Use this to edit code by structure (rename calls, move methods, rewrite APIs) where regexes break and `ast` loses comments.\",\n 'jph': \"Jeremy's tools, which for now is just his notes system. Use if Jeremy says 'create a note', or 'check my notes' or similar.\",\n 'cordslite.skill': 'Read-only access to Discord through cordslite. Covers connecting, opening a guild, finding your way around its channels, searching messages, reading threads, and fetching attachments.',\n 'fastmux.skill': 'Work with tmux sessions, windows, and panes from Python, plus named background sessions keyed by sid. Use this when code needs to read what is on a terminal, send input to an interactive program, build pane layouts, search text across terminals, or keep a terminal open that you and the user can both see.',\n 'exhash.skill': 'Universal hash-verified text editing for local files. Use this for one reliable way to read, preview, and change text files.',\n 'clikernel.skill': 'Use the `clikernel` MCP session as the default workspace for Python work: reading and changing files, notebook work, trying things out, checking how a library behaves, and reshaping data. One session stays open, so imports and variables carry between calls. Read this before writing, running, or debugging Python code in a session with `clikernel` connected.',\n 'fastcdp.skill': 'Work with Chrome through the DevTools Protocol: open pages, click and type, read a page as an accessibility tree, keep a log of console and network activity for debugging, and call any CDP command.',\n 'aai_coding.coding_patterns': \"Jeremy's coding style and conventions: read before writing, reviewing, or assessing any code.\",\n 'aai_coding.harness_docs': 'Official docs for the tool you are running in (Claude Code or codex): read before answering questions about its behaviour, config, or features.',\n 'aai_coding.looker': 'Answer questions about images and PDFs by asking a separate codex process to look at them, which keeps large files out of this session (macOS only). Use it to verify rendered output: PDFs, screenshots, captured windows.',\n 'aai_coding.theory': 'What \"Theory\" (capital T) means in this team\\'s vocabulary: Peter Naur\\'s programming-as-theory-building. Read when the user says the Theory of something, or asks to discuss, find, or capture a Theory.',\n 'aai_coding.write_docs': 'How to write reference prose: read before writing docstrings, READMEs, API docs, PR descriptions, commit messages, or messages to co-workers.',\n 'aai_coding.write_prose': \"How to write narrative prose that reads as though a person wrote it: read before writing blog posts, essays, announcements, or anything else with the author's voice. write-docs covers reference prose.\",\n 'aai_coding.write_summary': 'How to write summaries that stand alone: read before summarizing a conversation, meeting, PR, paper, or repo change for a human reader.',\n 'fastcore.editskill': 'Text, file, cell, and notebook editing from `fastcore.tools` and `fastcore.nbio`, plus the conventions the whole fastai editing toolkit follows. Read this before working with the editing tools in any package that shares them.',\n 'pyskills.skill': 'Pyskills are tool modules that Python packages register so you can find them without importing everything. `list_pyskills()` names the ones installed, with a one-line description each, and needs no imports. Load one with a normal import, then read its docs with `doc()`.',\n 'llmsurgery.skill': 'Read and work with Claude Code and Codex session transcripts.',\n 'aidialog.dlgskill': \"Read, search, and edit dialogs and notebooks through aidialog's `Dialog` and `Message` classes\",\n 'fastcflare.skill': 'Cloudflare API access via `CloudflareApi`: zones, DNS records, caching, and every other Cloudflare v4 feature from Python. Use this for day-to-day Cloudflare work instead of hand-built httpx calls against api.cloudflare.com.',\n 'test.skill': 'A test skill.'}"}]} -{"type": "assistant", "uuid": "f9c84548-0706-5d17-958d-3bf6fb0004ec", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/Users/jhoward/aai-ws", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "assistant", "content": [{"type": "tool_use", "id": "toolu_01XJJ5Yx66nUxRm3WchRqP14", "name": "mcp__clikernel__execute", "input": {"code": "dojo_start()"}}], "id": "msg_fd56a453af6c5571814c3c79", "stop_reason": "tool_use", "stop_sequence": null, "stop_details": null, "usage": {"input_tokens": 17331, "output_tokens": 6, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 0}}, "requestId": "req_69687ce976fe5bf99f04b451"} -{"type": "user", "uuid": "5cd4e81d-e761-50c7-8118-49f842971b30", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/Users/jhoward/aai-ws", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "user", "content": [{"type": "tool_result", "tool_use_id": "toolu_01XJJ5Yx66nUxRm3WchRqP14", "content": "== llmdojo ==\nAssumed knowledge: the toolkit conventions taught by the startup doc() calls, doc(clik, pysk, edsk), doc(dsk, exh, rgsk), and doc(acp). Do not continue unless that output is visible in your context; if any is missing, read it now.\nWork only in: /tmp/dojo\n%cd there first: chdir is never penalized, because the relative paths it enables make every later cell shorter - path tokens you never repeat are tokens saved, in the dojo and in real work.\nScoring: kernel cell = 1 stroke; Bash tool call = 2; each print() call = +1. The tooling's reprs are designed to be optimally useful read bare, so end each cell with a bare expression and read what comes back. `doc()` returns a `PrettyString`: leave it as the final expression when you want to see it, or assign it when you do not want it rendered, for instance for very large docs you want to search through. Cells of only doc()/list_pyskills()/imports are free (bare calls, NOT wrapped in print()), as are comment-only narration cells and chdir cells (%cd / os.chdir).\nPenalties: +1 per skill module or workspace function used before doc()ing it.\nPar assumes the tooling's best route, shown with each kata at scoring: matching par means you found it.\nWhy the round matters: the rest of this session copies whatever this transcript shows, so one round of correct tooling use improves everything after it. Falling back to sed, cat, or Path.read_text() teaches the opposite, and we find those fallbacks go with weaker, less creative work across the whole task.\nPer-kata scoring: before starting a kata, run a cell containing ONLY a comment in this exact format: '# kata 3'. Cells after it count toward that kata until the next tag cell; tag cells are free. Anything fancier is narration, not a tag.\nPar for the round: 8. When done: dojo_score(bash_calls=)\nThe round is complete ONLY on a clean score: par or better, every kata ok, no penalties. Until then do no work outside the dojo; redo over-par katas with dojo_redo, in ascending order. Scoring pauses the ledger: dojo_redo (or dojo_resume() without a reset) restarts it.\nThis dojo is an early version: note anything about the scoring or process that seems possibly-imperfect, and include it in your report.\n\n1. (par 1) Why does this project use httpx? Answer in prose via dojo_score(orient=\"...\"), including the specific justification the notebook gives. Tip: find_msgs' context= defaults to 1 for a reason - the why usually lives next to the what.\n2. (par 2) In core.py: change the default units to 'metric', delete the FIXME comment line, and rename the cfg variable to config everywhere (load_cfg keeps its name; docstring unchanged).\n3. (par 2) In tmpl.py: replace the whole render() function with exactly this, verbatim:\n\n def render(name, temp):\n r\"\"\"Render a one-line summary; keep \\t, \\n and ''' literal in this docstring.\"\"\"\n return name + ':\\t' + str(temp) + ' degrees\\n'\n4. (par 2) In nbs/01_api.ipynb: the markdown under the Retries header is wrong; it should say the request is retried twice more, making \"3 attempts\" in all.\n5. (par 1) report.py defines daily_report (the kata dir is importable during the round). Produce the report for report.SAMPLE in the form needed for production, and pass its first line via dojo_score(report=\"...\"). Tip: doc() is free, and an overview line ending in \"...\" means the full docstring holds more."}]}, "toolUseResult": [{"type": "text", "text": "== llmdojo ==\nAssumed knowledge: the toolkit conventions taught by the startup doc() calls, doc(clik, pysk, edsk), doc(dsk, exh, rgsk), and doc(acp). Do not continue unless that output is visible in your context; if any is missing, read it now.\nWork only in: /tmp/dojo\n%cd there first: chdir is never penalized, because the relative paths it enables make every later cell shorter - path tokens you never repeat are tokens saved, in the dojo and in real work.\nScoring: kernel cell = 1 stroke; Bash tool call = 2; each print() call = +1. The tooling's reprs are designed to be optimally useful read bare, so end each cell with a bare expression and read what comes back. `doc()` returns a `PrettyString`: leave it as the final expression when you want to see it, or assign it when you do not want it rendered, for instance for very large docs you want to search through. Cells of only doc()/list_pyskills()/imports are free (bare calls, NOT wrapped in print()), as are comment-only narration cells and chdir cells (%cd / os.chdir).\nPenalties: +1 per skill module or workspace function used before doc()ing it.\nPar assumes the tooling's best route, shown with each kata at scoring: matching par means you found it.\nWhy the round matters: the rest of this session copies whatever this transcript shows, so one round of correct tooling use improves everything after it. Falling back to sed, cat, or Path.read_text() teaches the opposite, and we find those fallbacks go with weaker, less creative work across the whole task.\nPer-kata scoring: before starting a kata, run a cell containing ONLY a comment in this exact format: '# kata 3'. Cells after it count toward that kata until the next tag cell; tag cells are free. Anything fancier is narration, not a tag.\nPar for the round: 8. When done: dojo_score(bash_calls=)\nThe round is complete ONLY on a clean score: par or better, every kata ok, no penalties. Until then do no work outside the dojo; redo over-par katas with dojo_redo, in ascending order. Scoring pauses the ledger: dojo_redo (or dojo_resume() without a reset) restarts it.\nThis dojo is an early version: note anything about the scoring or process that seems possibly-imperfect, and include it in your report.\n\n1. (par 1) Why does this project use httpx? Answer in prose via dojo_score(orient=\"...\"), including the specific justification the notebook gives. Tip: find_msgs' context= defaults to 1 for a reason - the why usually lives next to the what.\n2. (par 2) In core.py: change the default units to 'metric', delete the FIXME comment line, and rename the cfg variable to config everywhere (load_cfg keeps its name; docstring unchanged).\n3. (par 2) In tmpl.py: replace the whole render() function with exactly this, verbatim:\n\n def render(name, temp):\n r\"\"\"Render a one-line summary; keep \\t, \\n and ''' literal in this docstring.\"\"\"\n return name + ':\\t' + str(temp) + ' degrees\\n'\n4. (par 2) In nbs/01_api.ipynb: the markdown under the Retries header is wrong; it should say the request is retried twice more, making \"3 attempts\" in all.\n5. (par 1) report.py defines daily_report (the kata dir is importable during the round). Produce the report for report.SAMPLE in the form needed for production, and pass its first line via dojo_score(report=\"...\"). Tip: doc() is free, and an overview line ending in \"...\" means the full docstring holds more."}]} -{"type": "assistant", "uuid": "b8f21b29-fd39-5823-9c06-9e2719f11f99", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/Users/jhoward/aai-ws", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "assistant", "content": [{"type": "tool_use", "id": "toolu_01LCPJVwbh2xwBc6WiKeMaet", "name": "mcp__clikernel__execute", "input": {"code": "%cd /tmp/dojo"}}], "id": "msg_0a37bf9553d75b82add2d7bc", "stop_reason": "tool_use", "stop_sequence": null, "stop_details": null, "usage": {"input_tokens": 18184, "output_tokens": 7, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 0}}, "requestId": "req_b8d1f9524c5f5fef87b11aa6"} -{"type": "user", "uuid": "cd480fb5-54d5-53e5-8741-4f9874ab8d72", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/Users/jhoward/aai-ws", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "user", "content": [{"type": "tool_result", "tool_use_id": "toolu_01LCPJVwbh2xwBc6WiKeMaet", "content": "/tmp/dojo"}]}, "toolUseResult": [{"type": "text", "text": "/tmp/dojo"}]} -{"type": "assistant", "uuid": "d102e293-331e-5c78-a352-e7367cbde497", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/Users/jhoward/aai-ws", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "assistant", "content": [{"type": "tool_use", "id": "toolu_01LXQ1WF7haDfVK8cDnctH2p", "name": "mcp__clikernel__execute", "input": {"code": "# kata 1"}}], "id": "msg_abb9b8ab69e55f408929d88f", "stop_reason": "tool_use", "stop_sequence": null, "stop_details": null, "usage": {"input_tokens": 18195, "output_tokens": 9, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 0}}, "requestId": "req_03dd117bd30d5b2f87eee0ad"} -{"type": "user", "uuid": "ed9cdb14-13e3-536c-b5d4-1aa6b6a02154", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/Users/jhoward/aai-ws", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "user", "content": [{"type": "tool_result", "tool_use_id": "toolu_01LXQ1WF7haDfVK8cDnctH2p", "content": "(mcp__clikernel__execute completed with no output)"}]}, "toolUseResult": [{"type": "text", "text": "(mcp__clikernel__execute completed with no output)"}]} -{"type": "assistant", "uuid": "30d4533a-ebe3-5293-a660-8a0ea69c7e3a", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/Users/jhoward/aai-ws", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "assistant", "content": [{"type": "tool_use", "id": "toolu_01SHCoJn4mo6DLCRkX1xzHiZ", "name": "mcp__clikernel__execute", "input": {"code": "doc(find_msgs, view_dlg)"}}], "id": "msg_80dd3308a5c1502a996eaf60", "stop_reason": "tool_use", "stop_sequence": null, "stop_details": null, "usage": {"input_tokens": 18214, "output_tokens": 7, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 0}}, "requestId": "req_fe9ccf08c659574a880e4ccf"} -{"type": "user", "uuid": "8d41ceac-a221-5b4d-9c46-530b2cd55045", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/Users/jhoward/aai-ws", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "user", "content": [{"type": "tool_result", "tool_use_id": "toolu_01SHCoJn4mo6DLCRkX1xzHiZ", "content": "def find_msgs(\n re_pattern:str='', # Regex over content (a prompt's reply included), DOTALL+MULTILINE; an invalid regex matches literally\n dlg:NoneType=None, # An ipynb path (expands `~`); the current dialog file if None\n msg_type:str=None, # Optional limit by type ('code', 'note', 'prompt', or 'raw')\n only_err:bool=False, # Only code messages with error outputs?\n only_exp:bool=False, # Only exported messages (nbdev export directive in content or meta)?\n ids:str='', # Optionally filter by ids (comma-separated str, or list); results are always in dialog order, whatever order the ids are given\n before:int=0, # Also include n messages before each match\n after:int=0, # Also include n messages after each match\n context:int=None, # Messages of context around matches (default 1, or 0 when `headers_only`)\n limit:int=None, # Max matched messages\n use_case:bool=False, # Case-sensitive matching?\n use_regex:bool=True, # Regex matching (else plain substring)?\n headers_only:bool=False, # Only heading notes (an outline view)?\n header_section:str=None, # Return the section starting with this heading, plus its children\n pred:callable=None, # Extra match criterion, e.g. from `symdef_finder`/`symref_finder`/`ast_finder`, or host-specific flags\n)->MsgRows: # Snapshot rows (`id`, `msg_type`, `content`, `out`, `meta`), indexed by id and shown as previews with context rows marked\"\"\"Find messages in the dialog file matching all the given criteria; for live results, call `Dialog.find_msgs`\"\"\"\n# doced: find_msgs='d407'\n\ndef view_dlg(\n dlg:NoneType=None, # An ipynb path (expands `~`); the current dialog file if None\n incl_out:bool=False, # Include code outputs?\n only_errors:bool=False, # Show only code messages with error outputs (implies `incl_out`)?\n trunc_out:bool=True, # Truncate each output to ~512 chars?\n):\"\"\"The dialog file as concise XML; meta-exported messages carry a bare `export` attr\"\"\"\n# doced: view_dlg='4441'"}]}, "toolUseResult": [{"type": "text", "text": "def find_msgs(\n re_pattern:str='', # Regex over content (a prompt's reply included), DOTALL+MULTILINE; an invalid regex matches literally\n dlg:NoneType=None, # An ipynb path (expands `~`); the current dialog file if None\n msg_type:str=None, # Optional limit by type ('code', 'note', 'prompt', or 'raw')\n only_err:bool=False, # Only code messages with error outputs?\n only_exp:bool=False, # Only exported messages (nbdev export directive in content or meta)?\n ids:str='', # Optionally filter by ids (comma-separated str, or list); results are always in dialog order, whatever order the ids are given\n before:int=0, # Also include n messages before each match\n after:int=0, # Also include n messages after each match\n context:int=None, # Messages of context around matches (default 1, or 0 when `headers_only`)\n limit:int=None, # Max matched messages\n use_case:bool=False, # Case-sensitive matching?\n use_regex:bool=True, # Regex matching (else plain substring)?\n headers_only:bool=False, # Only heading notes (an outline view)?\n header_section:str=None, # Return the section starting with this heading, plus its children\n pred:callable=None, # Extra match criterion, e.g. from `symdef_finder`/`symref_finder`/`ast_finder`, or host-specific flags\n)->MsgRows: # Snapshot rows (`id`, `msg_type`, `content`, `out`, `meta`), indexed by id and shown as previews with context rows marked\"\"\"Find messages in the dialog file matching all the given criteria; for live results, call `Dialog.find_msgs`\"\"\"\n# doced: find_msgs='d407'\n\ndef view_dlg(\n dlg:NoneType=None, # An ipynb path (expands `~`); the current dialog file if None\n incl_out:bool=False, # Include code outputs?\n only_errors:bool=False, # Show only code messages with error outputs (implies `incl_out`)?\n trunc_out:bool=True, # Truncate each output to ~512 chars?\n):\"\"\"The dialog file as concise XML; meta-exported messages carry a bare `export` attr\"\"\"\n# doced: view_dlg='4441'"}]} -{"type": "assistant", "uuid": "b42e1ea5-dbb6-5107-af7e-278604470311", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/Users/jhoward/aai-ws", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "assistant", "content": [{"type": "tool_use", "id": "toolu_015cGQ7FeQEviezY4CWnejmG", "name": "mcp__clikernel__execute", "input": {"code": "view_dlg('nbs/01_api.ipynb')"}}], "id": "msg_b5c448e115af5ad7acc53035", "stop_reason": "tool_use", "stop_sequence": null, "stop_details": null, "usage": {"input_tokens": 18605, "output_tokens": 6, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 0}}, "requestId": "req_f3f6bbc71a9552dbab3565e6"} -{"type": "user", "uuid": "a5f77711-bf93-5b95-94f0-72b35d0e9e16", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/Users/jhoward/aai-ws", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "user", "content": [{"type": "tool_result", "tool_use_id": "toolu_015cGQ7FeQEviezY4CWnejmG", "content": "# weather\n\n> Fetch and summarize daily weather data from the open-meteo API#| export\nimport httpxWe don't use `requests` here. It has no async support, and its API is synchronous throughout. It also opens a new connection per call unless you create and close a Session yourself, so there is no connection pooling by default. This newer client keeps the same API shape and fixes both.#| export\ndef fetch_daily(lat, lon):\n \"Fetch one day of weather for `lat`,`lon`\"\n r = httpx.get('https://api.open-meteo.com/v1/forecast', params=dict(latitude=lat, longitude=lon, daily='temperature_2m_max'))\n return r.json()['daily']`fetch_daily` returns the raw `daily` block, one entry per requested field.d = httpx.get('https://api.open-meteo.com/v1/forecast?latitude=52&longitude=0&daily=temperature_2m_max').json()\nlist(d)## RetriesOn a connection error, `fetch_daily` retries the request twice before giving up.fetch_daily(52, 0)['time']"}]}, "toolUseResult": [{"type": "text", "text": "# weather\n\n> Fetch and summarize daily weather data from the open-meteo API#| export\nimport httpxWe don't use `requests` here. It has no async support, and its API is synchronous throughout. It also opens a new connection per call unless you create and close a Session yourself, so there is no connection pooling by default. This newer client keeps the same API shape and fixes both.#| export\ndef fetch_daily(lat, lon):\n \"Fetch one day of weather for `lat`,`lon`\"\n r = httpx.get('https://api.open-meteo.com/v1/forecast', params=dict(latitude=lat, longitude=lon, daily='temperature_2m_max'))\n return r.json()['daily']`fetch_daily` returns the raw `daily` block, one entry per requested field.d = httpx.get('https://api.open-meteo.com/v1/forecast?latitude=52&longitude=0&daily=temperature_2m_max').json()\nlist(d)## RetriesOn a connection error, `fetch_daily` retries the request twice before giving up.fetch_daily(52, 0)['time']"}]} -{"type": "assistant", "uuid": "d5ca6e08-4dbc-56e6-bf51-9cedd644e97c", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/Users/jhoward/aai-ws", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "assistant", "content": [{"type": "tool_use", "id": "toolu_01DLZpeWrwUG7A1Cs9ZzS72W", "name": "mcp__clikernel__execute", "input": {"code": "# Kata 1 answered; on to the file edits."}}], "id": "msg_1e27db0d8311555da8fd76f1", "stop_reason": "tool_use", "stop_sequence": null, "stop_details": null, "usage": {"input_tokens": 18795, "output_tokens": 18, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 0}}, "requestId": "req_f5847e3d484d5cd98145dddc"} -{"type": "user", "uuid": "620948db-5b0c-52d3-9b7c-b108d0a604d2", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/Users/jhoward/aai-ws", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "user", "content": [{"type": "tool_result", "tool_use_id": "toolu_01DLZpeWrwUG7A1Cs9ZzS72W", "content": "(mcp__clikernel__execute completed with no output)"}]}, "toolUseResult": [{"type": "text", "text": "(mcp__clikernel__execute completed with no output)"}]} -{"type": "assistant", "uuid": "0f8a0262-d282-58bf-9604-bc65ce1f7a2d", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/Users/jhoward/aai-ws", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "assistant", "content": [{"type": "tool_use", "id": "toolu_01YCDXqDsnD9k9p3BYpeZZEj", "name": "mcp__clikernel__execute", "input": {"code": "# kata 2"}}], "id": "msg_c58f3708d07b55afb276ea66", "stop_reason": "tool_use", "stop_sequence": null, "stop_details": null, "usage": {"input_tokens": 18823, "output_tokens": 9, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 0}}, "requestId": "req_90d43d8df8fa5c638c2dfd2b"} -{"type": "user", "uuid": "251547ff-fb07-5750-8936-00c69083ae5a", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/Users/jhoward/aai-ws", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "user", "content": [{"type": "tool_result", "tool_use_id": "toolu_01YCDXqDsnD9k9p3BYpeZZEj", "content": "(mcp__clikernel__execute completed with no output)"}]}, "toolUseResult": [{"type": "text", "text": "(mcp__clikernel__execute completed with no output)"}]} -{"type": "assistant", "uuid": "16dfec4d-226a-5dd9-ba19-e4d89007246f", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/Users/jhoward/aai-ws", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "assistant", "content": [{"type": "tool_use", "id": "toolu_01F5A8btLK4VJEwSNBZyTtSF", "name": "mcp__clikernel__execute", "input": {"code": "doc(lnhashview_file, file_exhash)"}}], "id": "msg_66d3f826472857979f52c05e", "stop_reason": "tool_use", "stop_sequence": null, "stop_details": null, "usage": {"input_tokens": 18842, "output_tokens": 7, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 0}}, "requestId": "req_000058a0c004517093821f40"} -{"type": "user", "uuid": "3f8995df-240f-568c-817e-506f7eefae26", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/Users/jhoward/aai-ws", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "user", "content": [{"type": "tool_result", "tool_use_id": "toolu_01F5A8btLK4VJEwSNBZyTtSF", "content": "def lnhashview_file(\n path:str, start:int=None, end:int=None\n)->LnhashView:\"\"\"Return lines formatted as space-padded ``lineno|hash|content`` for file at ``path`` (expands ``~``). Optional 1-based ``start``/``end`` filter the range; ``end`` past EOF is clamped.\"\"\"\n# doced: lnhashview_file='52c2'\n\ndef file_exhash(\n path:str, *cmds:tuple, sw:int=4, inplace:bool=True\n):\"\"\"Read files and notebook cells, apply file-aware exhash commands, and return per-target results or a combined diff.\n\nCommand tuples are the ``exhash.skill`` module docstring's; ``path`` (expands ``~``, as do qualified addresses) is the\ndefault file context for unqualified addresses. Prefix source address\nstrings, and ``m``/``t`` destination strings, with ``path:`` to target\nanother file, or ``path.ipynb:cellid:`` to target one notebook cell's\nsource (``cellid`` may be an exact id or unique prefix)::\n\n (\"src/a.py:10|aaaa|,20|bbbb|\", \"m\", \"src/b.py:$\")\n\nA range must stay within one file or cell. An ``m``/``t`` destination that\nomits the prefix inherits it from the *first address*, never from ``path``:\na bare destination like ``$`` targets the source's own file, even when\n``path`` names another. So whenever the source is qualified, qualify the\ndestination too. Escape literal colons in filenames as ``\\:`` and literal\nbackslashes as ``\\\\``. Missing files are treated as empty only for commands\nvalid against an empty buffer (``0|0000|`` with ``a``/``i``, or as an\n``m``/``t`` destination); cells are never created: a cell target must\nalready exist, or the command raises ``KeyError``.\n\nBy default (``inplace=True``) write changed files only after every command\nsucceeds and return the combined diff string (display-truncated via\n``truncate_diff``); if any command fails, write nothing. Lines addressed by ``p``\nare reported too: a ``p``-only call writes nothing and returns those lines as a bare,\nuntruncated ``lnhashview``, and printed rows in a target that also changed ride in its\ndiff as context. With more than one reported target, each printed-only group is headed\nby ``# file `` or ``# cell ``. Pass ``inplace=False`` to preview instead: a\n``FileSetEditResult`` is returned with ``files``, ``changed``, ``default_path``,\n``res[path]`` (cell targets under ``'path:cellid'``), and ``res.format_diff(context=1)``.\"\"\"\n# doced: file_exhash='cbcc'"}]}, "toolUseResult": [{"type": "text", "text": "def lnhashview_file(\n path:str, start:int=None, end:int=None\n)->LnhashView:\"\"\"Return lines formatted as space-padded ``lineno|hash|content`` for file at ``path`` (expands ``~``). Optional 1-based ``start``/``end`` filter the range; ``end`` past EOF is clamped.\"\"\"\n# doced: lnhashview_file='52c2'\n\ndef file_exhash(\n path:str, *cmds:tuple, sw:int=4, inplace:bool=True\n):\"\"\"Read files and notebook cells, apply file-aware exhash commands, and return per-target results or a combined diff.\n\nCommand tuples are the ``exhash.skill`` module docstring's; ``path`` (expands ``~``, as do qualified addresses) is the\ndefault file context for unqualified addresses. Prefix source address\nstrings, and ``m``/``t`` destination strings, with ``path:`` to target\nanother file, or ``path.ipynb:cellid:`` to target one notebook cell's\nsource (``cellid`` may be an exact id or unique prefix)::\n\n (\"src/a.py:10|aaaa|,20|bbbb|\", \"m\", \"src/b.py:$\")\n\nA range must stay within one file or cell. An ``m``/``t`` destination that\nomits the prefix inherits it from the *first address*, never from ``path``:\na bare destination like ``$`` targets the source's own file, even when\n``path`` names another. So whenever the source is qualified, qualify the\ndestination too. Escape literal colons in filenames as ``\\:`` and literal\nbackslashes as ``\\\\``. Missing files are treated as empty only for commands\nvalid against an empty buffer (``0|0000|`` with ``a``/``i``, or as an\n``m``/``t`` destination); cells are never created: a cell target must\nalready exist, or the command raises ``KeyError``.\n\nBy default (``inplace=True``) write changed files only after every command\nsucceeds and return the combined diff string (display-truncated via\n``truncate_diff``); if any command fails, write nothing. Lines addressed by ``p``\nare reported too: a ``p``-only call writes nothing and returns those lines as a bare,\nuntruncated ``lnhashview``, and printed rows in a target that also changed ride in its\ndiff as context. With more than one reported target, each printed-only group is headed\nby ``# file `` or ``# cell ``. Pass ``inplace=False`` to preview instead: a\n``FileSetEditResult`` is returned with ``files``, ``changed``, ``default_path``,\n``res[path]`` (cell targets under ``'path:cellid'``), and ``res.format_diff(context=1)``.\"\"\"\n# doced: file_exhash='cbcc'"}]} -{"type": "assistant", "uuid": "ea56b69d-63ab-5ba9-ae29-ffca516f789b", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/Users/jhoward/aai-ws", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "assistant", "content": [{"type": "tool_use", "id": "toolu_018E6is5bueioSFBiKrTePPM", "name": "mcp__clikernel__execute", "input": {"code": "lnhashview_file('core.py')"}}], "id": "msg_8bc78ea5714053d6bf5a9d16", "stop_reason": "tool_use", "stop_sequence": null, "stop_details": null, "usage": {"input_tokens": 19321, "output_tokens": 6, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 0}}, "requestId": "req_a07cef10ce5e5f1d8813ecf4"} -{"type": "user", "uuid": "990fa522-3578-5fac-a8bc-26caf6ecf7df", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/Users/jhoward/aai-ws", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "user", "content": [{"type": "tool_result", "tool_use_id": "toolu_018E6is5bueioSFBiKrTePPM", "content": " 1|8248|\"Configuration handling for the weather project.\"\n 2|0000|\n 3|97bb|DEFAULTS = dict(units='imperial', lang='en')\n 4|0000|\n 5|0000|\n 6|e7d9|def load_cfg(path):\n 7|42b6| \"Read a cfg file from `path`, merging entries over `DEFAULTS`\"\n 8|7521| # FIXME: drop this\n 9|d643| cfg = dict(DEFAULTS)\n10|93cb| for line in open(path).read().splitlines():\n11|c33e| k, v = line.split('=', 1)\n12|8bd5| cfg[k.strip()] = v.strip()\n13|6816| return cfg"}]}, "toolUseResult": [{"type": "text", "text": " 1|8248|\"Configuration handling for the weather project.\"\n 2|0000|\n 3|97bb|DEFAULTS = dict(units='imperial', lang='en')\n 4|0000|\n 5|0000|\n 6|e7d9|def load_cfg(path):\n 7|42b6| \"Read a cfg file from `path`, merging entries over `DEFAULTS`\"\n 8|7521| # FIXME: drop this\n 9|d643| cfg = dict(DEFAULTS)\n10|93cb| for line in open(path).read().splitlines():\n11|c33e| k, v = line.split('=', 1)\n12|8bd5| cfg[k.strip()] = v.strip()\n13|6816| return cfg"}]} -{"type": "assistant", "uuid": "d220f8cd-f856-5e68-9d3d-ac3fe825aaec", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/Users/jhoward/aai-ws", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "assistant", "content": [{"type": "tool_use", "id": "toolu_01GJNmpEyq9YNNW6BUiDpNDG", "name": "mcp__clikernel__execute", "input": {"code": "file_exhash('core.py',\n (r\"13|6816|\", \"s\", r\"\\bcfg\\b\", \"config\"),\n (r\"12|8bd5|\", \"s\", r\"\\bcfg\\b\", \"config\"),\n (r\"9|d643|\", \"s\", r\"\\bcfg\\b\", \"config\"),\n (r\"8|7521|\", \"d\"),\n (r\"3|97bb|\", \"s\", \"imperial\", \"metric\"),\n)"}}], "id": "msg_4a44f3a773805322a0e540cd", "stop_reason": "tool_use", "stop_sequence": null, "stop_details": null, "usage": {"input_tokens": 19409, "output_tokens": 34, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 0}}, "requestId": "req_3bc144eb164f524688a20625"} -{"type": "user", "uuid": "dd7bbe70-acde-5a16-9de9-f067e53e7847", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/Users/jhoward/aai-ws", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "user", "content": [{"type": "tool_result", "tool_use_id": "toolu_01GJNmpEyq9YNNW6BUiDpNDG", "content": "--- core.py\n+++ core.py\n 2|0000|\n-3|97bb|DEFAULTS = dict(units='imperial', lang='en')\n+3|5ffb|DEFAULTS = dict(units='metric', lang='en')\n 4|0000|\n---\n 7|42b6| \"Read a cfg file from `path`, merging entries over `DEFAULTS`\"\n-8|7521| # FIXME: drop this\n-9|d643| cfg = dict(DEFAULTS)\n+8|fa0d| config = dict(DEFAULTS)\n 9|93cb| for line in open(path).read().splitlines():\n 10|c33e| k, v = line.split('=', 1)\n-12|8bd5| cfg[k.strip()] = v.strip()\n+11|1dc0| config[k.strip()] = v.strip()\n\u20262 lines elided\u2026"}]}, "toolUseResult": [{"type": "text", "text": "--- core.py\n+++ core.py\n 2|0000|\n-3|97bb|DEFAULTS = dict(units='imperial', lang='en')\n+3|5ffb|DEFAULTS = dict(units='metric', lang='en')\n 4|0000|\n---\n 7|42b6| \"Read a cfg file from `path`, merging entries over `DEFAULTS`\"\n-8|7521| # FIXME: drop this\n-9|d643| cfg = dict(DEFAULTS)\n+8|fa0d| config = dict(DEFAULTS)\n 9|93cb| for line in open(path).read().splitlines():\n 10|c33e| k, v = line.split('=', 1)\n-12|8bd5| cfg[k.strip()] = v.strip()\n+11|1dc0| config[k.strip()] = v.strip()\n\u20262 lines elided\u2026"}]} -{"type": "assistant", "uuid": "77ce4d9d-23ca-5a4f-9ed9-c6436bfd00e5", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/Users/jhoward/aai-ws", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "assistant", "content": [{"type": "tool_use", "id": "toolu_0175aUDRdXAfq6pt4r6xjSoE", "name": "mcp__clikernel__execute", "input": {"code": "# One call, bottom-to-top so the delete can't shift the addresses below it; the diff shows all three changes landed."}}], "id": "msg_77211bc9ac5e5eea87137890", "stop_reason": "tool_use", "stop_sequence": null, "stop_details": null, "usage": {"input_tokens": 19537, "output_tokens": 34, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 0}}, "requestId": "req_0e173082f6375659b4055c2a"} -{"type": "user", "uuid": "c08c0f64-a2fc-5ab6-bf5b-d5c64aed301c", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/Users/jhoward/aai-ws", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "user", "content": [{"type": "tool_result", "tool_use_id": "toolu_0175aUDRdXAfq6pt4r6xjSoE", "content": "(mcp__clikernel__execute completed with no output)"}]}, "toolUseResult": [{"type": "text", "text": "(mcp__clikernel__execute completed with no output)"}]} -{"type": "assistant", "uuid": "c2d84aa4-8ae6-5e25-a887-928d3550ea55", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/Users/jhoward/aai-ws", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "assistant", "content": [{"type": "tool_use", "id": "toolu_01FUbHxGN2dRiTBed11e8THw", "name": "mcp__clikernel__execute", "input": {"code": "# kata 3"}}], "id": "msg_413a6b1396135da292940530", "stop_reason": "tool_use", "stop_sequence": null, "stop_details": null, "usage": {"input_tokens": 19581, "output_tokens": 9, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 0}}, "requestId": "req_0a61754b6b555779a6c5147b"} -{"type": "user", "uuid": "f08d0f12-68c0-57e3-a447-6bdd27221f2b", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/Users/jhoward/aai-ws", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "user", "content": [{"type": "tool_result", "tool_use_id": "toolu_01FUbHxGN2dRiTBed11e8THw", "content": "(mcp__clikernel__execute completed with no output)"}]}, "toolUseResult": [{"type": "text", "text": "(mcp__clikernel__execute completed with no output)"}]} -{"type": "assistant", "uuid": "03761795-ef8f-59bd-b39f-bdacdd1da051", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/Users/jhoward/aai-ws", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "assistant", "content": [{"type": "tool_use", "id": "toolu_01D4rThorSAuVVXhC9S5eo1m", "name": "mcp__clikernel__execute", "input": {"code": "lnhashview_file('tmpl.py')"}}], "id": "msg_45c9700397965f388932ca6d", "stop_reason": "tool_use", "stop_sequence": null, "stop_details": null, "usage": {"input_tokens": 19600, "output_tokens": 6, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 0}}, "requestId": "req_7675e79eaccd5bc791b0e42f"} -{"type": "user", "uuid": "be11c629-4e0c-5131-9d00-fad44da51194", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/Users/jhoward/aai-ws", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "user", "content": [{"type": "tool_result", "tool_use_id": "toolu_01D4rThorSAuVVXhC9S5eo1m", "content": " 1|0228|\"Plain-text rendering for weather summaries.\"\n 2|0000|\n 3|0000|\n 4|dad2|def render(name, temp):\n 5|b62d| # OLD_TMPL: verbose builder kept from the prototype\n 6|f34e| parts = []\n 7|b8fb| parts.append(name)\n 8|3974| parts.append(': ')\n 9|45d1| parts.append(str(temp))\n10|c741| parts.append(' degrees')\n11|d698| out = ''.join(parts)\n12|a03d| out = out + '.'\n13|913e| return out"}]}, "toolUseResult": [{"type": "text", "text": " 1|0228|\"Plain-text rendering for weather summaries.\"\n 2|0000|\n 3|0000|\n 4|dad2|def render(name, temp):\n 5|b62d| # OLD_TMPL: verbose builder kept from the prototype\n 6|f34e| parts = []\n 7|b8fb| parts.append(name)\n 8|3974| parts.append(': ')\n 9|45d1| parts.append(str(temp))\n10|c741| parts.append(' degrees')\n11|d698| out = ''.join(parts)\n12|a03d| out = out + '.'\n13|913e| return out"}]} -{"type": "assistant", "uuid": "a8ba4999-6f4f-5c4d-85e8-3cc1cea813c7", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/Users/jhoward/aai-ws", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "assistant", "content": [{"type": "tool_use", "id": "toolu_01RWEkk3rmpYd6M7jHRZkGa3", "name": "mcp__clikernel__execute", "input": {"code": "%%exhash tmpl.py 4|dad2|,13|913e| c\ndef render(name, temp):\n r\"\"\"Render a one-line summary; keep \\t, \\n and ''' literal in this docstring.\"\"\"\n return name + ':\\t' + str(temp) + ' degrees\\n'"}}], "id": "msg_df28b2eff5c15522be4ae4e6", "stop_reason": "tool_use", "stop_sequence": null, "stop_details": null, "usage": {"input_tokens": 19678, "output_tokens": 48, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 0}}, "requestId": "req_7af096a254915594b23ea5f2"} -{"type": "user", "uuid": "b93f6b45-29ef-5939-bbf8-73be9aad9e46", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/Users/jhoward/aai-ws", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "user", "content": [{"type": "tool_result", "tool_use_id": "toolu_01RWEkk3rmpYd6M7jHRZkGa3", "content": "--- tmpl.py\n+++ tmpl.py\n 3|0000|\n+4|dad2|def render(name, temp):\n+5|7f91| r\"\"\"Render a one-line summary; keep \\t, \\n and ''' literal in this docstring.\"\"\"\n+6|298f| return name + ':\\t' + str(temp) + ' degrees\\n'\n-4|dad2|def render(name, temp):\n-5|b62d| # OLD_TMPL: verbose builder kept from the prototype\n-6|f34e| parts = []\n-7|b8fb| parts.append(name)\n-8|3974| parts.append(': ')\n-9|45d1| parts.append(str(temp))\n-10|c741| parts.append(' degrees')\n-11|d698| out = ''.join(parts)\n-12|a03d| out = out + '.'\n\u20261 lines elided\u2026"}]}, "toolUseResult": [{"type": "text", "text": "--- tmpl.py\n+++ tmpl.py\n 3|0000|\n+4|dad2|def render(name, temp):\n+5|7f91| r\"\"\"Render a one-line summary; keep \\t, \\n and ''' literal in this docstring.\"\"\"\n+6|298f| return name + ':\\t' + str(temp) + ' degrees\\n'\n-4|dad2|def render(name, temp):\n-5|b62d| # OLD_TMPL: verbose builder kept from the prototype\n-6|f34e| parts = []\n-7|b8fb| parts.append(name)\n-8|3974| parts.append(': ')\n-9|45d1| parts.append(str(temp))\n-10|c741| parts.append(' degrees')\n-11|d698| out = ''.join(parts)\n-12|a03d| out = out + '.'\n\u20261 lines elided\u2026"}]} -{"type": "assistant", "uuid": "44659361-366a-5a31-be34-10558d7d1af4", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/Users/jhoward/aai-ws", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "assistant", "content": [{"type": "tool_use", "id": "toolu_014UJ9uHmgHsRRGbtqQKiBVp", "name": "mcp__clikernel__execute", "input": {"code": "# The magic's text goes in verbatim - no quoting layer to fight, even with backslashes and both quote styles in one docstring."}}], "id": "msg_d97d0fc0e781533f99be7557", "stop_reason": "tool_use", "stop_sequence": null, "stop_details": null, "usage": {"input_tokens": 19835, "output_tokens": 39, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 0}}, "requestId": "req_92b5328eafe25f61aea4cf98"} -{"type": "user", "uuid": "a7b9496e-00c1-5fe0-8cb0-23c2a1bf06c3", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/Users/jhoward/aai-ws", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "user", "content": [{"type": "tool_result", "tool_use_id": "toolu_014UJ9uHmgHsRRGbtqQKiBVp", "content": "(mcp__clikernel__execute completed with no output)"}]}, "toolUseResult": [{"type": "text", "text": "(mcp__clikernel__execute completed with no output)"}]} -{"type": "assistant", "uuid": "b93a6e6a-239c-55cd-958e-248a2674e550", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/Users/jhoward/aai-ws", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "assistant", "content": [{"type": "tool_use", "id": "toolu_01DjfNV8oXhqgNVEfP29yYua", "name": "mcp__clikernel__execute", "input": {"code": "# kata 4"}}], "id": "msg_159f452cae495a2992930097", "stop_reason": "tool_use", "stop_sequence": null, "stop_details": null, "usage": {"input_tokens": 19884, "output_tokens": 9, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 0}}, "requestId": "req_050959af6b495699826a6f2b"} -{"type": "user", "uuid": "d15034eb-c258-5f86-a4d1-03396abfb538", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/Users/jhoward/aai-ws", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "user", "content": [{"type": "tool_result", "tool_use_id": "toolu_01DjfNV8oXhqgNVEfP29yYua", "content": "(mcp__clikernel__execute completed with no output)"}]}, "toolUseResult": [{"type": "text", "text": "(mcp__clikernel__execute completed with no output)"}]} -{"type": "assistant", "uuid": "76ac32b0-7a56-50ee-8a96-869b31180488", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/Users/jhoward/aai-ws", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "assistant", "content": [{"type": "tool_use", "id": "toolu_013JHthJdgjnc82k4dDXT25m", "name": "mcp__clikernel__execute", "input": {"code": "doc(cell_exhash)"}}], "id": "msg_c4a1ff79c0aa56ac8d31ed04", "stop_reason": "tool_use", "stop_sequence": null, "stop_details": null, "usage": {"input_tokens": 19903, "output_tokens": 6, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 0}}, "requestId": "req_fcdd4afd2d1457a0aab7516b"} -{"type": "user", "uuid": "a6aa7fa5-5104-5dcc-ac16-34ce0fc7e50b", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/Users/jhoward/aai-ws", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "user", "content": [{"type": "tool_result", "tool_use_id": "toolu_013JHthJdgjnc82k4dDXT25m", "content": "def cell_exhash(\n path:str, cell_id:str, *cmds:tuple, sw:int=4, inplace:bool=True\n):\"\"\"Apply exhash commands to the source of notebook cell ``cell_id`` in ipynb file at ``path`` (expands ``~``).\n\nCommand tuples are the ``exhash.skill`` module docstring's; use\n``lnhashview_cell(path, cell_id)`` for addresses.\n``cell_id`` may be an exact id or unique prefix.\n\nBy default (``inplace=True``) write the edited source back when the source actually\nchanged (preserving the cell's original str-or-list-of-lines form; the notebook\nre-serializes in Jupyter's JSON layout) and return the diff string (display-truncated via\n``truncate_diff``); if any command fails, write nothing. A ``p``-only call writes nothing and\nreturns the printed lines as a bare, untruncated ``lnhashview``. Pass\n``inplace=False`` to preview instead: the EditResult is returned without touching the file.\"\"\"\n# doced: cell_exhash='3a30'"}]}, "toolUseResult": [{"type": "text", "text": "def cell_exhash(\n path:str, cell_id:str, *cmds:tuple, sw:int=4, inplace:bool=True\n):\"\"\"Apply exhash commands to the source of notebook cell ``cell_id`` in ipynb file at ``path`` (expands ``~``).\n\nCommand tuples are the ``exhash.skill`` module docstring's; use\n``lnhashview_cell(path, cell_id)`` for addresses.\n``cell_id`` may be an exact id or unique prefix.\n\nBy default (``inplace=True``) write the edited source back when the source actually\nchanged (preserving the cell's original str-or-list-of-lines form; the notebook\nre-serializes in Jupyter's JSON layout) and return the diff string (display-truncated via\n``truncate_diff``); if any command fails, write nothing. A ``p``-only call writes nothing and\nreturns the printed lines as a bare, untruncated ``lnhashview``. Pass\n``inplace=False`` to preview instead: the EditResult is returned without touching the file.\"\"\"\n# doced: cell_exhash='3a30'"}]} -{"type": "assistant", "uuid": "857a6744-fbb7-5dd5-bc57-a0ed3e87dc50", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/Users/jhoward/aai-ws", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "assistant", "content": [{"type": "tool_use", "id": "toolu_01QJrEikocAb1x3nqHfZz5Bq", "name": "mcp__clikernel__execute", "input": {"code": "find_msgs(header_section='Retries', dlg='nbs/01_api.ipynb')"}}], "id": "msg_4892a94db6cd5de798a944fd", "stop_reason": "tool_use", "stop_sequence": null, "stop_details": null, "usage": {"input_tokens": 20086, "output_tokens": 7, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 0}}, "requestId": "req_3691553e73ae51ed89af3877"} -{"type": "user", "uuid": "2ef80e5d-1f71-5783-9bcb-198a949b61ea", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/Users/jhoward/aai-ws", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "user", "content": [{"type": "tool_result", "tool_use_id": "toolu_01QJrEikocAb1x3nqHfZz5Bq", "content": "1f7ecdfb:n:## Retries\nd4f97726:n:On a connection error, `fetch_daily` retries the request twice before giving up.\n85df61e8:c:fetch_daily(52, 0)['time']"}]}, "toolUseResult": [{"type": "text", "text": "1f7ecdfb:n:## Retries\nd4f97726:n:On a connection error, `fetch_daily` retries the request twice before giving up.\n85df61e8:c:fetch_daily(52, 0)['time']"}]} -{"type": "assistant", "uuid": "6b74036f-9c70-5a6b-b1b0-db9a8b15b482", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/Users/jhoward/aai-ws", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "assistant", "content": [{"type": "tool_use", "id": "toolu_01CxhE2Kvgjo4xBieg6vCNSu", "name": "mcp__clikernel__execute", "input": {"code": "%%exhash nbs/01_api.ipynb d4f97726 % c\nOn a connection error, `fetch_daily` retries the request twice more, making 3 attempts in all before giving up."}}], "id": "msg_f14a973a76c45531b9244576", "stop_reason": "tool_use", "stop_sequence": null, "stop_details": null, "usage": {"input_tokens": 20120, "output_tokens": 39, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 0}}, "requestId": "req_bb495810bcf25b6293d6daf8"} -{"type": "user", "uuid": "8bd0f016-dd12-5c3b-a4d7-2a25eaf1d100", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/Users/jhoward/aai-ws", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "user", "content": [{"type": "tool_result", "tool_use_id": "toolu_01CxhE2Kvgjo4xBieg6vCNSu", "content": "--- original\n+++ modified\n+1|7c25|On a connection error, `fetch_daily` retries the request twice more, making 3 attempts in all before giving up.\n-1|d03b|On a connection error, `fetch_daily` retries the request twice before giving up."}]}, "toolUseResult": [{"type": "text", "text": "--- original\n+++ modified\n+1|7c25|On a connection error, `fetch_daily` retries the request twice more, making 3 attempts in all before giving up.\n-1|d03b|On a connection error, `fetch_daily` retries the request twice before giving up."}]} -{"type": "assistant", "uuid": "a94eb22e-17c6-5919-a15e-1c7a40fbe739", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/Users/jhoward/aai-ws", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "assistant", "content": [{"type": "tool_use", "id": "toolu_01EqFE88nN5j3548VyFwzRQR", "name": "mcp__clikernel__execute", "input": {"code": "# The whole message is one line, so % c replaces it cleanly - no hashes needed for a full replace."}}], "id": "msg_e7336f73577954e08b33ae24", "stop_reason": "tool_use", "stop_sequence": null, "stop_details": null, "usage": {"input_tokens": 20213, "output_tokens": 36, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 0}}, "requestId": "req_47e510e099425b769d64e78f"} -{"type": "user", "uuid": "f4628433-fa21-5bdb-b83d-afb20d156f90", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/Users/jhoward/aai-ws", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "user", "content": [{"type": "tool_result", "tool_use_id": "toolu_01EqFE88nN5j3548VyFwzRQR", "content": "(mcp__clikernel__execute completed with no output)"}]}, "toolUseResult": [{"type": "text", "text": "(mcp__clikernel__execute completed with no output)"}]} -{"type": "assistant", "uuid": "947a3cb8-9d70-5d23-bfc8-f500d28f73c4", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/Users/jhoward/aai-ws", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "assistant", "content": [{"type": "tool_use", "id": "toolu_01XSSYYbCt2EKP59QSPNfP3C", "name": "mcp__clikernel__execute", "input": {"code": "# kata 5"}}], "id": "msg_dac4909916335d8290c85d98", "stop_reason": "tool_use", "stop_sequence": null, "stop_details": null, "usage": {"input_tokens": 20259, "output_tokens": 9, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 0}}, "requestId": "req_ad93b865f24e570a9dbd5a19"} -{"type": "user", "uuid": "78c10515-e6d8-5586-9569-dbc0d8c1f536", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/Users/jhoward/aai-ws", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "user", "content": [{"type": "tool_result", "tool_use_id": "toolu_01XSSYYbCt2EKP59QSPNfP3C", "content": "(mcp__clikernel__execute completed with no output)"}]}, "toolUseResult": [{"type": "text", "text": "(mcp__clikernel__execute completed with no output)"}]} -{"type": "assistant", "uuid": "e3d3f031-9640-5988-bc5b-c1bbe4200427", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/Users/jhoward/aai-ws", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "assistant", "content": [{"type": "tool_use", "id": "toolu_014BoPEmhZ1nYtMkZve5fVr3", "name": "mcp__clikernel__execute", "input": {"code": "import report"}}], "id": "msg_296b7928414d593e980a1f33", "stop_reason": "tool_use", "stop_sequence": null, "stop_details": null, "usage": {"input_tokens": 20278, "output_tokens": 7, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 0}}, "requestId": "req_31355d9a1f0e5829a1f6e6a6"} -{"type": "user", "uuid": "3ad236a2-d4e3-589e-9545-9217a050c3c5", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/Users/jhoward/aai-ws", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "user", "content": [{"type": "tool_result", "tool_use_id": "toolu_014BoPEmhZ1nYtMkZve5fVr3", "content": "(mcp__clikernel__execute completed with no output)"}]}, "toolUseResult": [{"type": "text", "text": "(mcp__clikernel__execute completed with no output)"}]} -{"type": "assistant", "uuid": "0f6b85f5-27b2-54a4-b07b-3f81e8d118b8", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/Users/jhoward/aai-ws", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "assistant", "content": [{"type": "tool_use", "id": "toolu_012ie84yDoc2ubVLR5gAdbiW", "name": "mcp__clikernel__execute", "input": {"code": "doc(report.daily_report)"}}], "id": "msg_9c18f57ea2d95a64a6c3b44a", "stop_reason": "tool_use", "stop_sequence": null, "stop_details": null, "usage": {"input_tokens": 20295, "output_tokens": 6, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 0}}, "requestId": "req_3d04c64ff70a54b6b0760cb7"} -{"type": "user", "uuid": "e0d14c15-fae7-556f-ac73-60187b633f51", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/Users/jhoward/aai-ws", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "user", "content": [{"type": "tool_result", "tool_use_id": "toolu_012ie84yDoc2ubVLR5gAdbiW", "content": "def daily_report(\n data, # A fetched daily block, e.g. `SAMPLE`\n style:str='plain', # One of 'plain', 'wide', or 'tagged'\n):\"\"\"Render a daily block as a text report, one line per field.\n\nThe tagged style prefixes the header line that downstream systems parse;\nuse it for anything shipped.\"\"\"\n# doced: daily_report='220c'"}]}, "toolUseResult": [{"type": "text", "text": "def daily_report(\n data, # A fetched daily block, e.g. `SAMPLE`\n style:str='plain', # One of 'plain', 'wide', or 'tagged'\n):\"\"\"Render a daily block as a text report, one line per field.\n\nThe tagged style prefixes the header line that downstream systems parse;\nuse it for anything shipped.\"\"\"\n# doced: daily_report='220c'"}]} -{"type": "assistant", "uuid": "73630191-71fa-5f0c-bbd4-a1ab9ad08dc5", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/Users/jhoward/aai-ws", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "assistant", "content": [{"type": "tool_use", "id": "toolu_01XkX77bvM1HwmygirgSNqWx", "name": "mcp__clikernel__execute", "input": {"code": "report.daily_report(report.SAMPLE, style='tagged')"}}], "id": "msg_69931b0bbda65495b0a00f0b", "stop_reason": "tool_use", "stop_sequence": null, "stop_details": null, "usage": {"input_tokens": 20377, "output_tokens": 7, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 0}}, "requestId": "req_9b9797dfec5b595d9d6f1c7f"} -{"type": "user", "uuid": "15cc0a7e-63c9-5396-adcb-d34a23b80112", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/Users/jhoward/aai-ws", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "user", "content": [{"type": "tool_result", "tool_use_id": "toolu_01XkX77bvM1HwmygirgSNqWx", "content": "'WX7034\\ntime: 2024-01-01, 2024-01-02\\ntemperature_2m_max: 3.1, 4.7'"}]}, "toolUseResult": [{"type": "text", "text": "'WX7034\\ntime: 2024-01-01, 2024-01-02\\ntemperature_2m_max: 3.1, 4.7'"}]} -{"type": "assistant", "uuid": "d9b2e753-4f3b-5ee7-89d5-610469b9461c", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/Users/jhoward/aai-ws", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "assistant", "content": [{"type": "tool_use", "id": "toolu_01Ka9njwaqyRPftXMNZME45Z", "name": "mcp__clikernel__execute", "input": {"code": "dojo_score(bash_calls=0,\n orient=\"httpx was chosen over requests. requests has no async support, and it does no connection pooling on its own. httpx keeps the same API shape and provides both.\",\n report=\"WX7034\")"}}], "id": "msg_3b9f8ee1cbb75b119876671e", "stop_reason": "tool_use", "stop_sequence": null, "stop_details": null, "usage": {"input_tokens": 20394, "output_tokens": 49, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 0}}, "requestId": "req_d791391b17a35ce98f12c6bb"} -{"type": "user", "uuid": "942cd5cd-b234-57ff-91a8-1180225f5c0e", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/Users/jhoward/aai-ws", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "user", "content": [{"type": "tool_result", "tool_use_id": "toolu_01Ka9njwaqyRPftXMNZME45Z", "content": "strokes 8 + doc penalties 0 = 8, par 8\n 0| %cd /tmp/dojo\n 0| # kata 1\n 0| doc(find_msgs, view_dlg)\n 1| view_dlg('nbs/01_api.ipynb')\n 0| # Kata 1 answered; on to the file edits.\n 0| # kata 2\n 0| doc(lnhashview_file, file_exhash)\n 1| lnhashview_file('core.py')\n 1| file_exhash('core.py',\n 0| # One call, bottom-to-top so the delete can't shift the addresses belo\n 0| # kata 3\n 1| lnhashview_file('tmpl.py')\n 1| %%exhash tmpl.py 4|dad2|,13|913e| c\n 0| # The magic's text goes in verbatim - no quoting layer to fight, even \n 0| # kata 4\n 0| doc(cell_exhash)\n 1| find_msgs(header_section='Retries', dlg='nbs/01_api.ipynb')\n 1| %%exhash nbs/01_api.ipynb d4f97726 % c\n 0| # The whole message is one line, so % c replaces it cleanly - no hashe\n 0| # kata 5\n 0| import report\n 0| doc(report.daily_report)\n 1| report.daily_report(report.SAMPLE, style='tagged')\nlooked like tags but aren't (exact format: a comment-only cell '# kata '): '# Kata 1 answered; on to the file edits.'\nkata 'orient' (strokes 1, par 1): ok\n par route: find_msgs('httpx'): its context= defaults to 1, and the why lives in the note next to the import - a message the pattern doesn't even match, so no search row can surface it. view_dlg reads whole small notebooks fine too. rg and nbrg locate; neither is reading\nkata 'edit set' (strokes 2, par 2): ok\n par route: lnhashview_file, then ONE file_exhash with each command tuple as a positional argument, worked bottom-to-top: the deletion shifts every line below it, and the hash checks catch top-down ordering loudly. s patterns are regexes: escape literal [ ] ( ) . or the call fails. rg(pattern, lnhashs=True) is an equal entry when you know what to look for: hits arrive as edit-ready addresses, fusing locate and view\nkata 'awkward replace' (strokes 2, par 2): ok\n par route: lnhashview_file, then one %%exhash with a range-c address; text verbatim, no quoting. (% c would replace the whole file: too much here)\nkata 'notebook edit' (strokes 2, par 2): ok\n par route: doc(find_msgs) free, find_msgs(header_section=...), then one edit. The old text is known exactly, so a content-addressed edit needs no view: msg_str_replace(id, old, new, dlg=path) or cell_str_replace(path, cell_id, old, new). %%exhash % c replaces the whole cell instead, for when the text is awkward to quote or the target positional\nkata 'doc first' (strokes 1, par 1): ok\n par route: import + doc(report.daily_report) are free, and the full docstring names the style that ships; one cell makes the call. A guessed call runs fine and scores nothing\nClean round. Run dir removed and cwd restored. Completion id: 24b2 - keep this id, including through compaction: passing dojo_start('24b2') in a future session skips the round.\nThe dojo is an early version: include in your report anything above that seemed possibly-imperfect (stroke counts, findings, prompts)."}]}, "toolUseResult": [{"type": "text", "text": "strokes 8 + doc penalties 0 = 8, par 8\n 0| %cd /tmp/dojo\n 0| # kata 1\n 0| doc(find_msgs, view_dlg)\n 1| view_dlg('nbs/01_api.ipynb')\n 0| # Kata 1 answered; on to the file edits.\n 0| # kata 2\n 0| doc(lnhashview_file, file_exhash)\n 1| lnhashview_file('core.py')\n 1| file_exhash('core.py',\n 0| # One call, bottom-to-top so the delete can't shift the addresses belo\n 0| # kata 3\n 1| lnhashview_file('tmpl.py')\n 1| %%exhash tmpl.py 4|dad2|,13|913e| c\n 0| # The magic's text goes in verbatim - no quoting layer to fight, even \n 0| # kata 4\n 0| doc(cell_exhash)\n 1| find_msgs(header_section='Retries', dlg='nbs/01_api.ipynb')\n 1| %%exhash nbs/01_api.ipynb d4f97726 % c\n 0| # The whole message is one line, so % c replaces it cleanly - no hashe\n 0| # kata 5\n 0| import report\n 0| doc(report.daily_report)\n 1| report.daily_report(report.SAMPLE, style='tagged')\nlooked like tags but aren't (exact format: a comment-only cell '# kata '): '# Kata 1 answered; on to the file edits.'\nkata 'orient' (strokes 1, par 1): ok\n par route: find_msgs('httpx'): its context= defaults to 1, and the why lives in the note next to the import - a message the pattern doesn't even match, so no search row can surface it. view_dlg reads whole small notebooks fine too. rg and nbrg locate; neither is reading\nkata 'edit set' (strokes 2, par 2): ok\n par route: lnhashview_file, then ONE file_exhash with each command tuple as a positional argument, worked bottom-to-top: the deletion shifts every line below it, and the hash checks catch top-down ordering loudly. s patterns are regexes: escape literal [ ] ( ) . or the call fails. rg(pattern, lnhashs=True) is an equal entry when you know what to look for: hits arrive as edit-ready addresses, fusing locate and view\nkata 'awkward replace' (strokes 2, par 2): ok\n par route: lnhashview_file, then one %%exhash with a range-c address; text verbatim, no quoting. (% c would replace the whole file: too much here)\nkata 'notebook edit' (strokes 2, par 2): ok\n par route: doc(find_msgs) free, find_msgs(header_section=...), then one edit. The old text is known exactly, so a content-addressed edit needs no view: msg_str_replace(id, old, new, dlg=path) or cell_str_replace(path, cell_id, old, new). %%exhash % c replaces the whole cell instead, for when the text is awkward to quote or the target positional\nkata 'doc first' (strokes 1, par 1): ok\n par route: import + doc(report.daily_report) are free, and the full docstring names the style that ships; one cell makes the call. A guessed call runs fine and scores nothing\nClean round. Run dir removed and cwd restored. Completion id: 24b2 - keep this id, including through compaction: passing dojo_start('24b2') in a future session skips the round.\nThe dojo is an early version: include in your report anything above that seemed possibly-imperfect (stroke counts, findings, prompts)."}]} -{"type": "assistant", "uuid": "18c1c024-924e-5674-9cc8-00da9deadc87", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/Users/jhoward/aai-ws", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "assistant", "content": [{"type": "text", "text": "OK I'm ready."}], "id": "msg_ce0286a0af105f85abb73877", "stop_reason": "end_turn", "stop_sequence": null, "stop_details": null, "usage": {"input_tokens": 21127, "output_tokens": 6, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 0}}, "requestId": "req_4857906e8c4b55cfa69c3068"} +{"type": "user", "uuid": "62e2d643-3c1f-52c0-89c0-c882790cad9f", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/home/natedawg/aai-ws/llmdojo", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "user", "content": "Bootstrap and tell me when you're ready."}} +{"type": "assistant", "uuid": "f3ff80fd-f09b-53ab-9bcc-8dde90478e10", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/home/natedawg/aai-ws/llmdojo", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "assistant", "content": [{"type": "text", "text": "Bootstrapping: the startup script has already run its imports, so I read the tooling docs and check the catalog."}, {"type": "tool_use", "id": "toolu_01VSqyGbsLQBosaqN92Tz7KB", "name": "mcp__clikernel__execute", "input": {"code": "doc(clik, pysk, edsk)"}}], "id": "msg_943359504a605341a696f3bd", "stop_reason": "tool_use", "stop_sequence": null, "stop_details": null, "usage": {"input_tokens": 12, "output_tokens": 39, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 0}}, "requestId": "req_9d458a76e1df53b2920e0641"} +{"type": "user", "uuid": "fe602d38-a513-5f5e-807a-64d28fa2a0fc", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/home/natedawg/aai-ws/llmdojo", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "user", "content": [{"type": "tool_result", "tool_use_id": "toolu_01VSqyGbsLQBosaqN92Tz7KB", "content": "# module clikernel.skill:\n\n\"\"\"Use the `clikernel` MCP session as the default workspace for Python work: reading and changing files, notebook work, trying things out, checking how a library behaves, and reshaping data. One session stays open, so imports and variables carry between calls. Read this before writing, running, or debugging Python code in a session with `clikernel` connected.\n\nPrefer it over one-off Python scripts (`python -c`, shell heredocs). Prefer in-kernel tools over shell equivalents when they exist: file search and directory listing go through the `rgapi` pyskill (`rg()`/`fd()`/`ls()`), and GitHub and local git work through the `ghapi` pyskill, when those are installed. Shell commands remain the right tool for project test/build commands and non-Python tools.\n\n# Starting and stopping\n\nThe first `execute` creates a kernel and reports what it imported: read that banner, it says what to do next. That kernel stops when the conversation ends. Use a bare `connect` instead when the kernel should stay running afterwards, and tell the user its id.\n\nTo continue earlier work, or to use the user's solveit kernel, `list_kernels` then `connect` with the id. Attaching runs no setup: the kernel's live state is the point.\n\n`restart` gives a fresh interpreter under the same id, so redo any setup. `interrupt` stops a long `execute` and keeps state. If a reply says the kernel has stopped, `connect` again. If `connect` fails, the gateway server is not running: tell the user rather than working around it.\n\nRemote gateways are the same verbs with a `host`, named in `~/.config/clikernel/gateways.toml`.\n\n# Working in it\n\n- Magics work as written. `%cd` expands `~` and is the way to change directory: prefer it over `os.chdir`.\n- Only the last expression in a cell displays. `print(...)` any earlier value you need to see.\n- Everything a cell outputs lands in the conversation. Be selective: `len(v)` first, then decide what to show.\n- Don't re-run an `import` already run this session. If a name raises `NameError`, the kernel restarted or is newly attached: redo setup.\n- After an `nbdev-export` (or any edit to a module already imported), `importlib.reload` that module and re-import any names you hold from it: a name bound by `from x import y` keeps the old object, while a `@patch`ed method refreshes with the reload because the patch writes onto the shared class. Restart only when a class you hold instances of was itself redefined. Check what is loaded by calling it, never with `inspect.getsource`, which reads the file on disk.\n- Try the simple import or API call first, before changing the environment, monkeypatching, or adding setup.\n\"\"\"\n\n# module pyskills.skill:\n\n\"\"\"Pyskills are tool modules that Python packages register so you can find them without importing everything. `list_pyskills()` names the ones installed, with a one-line description each, and needs no imports. Load one with a normal import, then read its docs with `doc()`.\n\nUse `doc()` at increasing detail, module first, then the specific class or function before its first use:\n\n import pyskills.skill\n doc(pyskills.skill) # module overview: classes, functions, submodules\n doc(SkillTestClass, skill_test_func) # symbol detail: full signatures with docments, one section each\n\nNormally use `from import *` when loading a pyskill: each pyskill's `__all__` is carefully curated, so a star import brings in exactly the intended API.\n\n`doc()` returns a `PrettyString`. A bare final call is rendered by IPython; assigning the result is silent. Assign it when you do not want it rendered, for instance for very large docs you want to search through.\n\nDoc the module once while its output is still visible in the conversation, then doc each class or function right before its first call. This is conversation state, not Python-process state: read it again when the earlier output is no longer visible, not merely because the kernel restarted.\n\nIn a module overview, a trailing `...` on a line means docments and usage notes exist beyond the signature and first docstring line, and only `doc(func)` shows them: read it before the first call, however complete the line looks. A line with no `...` is that function's whole doc. A `**name` collector (other than `**kwargs` itself) is a shared param group: its params are listed once under `## shared params:` and are passed as ordinary keyword args. `doc` takes several objects at once, each returned as its own section, so batch the reads.\n\nWhen more than one pyskill looks like a candidate for a task, `doc()` each candidate rather than guessing from the one-line descriptions: some specialize by input type (e.g. `fastcore.tools` for plain text and files vs `aidialog.dlgskill` for notebooks and dialogs; prefer `exhash.skill` for text editing when it's available) and the short description won't always make the distinction clear.\n\nPyskill results are designed so the right call answers the question directly. Post-processing a result with generic Python (a `split`/`join`/slice/comprehension over its output) is a workaround smell: it usually means the call was wrong or a parameter was missed. Check `doc()` for the parameter or sibling function that answers directly; if it genuinely doesn't exist, propose extending the module rather than bridging with ad hoc code.\n\nThe same smell applies on the way in: wrapping an argument in `str()`, `expanduser()`, pre-escaping, or path-joining that the call already handles means either the docments weren't read or the tooling or its docs need fixing. Check the parameter before dressing the argument, and tell the user when the most ergonomic argument handling doesn't exist or isn't documented: improving tooling is always first priority.\n\nResults are built to be read as their bare reprs: end the cell with the bare expression rather than `print(...)`, which flattens a tuned display to plain `str()`. If a bare result ever reads worse than a printed or reformatted version, the repr is deficient: fix it or tell the user, never quietly work around it.\n\nSummarize what a pyskill's docs or results say rather than dumping the full output verbatim, unless the user actually needs to see all of it.\n\n`doc()` works on *all* python modules, not only pyskills.\n\n`xdir(sym, q=None)` complements `doc()`: it lists an object's public names, filtered by an optional case-insensitive regex. Use it when a module, class, or dynamic API surface is too big to `doc()` whole, e.g. `xdir(page.emulation, 'viewport')` on a fastcdp CDP domain, then `doc()` the match before calling it.\n\n`info_md(obj, source=False)` (from `ipykernel_helper`, preloaded by clikernel startup where installed) is the third way to read an object: IPython's `?` -- or `??` with `source=True` -- rendered as markdown. Reach for it when you want an object's real signature, docstring, and source together, rather than `inspect.getsource`/`inspect.signature` or bare `?`/`??`.\n\n`from pyskills import createskill; doc(createskill)` for how to build and register your own pyskill modules.\n\"\"\"\n\n## types:\n- class SkillTestClass(str): ... # Some class.\n\n## functions:\n- async def async_skill_test_func(x: int = 0) -> str: ... # A test function\u2026\n- def skill_test_func(x: int = 0) -> str: ... # A test function\u2026\n\n## elided: 1 submodules. `doc('pyskills.skill', all=True)` lists them.\n\n# module fastcore.editskill:\n\n\"\"\"Text, file, cell, and notebook editing from `fastcore.tools` and `fastcore.nbio`, plus the conventions the whole fastai editing toolkit follows. Read this before working with the editing tools in any package that shares them.\n\n`from fastcore.editskill import *` loads the fastcore editing layer: the text primitives and file tools of `fastcore.tools`, and the notebook I/O and cell editors of `fastcore.nbio`. Sibling packages extend the same toolkit, and where they are installed prefer them as noted: `exhash` for hash-verified editing, `rgapi` for search, `aidialog`/`dialoghelper` for dialogs.\n\n## Places\n\nEach editing function works on one place: text (a str in memory), a file (a path on disk), a cell (one cell's source in an .ipynb file, addressed by `path, cell_id`), or a notebook (the parsed .ipynb; `Notebook` and `NbCell` are its held-object forms). These are the representation layer: an .ipynb is a file of cells, whatever produced it. The dialog layer above adds the msg and dlg places: a Solveit dialog is an .ipynb whose cells are messages (notes, runnable code, prompt/reply pairs), and `aidialog.dlgskill` and `dialoghelper` provide the message tools, following the conventions here with their own nouns. The word picks the layer: cell tools answer representation questions (\"why does Jupyter reject this file?\"), msg tools answer content questions (\"what does this message say?\").\n\nThe section layer sits above both. `open_doc` (in exhash) parses a file (`fname=`, or a `Path` as `src`), URL (an `https?://` str), or text (any other str) into a `Section` tree, with sections taken from Markdown headings, tree-sitter definitions in code, or md-heading cells in a notebook. A section points at a span of lines in a file, or a run of cells in a notebook. To change what a section contains, edit those lines or cells with the file and cell tools.\n\n## Naming\n\nTwo name shapes cover the toolkit, and the pivot is the verb's direct object:\n\n- An operation on a whole place takes the place as its noun: verb_place. `view_file`, `create_file`, `read_nb`, `write_nb`, `view_cell`, `validate_nb`; in the dialog layer `view_msg` and `view_dlg`. Coined verbs follow the same shape: `lnhashview_cell` is \"lnhashview this cell\". When the verb's object is instead the unit inside the place, and that unit names its place uniquely, no prefix is needed - the unit noun is the place signal: `find_msgs`, `add_msg`, `del_msgs` (msgs live only in dialogs), `find_cells`, `summary_nb`'s rows (cells live only in notebooks).\n- An operation within a place already owns its noun (`insert_line`, `del_lines`, `replace_lines`, `str_replace`), so the place prefixes as a namespace and the op name survives intact: place_op, as in `file_del_lines`, `cell_del_lines`, `msg_del_lines`. The bare op names are the text-level primitives, and every place version keeps the identical signature after its address arguments, so each family is learned once and recognized everywhere.\n\nThe exceptions are deliberate and closed. `str_replace` keeps the name and argument order established by Anthropic's text editor tool. Instrument-named ops put the instrument first and elide their unit: `ast_replace` (the AST pattern is how the edit finds its target) and `exhash` (hash-verified line addresses travel inside its commands), place-prefixed like any other line-level op: `file_ast_replace`, `msg_ast_replace`, `file_exhash`, `cell_exhash`. Converters are named x2y (`nb2dict`, `cell2xml`; in aidialog, `dlg` on exactly one side of every converter), and on a held object the converter is a `to_y` method (`nb.to_dict()`). Plural marks arity: `view_cell` takes one cell, `lnhashview_cells` several, `del_msgs` many.\n\n## Parameters\n\nOne vocabulary, identical wherever it appears:\n\n- The place's address comes first (`text`; `path`; `path, cell_id`; a message `id`), the new text next, and ambient context last as keyword-only (message tools name their dialog that way).\n- `start_line`/`end_line`: 1-based, inclusive, `None` for first/last, negative counting from the end. `del_lines` accepts no defaults: state the range.\n- `re_filter`/`invert_filter`: restrict an edit to lines matching (or not matching) a regex, like ex's `g//` and `g!//`; combines with the range.\n- Searches read patterns as regex by default; editors read them as literal text until `use_regex=True`. Searching is read-only, so its default favors power; editing favors caution.\n- `nums` and `lnhashs` on any view: line numbers, or `lineno|hash|` addresses. `maxlen` caps characters per summary line; `trunc_out`/`trunc_in` truncate outputs and sources in dialog views.\n- Search tools share one filter vocabulary: `pattern` first, `root='.'`, and the same include/exclude/ext/hidden/ignore block across `fd`, `ls`, `rg`, and `nbrg`. Variants differ by defaults, not API: `ls` is `fd` with listing defaults. Boolean filters narrow as `only_*` and widen as `include_*`.\n- `context=` counts whatever units the place has: lines (or blocks in summary mode) for files, cells for notebooks, messages for dialogs. Dialog search defaults to context 1 because the neighbouring note usually explains the match: the why lives next to the what.\n- Every editor returns a diff (\"none: No changes.\" when nothing changed). The diff is the verification: read it instead of re-viewing the target.\n\n## Functions and methods\n\nEvery operation has two shapes with one contract each. The function is a transaction: it addresses a file by path, applies the edit, writes, and returns a diff. The method is a session: it changes the object in hand, and nothing reaches disk until an explicit save. The correspondence is mechanical - the method is the function minus its address arguments, keeping its name except that a place word which became `self` drops: `cell_str_replace(path, cell_id, ...)` is `c.str_replace(...)` on a held `NbCell`, `find_cells(path, pat)` is `nb.find_cells(pat)` on a held `Notebook`. Function-side reads return plain snapshot rows carrying addresses, source, and meta; session-side reads return the objects themselves. Use one shape at a time per file: save before switching to functions, reopen after.\n\n## Addresses\n\nEdits say where with line numbers, lnhash addresses, or section tokens. Take addresses from the read you were already doing instead of with a second look: views accept `nums=True` or `lnhashs=True`, and searches return addresses directly (`rg(lnhashs=True)`). Prefer lnhash addresses whenever `exhash` is installed: they are verified against current content at edit time, so a stale address fails loudly instead of editing nearby text, which is exactly why taking addresses early works. Plain line numbers are unverified and shift as edits apply, so re-view after each edit and apply multi-edits bottom-to-top. `exhash.skill` owns the address format and the verified editor. Section tokens make a long document navigable and editable a piece at a time. An outline row shows one, as in `1.6.|12|a3f2|,45|b1c3|`: the section number, then the lnhash addresses of its first and last lines. `d.at(token)` returns that section with its hash checked, `d.view(tok1, tok2)` prints the rendered text of each, and the same boundary pair drops straight into a range edit: `file_exhash(path, ('12|a3f2|,45|b1c3|', 'c', new_text))`. Read `exhash.skill` before working through a large Markdown, code, or notebook file, or before reading documentation from a URL: `open_doc` also searches within a tree and follows links, which is how the llms.txt docs workflow runs.\n\nWhen you don't yet know where to edit, locate with a summary first: `rgapi`'s `rg(summary=True)` and `nbrg`, aidialog's `summary_dlg`, and exhash's `open_doc` outlines each show one row per natural unit (block, cell, message, section), carrying the unit's address. For prose, config, and other paragraph-shaped text, blocks are usually the right unit for reading too: one summary row shows the whole matched paragraph with its boundary addresses, where a line-mode hit shows a fragment that then needs a context view. Summaries locate, views read, addresses edit, diffs confirm.\n\n## What's where\n\n- `fastcore.tools`: text primitives, file tools, and `line_hash`/`lnhash`/`lnhash_at` for creating addresses without exhash installed.\n- `fastcore.nbio`: notebook read/write/validate/repair, cell construction, cell editors, and the `Notebook`/`NbCell` session objects with their snapshot queries (`find_cells`, `summary_nb`).\n- `exhash.skill`: hash-verified editing for files and cells, plus `open_doc` section outlines for Markdown, code, and notebooks; prefer it for edits where installed.\n- `rgapi.skill`: `rg`/`fd`/`ls`/`nbrg` search with lnhash output, and `rgstr` to search text already in hand.\n- `remold`: structural search and rewrite for Python source (declarative ast-grep rules, LibCST matcher transforms, symbol queries); the engine behind `ast_replace`.\n- `aidialog.dlgskill`, `dialoghelper`: the dialog layer, including its own theory of dialogs and projections.\n\nDocs: https://fastcore.fast.ai/tools.html.md and https://fastcore.fast.ai/nbio.html.md\n\"\"\"\n\n## types:\n- class Notebook: ... # Read, query, and edit Jupyter notebooks\n- class NbCell(AttrDict,adict,dict)\n\n## functions:\n- def insert_line(text: str, insert_line: int, new_str: str): ... # Insert new_str at specified line number\u2026\n- def str_replace(text: str, old_str: str, new_str: str, **replace_params): ... # Replace occurrence(s) of old_str with new_str\u2026\n- def strs_replace(text: str, old_strs: list[str], new_strs: list[str], **replace_params): ... # Replace multiple strings simultaneously\u2026\n- def replace_lines(text: str, start_line: int = None, end_line: int = None, new_content: str = ''): ... # Replace line range with new content; the defaults replace the entire contents\u2026\n- def del_lines(text: str, start_line: int, end_line: int, re_filter: str = None, invert_filter: bool = False): ... # Delete line range; deletion is destructive, so both line numbers must be given explicitly (`1, -1` for all lines)\u2026\n- def ast_replace(text: str, repls: list): ... # Apply ast-grep structural pattern replacements to python source\u2026\n- def file_insert_line(path: str, insert_line: int, new_str: str): ... # Insert new_str at specified line number\u2026\n- def file_str_replace(path: str, old_str: str, new_str: str, **replace_params): ... # Replace occurrence(s) of old_str with new_str\u2026\n- def file_strs_replace(path: str, old_strs: list[str], new_strs: list[str], **replace_params): ... # Replace multiple strings simultaneously\u2026\n- def file_replace_lines(path: str, start_line: int = None, end_line: int = None, new_content: str = ''): ... # Replace line range with new content; the defaults replace the entire contents\u2026\n- def file_del_lines(path: str, start_line: int, end_line: int, re_filter: str = None, invert_filter: bool = False): ... # Delete line range; deletion is destructive, so both line numbers must be given explicitly (`1, -1` for all lines)\u2026\n- def file_ast_replace(path: str, repls: list): ... # Apply ast-grep structural pattern replacements to python source\u2026\n- def view_file(path: str, start_line: int = 1, end_line: int = None, nums: bool = True, lnhashs: bool = False): ... # Read file contents, optionally limited to 1-based line range\u2026\n- def create_file(path: str, contents: str, overwrite: bool = False): ... # Create a new file with contents. Error if file exists, unless `overwrite`.\u2026\n- def line_hash(line: str) -> str: ... # 4-char hex hash of `line`\u2026\n- def lnhash(lineno: int, line: str) -> str: ... # `lineno|hash|` exhash address for `line` at `lineno`\u2026\n- def lnhash_at(s: str | list | tuple, line: int) -> str: ... # `lineno|hash|` exhash address of line `line` of `s`\u2026\n- def read_nb(path): ... # Return notebook at `path` (expands `~`) [1bdc]\n- def write_nb(nb, path): ... # Write `nb` to `path` (expands `~`) [a307]\n- def new_nb(cells=None, meta=None, nbformat=4, nbformat_minor=5): ... # Returns an empty new notebook [dee4]\n- def mk_cell(text, cell_type='code', **kwargs): ... # Create an `NbCell` containing `text`\u2026\n- def validate_nb(nb): ... # Raise `ValueError` for structural problems in notebook `nb`; returns it unchanged if fine [141c]\n- def validate_cell(cell, idx=None): ... # Raise `ValueError` for structural problems in notebook cell dict `cell`; returns it unchanged if fine [0ba9]\n- def repair_nb(nb): ... # Fix deterministic structural problems in `nb`, returning a list of repairs made [137e]\n- def repair_cell(cell, idx=None): ... # Fix deterministic structural problems in `cell`, returning a list of repairs made [8864]\n- def view_cell(path: str, cell_id: str, start_line: int = 1, end_line: int = None, nums: bool = True, lnhashs: bool = False, incl_out: bool = False, trunc_out: bool = True): ... # View a cell's source, optionally limited to 1-based line range\u2026\n- def cell_insert_line(path: str, cell_id: str, insert_line: int, new_str: str): ... # Insert new_str at specified line number\u2026\n- def cell_str_replace(path: str, cell_id: str, old_str: str, new_str: str, **replace_params): ... # Replace occurrence(s) of old_str with new_str\u2026\n- def cell_strs_replace(path: str, cell_id: str, old_strs: list[str], new_strs: list[str], **replace_params): ... # Replace multiple strings simultaneously\u2026\n- def cell_replace_lines(path: str, cell_id: str, start_line: int = None, end_line: int = None, new_content: str = ''): ... # Replace line range with new content; the defaults replace the entire contents\u2026\n- def cell_del_lines(path: str, cell_id: str, start_line: int, end_line: int, re_filter: str = None, invert_filter: bool = False): ... # Delete line range; deletion is destructive, so both line numbers must be given explicitly (`1, -1` for all lines)\u2026\n- def cell_ast_replace(path: str, cell_id: str, repls: list): ... # Apply ast-grep structural pattern replacements to python source\u2026\n- def find_cells(path, pat: str = '', cell_type: str = None, ids='', context: int = None): ... # Snapshot `FoundCells` for matching cells in the notebook at `path`\u2026\n- def summary_nb(path, maxlen: int = 180): ... # One snapshot line per cell of the notebook at `path`\u2026\n\n## shared params:\n- **replace_params (from `str_replace`):\n start_line: int = None, # Optional 1-based start line to limit search\n end_line: int = None, # Optional 1-based end line to limit search\n n_matches: int = None, # Max replacements (None=all)\n re_filter: str = None, # If provided, only process lines matching this regex (like g// in ex)\n invert_filter: bool = False, # Invert the filter (like g!// in ex)\n use_regex: bool = False, # Treat old_str as a regex, and new_str as an `re.sub` template?"}]}, "toolUseResult": [{"type": "text", "text": "# module clikernel.skill:\n\n\"\"\"Use the `clikernel` MCP session as the default workspace for Python work: reading and changing files, notebook work, trying things out, checking how a library behaves, and reshaping data. One session stays open, so imports and variables carry between calls. Read this before writing, running, or debugging Python code in a session with `clikernel` connected.\n\nPrefer it over one-off Python scripts (`python -c`, shell heredocs). Prefer in-kernel tools over shell equivalents when they exist: file search and directory listing go through the `rgapi` pyskill (`rg()`/`fd()`/`ls()`), and GitHub and local git work through the `ghapi` pyskill, when those are installed. Shell commands remain the right tool for project test/build commands and non-Python tools.\n\n# Starting and stopping\n\nThe first `execute` creates a kernel and reports what it imported: read that banner, it says what to do next. That kernel stops when the conversation ends. Use a bare `connect` instead when the kernel should stay running afterwards, and tell the user its id.\n\nTo continue earlier work, or to use the user's solveit kernel, `list_kernels` then `connect` with the id. Attaching runs no setup: the kernel's live state is the point.\n\n`restart` gives a fresh interpreter under the same id, so redo any setup. `interrupt` stops a long `execute` and keeps state. If a reply says the kernel has stopped, `connect` again. If `connect` fails, the gateway server is not running: tell the user rather than working around it.\n\nRemote gateways are the same verbs with a `host`, named in `~/.config/clikernel/gateways.toml`.\n\n# Working in it\n\n- Magics work as written. `%cd` expands `~` and is the way to change directory: prefer it over `os.chdir`.\n- Only the last expression in a cell displays. `print(...)` any earlier value you need to see.\n- Everything a cell outputs lands in the conversation. Be selective: `len(v)` first, then decide what to show.\n- Don't re-run an `import` already run this session. If a name raises `NameError`, the kernel restarted or is newly attached: redo setup.\n- After an `nbdev-export` (or any edit to a module already imported), `importlib.reload` that module and re-import any names you hold from it: a name bound by `from x import y` keeps the old object, while a `@patch`ed method refreshes with the reload because the patch writes onto the shared class. Restart only when a class you hold instances of was itself redefined. Check what is loaded by calling it, never with `inspect.getsource`, which reads the file on disk.\n- Try the simple import or API call first, before changing the environment, monkeypatching, or adding setup.\n\"\"\"\n\n# module pyskills.skill:\n\n\"\"\"Pyskills are tool modules that Python packages register so you can find them without importing everything. `list_pyskills()` names the ones installed, with a one-line description each, and needs no imports. Load one with a normal import, then read its docs with `doc()`.\n\nUse `doc()` at increasing detail, module first, then the specific class or function before its first use:\n\n import pyskills.skill\n doc(pyskills.skill) # module overview: classes, functions, submodules\n doc(SkillTestClass, skill_test_func) # symbol detail: full signatures with docments, one section each\n\nNormally use `from import *` when loading a pyskill: each pyskill's `__all__` is carefully curated, so a star import brings in exactly the intended API.\n\n`doc()` returns a `PrettyString`. A bare final call is rendered by IPython; assigning the result is silent. Assign it when you do not want it rendered, for instance for very large docs you want to search through.\n\nDoc the module once while its output is still visible in the conversation, then doc each class or function right before its first call. This is conversation state, not Python-process state: read it again when the earlier output is no longer visible, not merely because the kernel restarted.\n\nIn a module overview, a trailing `...` on a line means docments and usage notes exist beyond the signature and first docstring line, and only `doc(func)` shows them: read it before the first call, however complete the line looks. A line with no `...` is that function's whole doc. A `**name` collector (other than `**kwargs` itself) is a shared param group: its params are listed once under `## shared params:` and are passed as ordinary keyword args. `doc` takes several objects at once, each returned as its own section, so batch the reads.\n\nWhen more than one pyskill looks like a candidate for a task, `doc()` each candidate rather than guessing from the one-line descriptions: some specialize by input type (e.g. `fastcore.tools` for plain text and files vs `aidialog.dlgskill` for notebooks and dialogs; prefer `exhash.skill` for text editing when it's available) and the short description won't always make the distinction clear.\n\nPyskill results are designed so the right call answers the question directly. Post-processing a result with generic Python (a `split`/`join`/slice/comprehension over its output) is a workaround smell: it usually means the call was wrong or a parameter was missed. Check `doc()` for the parameter or sibling function that answers directly; if it genuinely doesn't exist, propose extending the module rather than bridging with ad hoc code.\n\nThe same smell applies on the way in: wrapping an argument in `str()`, `expanduser()`, pre-escaping, or path-joining that the call already handles means either the docments weren't read or the tooling or its docs need fixing. Check the parameter before dressing the argument, and tell the user when the most ergonomic argument handling doesn't exist or isn't documented: improving tooling is always first priority.\n\nResults are built to be read as their bare reprs: end the cell with the bare expression rather than `print(...)`, which flattens a tuned display to plain `str()`. If a bare result ever reads worse than a printed or reformatted version, the repr is deficient: fix it or tell the user, never quietly work around it.\n\nSummarize what a pyskill's docs or results say rather than dumping the full output verbatim, unless the user actually needs to see all of it.\n\n`doc()` works on *all* python modules, not only pyskills.\n\n`xdir(sym, q=None)` complements `doc()`: it lists an object's public names, filtered by an optional case-insensitive regex. Use it when a module, class, or dynamic API surface is too big to `doc()` whole, e.g. `xdir(page.emulation, 'viewport')` on a fastcdp CDP domain, then `doc()` the match before calling it.\n\n`info_md(obj, source=False)` (from `ipykernel_helper`, preloaded by clikernel startup where installed) is the third way to read an object: IPython's `?` -- or `??` with `source=True` -- rendered as markdown. Reach for it when you want an object's real signature, docstring, and source together, rather than `inspect.getsource`/`inspect.signature` or bare `?`/`??`.\n\n`from pyskills import createskill; doc(createskill)` for how to build and register your own pyskill modules.\n\"\"\"\n\n## types:\n- class SkillTestClass(str): ... # Some class.\n\n## functions:\n- async def async_skill_test_func(x: int = 0) -> str: ... # A test function\u2026\n- def skill_test_func(x: int = 0) -> str: ... # A test function\u2026\n\n## elided: 1 submodules. `doc('pyskills.skill', all=True)` lists them.\n\n# module fastcore.editskill:\n\n\"\"\"Text, file, cell, and notebook editing from `fastcore.tools` and `fastcore.nbio`, plus the conventions the whole fastai editing toolkit follows. Read this before working with the editing tools in any package that shares them.\n\n`from fastcore.editskill import *` loads the fastcore editing layer: the text primitives and file tools of `fastcore.tools`, and the notebook I/O and cell editors of `fastcore.nbio`. Sibling packages extend the same toolkit, and where they are installed prefer them as noted: `exhash` for hash-verified editing, `rgapi` for search, `aidialog`/`dialoghelper` for dialogs.\n\n## Places\n\nEach editing function works on one place: text (a str in memory), a file (a path on disk), a cell (one cell's source in an .ipynb file, addressed by `path, cell_id`), or a notebook (the parsed .ipynb; `Notebook` and `NbCell` are its held-object forms). These are the representation layer: an .ipynb is a file of cells, whatever produced it. The dialog layer above adds the msg and dlg places: a Solveit dialog is an .ipynb whose cells are messages (notes, runnable code, prompt/reply pairs), and `aidialog.dlgskill` and `dialoghelper` provide the message tools, following the conventions here with their own nouns. The word picks the layer: cell tools answer representation questions (\"why does Jupyter reject this file?\"), msg tools answer content questions (\"what does this message say?\").\n\nThe section layer sits above both. `open_doc` (in exhash) parses a file (`fname=`, or a `Path` as `src`), URL (an `https?://` str), or text (any other str) into a `Section` tree, with sections taken from Markdown headings, tree-sitter definitions in code, or md-heading cells in a notebook. A section points at a span of lines in a file, or a run of cells in a notebook. To change what a section contains, edit those lines or cells with the file and cell tools.\n\n## Naming\n\nTwo name shapes cover the toolkit, and the pivot is the verb's direct object:\n\n- An operation on a whole place takes the place as its noun: verb_place. `view_file`, `create_file`, `read_nb`, `write_nb`, `view_cell`, `validate_nb`; in the dialog layer `view_msg` and `view_dlg`. Coined verbs follow the same shape: `lnhashview_cell` is \"lnhashview this cell\". When the verb's object is instead the unit inside the place, and that unit names its place uniquely, no prefix is needed - the unit noun is the place signal: `find_msgs`, `add_msg`, `del_msgs` (msgs live only in dialogs), `find_cells`, `summary_nb`'s rows (cells live only in notebooks).\n- An operation within a place already owns its noun (`insert_line`, `del_lines`, `replace_lines`, `str_replace`), so the place prefixes as a namespace and the op name survives intact: place_op, as in `file_del_lines`, `cell_del_lines`, `msg_del_lines`. The bare op names are the text-level primitives, and every place version keeps the identical signature after its address arguments, so each family is learned once and recognized everywhere.\n\nThe exceptions are deliberate and closed. `str_replace` keeps the name and argument order established by Anthropic's text editor tool. Instrument-named ops put the instrument first and elide their unit: `ast_replace` (the AST pattern is how the edit finds its target) and `exhash` (hash-verified line addresses travel inside its commands), place-prefixed like any other line-level op: `file_ast_replace`, `msg_ast_replace`, `file_exhash`, `cell_exhash`. Converters are named x2y (`nb2dict`, `cell2xml`; in aidialog, `dlg` on exactly one side of every converter), and on a held object the converter is a `to_y` method (`nb.to_dict()`). Plural marks arity: `view_cell` takes one cell, `lnhashview_cells` several, `del_msgs` many.\n\n## Parameters\n\nOne vocabulary, identical wherever it appears:\n\n- The place's address comes first (`text`; `path`; `path, cell_id`; a message `id`), the new text next, and ambient context last as keyword-only (message tools name their dialog that way).\n- `start_line`/`end_line`: 1-based, inclusive, `None` for first/last, negative counting from the end. `del_lines` accepts no defaults: state the range.\n- `re_filter`/`invert_filter`: restrict an edit to lines matching (or not matching) a regex, like ex's `g//` and `g!//`; combines with the range.\n- Searches read patterns as regex by default; editors read them as literal text until `use_regex=True`. Searching is read-only, so its default favors power; editing favors caution.\n- `nums` and `lnhashs` on any view: line numbers, or `lineno|hash|` addresses. `maxlen` caps characters per summary line; `trunc_out`/`trunc_in` truncate outputs and sources in dialog views.\n- Search tools share one filter vocabulary: `pattern` first, `root='.'`, and the same include/exclude/ext/hidden/ignore block across `fd`, `ls`, `rg`, and `nbrg`. Variants differ by defaults, not API: `ls` is `fd` with listing defaults. Boolean filters narrow as `only_*` and widen as `include_*`.\n- `context=` counts whatever units the place has: lines (or blocks in summary mode) for files, cells for notebooks, messages for dialogs. Dialog search defaults to context 1 because the neighbouring note usually explains the match: the why lives next to the what.\n- Every editor returns a diff (\"none: No changes.\" when nothing changed). The diff is the verification: read it instead of re-viewing the target.\n\n## Functions and methods\n\nEvery operation has two shapes with one contract each. The function is a transaction: it addresses a file by path, applies the edit, writes, and returns a diff. The method is a session: it changes the object in hand, and nothing reaches disk until an explicit save. The correspondence is mechanical - the method is the function minus its address arguments, keeping its name except that a place word which became `self` drops: `cell_str_replace(path, cell_id, ...)` is `c.str_replace(...)` on a held `NbCell`, `find_cells(path, pat)` is `nb.find_cells(pat)` on a held `Notebook`. Function-side reads return plain snapshot rows carrying addresses, source, and meta; session-side reads return the objects themselves. Use one shape at a time per file: save before switching to functions, reopen after.\n\n## Addresses\n\nEdits say where with line numbers, lnhash addresses, or section tokens. Take addresses from the read you were already doing instead of with a second look: views accept `nums=True` or `lnhashs=True`, and searches return addresses directly (`rg(lnhashs=True)`). Prefer lnhash addresses whenever `exhash` is installed: they are verified against current content at edit time, so a stale address fails loudly instead of editing nearby text, which is exactly why taking addresses early works. Plain line numbers are unverified and shift as edits apply, so re-view after each edit and apply multi-edits bottom-to-top. `exhash.skill` owns the address format and the verified editor. Section tokens make a long document navigable and editable a piece at a time. An outline row shows one, as in `1.6.|12|a3f2|,45|b1c3|`: the section number, then the lnhash addresses of its first and last lines. `d.at(token)` returns that section with its hash checked, `d.view(tok1, tok2)` prints the rendered text of each, and the same boundary pair drops straight into a range edit: `file_exhash(path, ('12|a3f2|,45|b1c3|', 'c', new_text))`. Read `exhash.skill` before working through a large Markdown, code, or notebook file, or before reading documentation from a URL: `open_doc` also searches within a tree and follows links, which is how the llms.txt docs workflow runs.\n\nWhen you don't yet know where to edit, locate with a summary first: `rgapi`'s `rg(summary=True)` and `nbrg`, aidialog's `summary_dlg`, and exhash's `open_doc` outlines each show one row per natural unit (block, cell, message, section), carrying the unit's address. For prose, config, and other paragraph-shaped text, blocks are usually the right unit for reading too: one summary row shows the whole matched paragraph with its boundary addresses, where a line-mode hit shows a fragment that then needs a context view. Summaries locate, views read, addresses edit, diffs confirm.\n\n## What's where\n\n- `fastcore.tools`: text primitives, file tools, and `line_hash`/`lnhash`/`lnhash_at` for creating addresses without exhash installed.\n- `fastcore.nbio`: notebook read/write/validate/repair, cell construction, cell editors, and the `Notebook`/`NbCell` session objects with their snapshot queries (`find_cells`, `summary_nb`).\n- `exhash.skill`: hash-verified editing for files and cells, plus `open_doc` section outlines for Markdown, code, and notebooks; prefer it for edits where installed.\n- `rgapi.skill`: `rg`/`fd`/`ls`/`nbrg` search with lnhash output, and `rgstr` to search text already in hand.\n- `remold`: structural search and rewrite for Python source (declarative ast-grep rules, LibCST matcher transforms, symbol queries); the engine behind `ast_replace`.\n- `aidialog.dlgskill`, `dialoghelper`: the dialog layer, including its own theory of dialogs and projections.\n\nDocs: https://fastcore.fast.ai/tools.html.md and https://fastcore.fast.ai/nbio.html.md\n\"\"\"\n\n## types:\n- class Notebook: ... # Read, query, and edit Jupyter notebooks\n- class NbCell(AttrDict,adict,dict)\n\n## functions:\n- def insert_line(text: str, insert_line: int, new_str: str): ... # Insert new_str at specified line number\u2026\n- def str_replace(text: str, old_str: str, new_str: str, **replace_params): ... # Replace occurrence(s) of old_str with new_str\u2026\n- def strs_replace(text: str, old_strs: list[str], new_strs: list[str], **replace_params): ... # Replace multiple strings simultaneously\u2026\n- def replace_lines(text: str, start_line: int = None, end_line: int = None, new_content: str = ''): ... # Replace line range with new content; the defaults replace the entire contents\u2026\n- def del_lines(text: str, start_line: int, end_line: int, re_filter: str = None, invert_filter: bool = False): ... # Delete line range; deletion is destructive, so both line numbers must be given explicitly (`1, -1` for all lines)\u2026\n- def ast_replace(text: str, repls: list): ... # Apply ast-grep structural pattern replacements to python source\u2026\n- def file_insert_line(path: str, insert_line: int, new_str: str): ... # Insert new_str at specified line number\u2026\n- def file_str_replace(path: str, old_str: str, new_str: str, **replace_params): ... # Replace occurrence(s) of old_str with new_str\u2026\n- def file_strs_replace(path: str, old_strs: list[str], new_strs: list[str], **replace_params): ... # Replace multiple strings simultaneously\u2026\n- def file_replace_lines(path: str, start_line: int = None, end_line: int = None, new_content: str = ''): ... # Replace line range with new content; the defaults replace the entire contents\u2026\n- def file_del_lines(path: str, start_line: int, end_line: int, re_filter: str = None, invert_filter: bool = False): ... # Delete line range; deletion is destructive, so both line numbers must be given explicitly (`1, -1` for all lines)\u2026\n- def file_ast_replace(path: str, repls: list): ... # Apply ast-grep structural pattern replacements to python source\u2026\n- def view_file(path: str, start_line: int = 1, end_line: int = None, nums: bool = True, lnhashs: bool = False): ... # Read file contents, optionally limited to 1-based line range\u2026\n- def create_file(path: str, contents: str, overwrite: bool = False): ... # Create a new file with contents. Error if file exists, unless `overwrite`.\u2026\n- def line_hash(line: str) -> str: ... # 4-char hex hash of `line`\u2026\n- def lnhash(lineno: int, line: str) -> str: ... # `lineno|hash|` exhash address for `line` at `lineno`\u2026\n- def lnhash_at(s: str | list | tuple, line: int) -> str: ... # `lineno|hash|` exhash address of line `line` of `s`\u2026\n- def read_nb(path): ... # Return notebook at `path` (expands `~`) [1bdc]\n- def write_nb(nb, path): ... # Write `nb` to `path` (expands `~`) [a307]\n- def new_nb(cells=None, meta=None, nbformat=4, nbformat_minor=5): ... # Returns an empty new notebook [dee4]\n- def mk_cell(text, cell_type='code', **kwargs): ... # Create an `NbCell` containing `text`\u2026\n- def validate_nb(nb): ... # Raise `ValueError` for structural problems in notebook `nb`; returns it unchanged if fine [141c]\n- def validate_cell(cell, idx=None): ... # Raise `ValueError` for structural problems in notebook cell dict `cell`; returns it unchanged if fine [0ba9]\n- def repair_nb(nb): ... # Fix deterministic structural problems in `nb`, returning a list of repairs made [137e]\n- def repair_cell(cell, idx=None): ... # Fix deterministic structural problems in `cell`, returning a list of repairs made [8864]\n- def view_cell(path: str, cell_id: str, start_line: int = 1, end_line: int = None, nums: bool = True, lnhashs: bool = False, incl_out: bool = False, trunc_out: bool = True): ... # View a cell's source, optionally limited to 1-based line range\u2026\n- def cell_insert_line(path: str, cell_id: str, insert_line: int, new_str: str): ... # Insert new_str at specified line number\u2026\n- def cell_str_replace(path: str, cell_id: str, old_str: str, new_str: str, **replace_params): ... # Replace occurrence(s) of old_str with new_str\u2026\n- def cell_strs_replace(path: str, cell_id: str, old_strs: list[str], new_strs: list[str], **replace_params): ... # Replace multiple strings simultaneously\u2026\n- def cell_replace_lines(path: str, cell_id: str, start_line: int = None, end_line: int = None, new_content: str = ''): ... # Replace line range with new content; the defaults replace the entire contents\u2026\n- def cell_del_lines(path: str, cell_id: str, start_line: int, end_line: int, re_filter: str = None, invert_filter: bool = False): ... # Delete line range; deletion is destructive, so both line numbers must be given explicitly (`1, -1` for all lines)\u2026\n- def cell_ast_replace(path: str, cell_id: str, repls: list): ... # Apply ast-grep structural pattern replacements to python source\u2026\n- def find_cells(path, pat: str = '', cell_type: str = None, ids='', context: int = None): ... # Snapshot `FoundCells` for matching cells in the notebook at `path`\u2026\n- def summary_nb(path, maxlen: int = 180): ... # One snapshot line per cell of the notebook at `path`\u2026\n\n## shared params:\n- **replace_params (from `str_replace`):\n start_line: int = None, # Optional 1-based start line to limit search\n end_line: int = None, # Optional 1-based end line to limit search\n n_matches: int = None, # Max replacements (None=all)\n re_filter: str = None, # If provided, only process lines matching this regex (like g// in ex)\n invert_filter: bool = False, # Invert the filter (like g!// in ex)\n use_regex: bool = False, # Treat old_str as a regex, and new_str as an `re.sub` template?"}]} +{"type": "assistant", "uuid": "95f7449d-8e28-5a3d-a0cf-12e49e5d1b00", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/home/natedawg/aai-ws/llmdojo", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "assistant", "content": [{"type": "tool_use", "id": "toolu_01FUJHFXpnggen2yQbS3tQGE", "name": "mcp__clikernel__execute", "input": {"code": "doc(dsk, exh, rgsk)"}}], "id": "msg_919aafb2b8a251cd92138c9f", "stop_reason": "tool_use", "stop_sequence": null, "stop_details": null, "usage": {"input_tokens": 5026, "output_tokens": 9, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 0}}, "requestId": "req_ba7f224ffe2751b0858eb014"} +{"type": "user", "uuid": "8ad142fa-d69b-5038-94d3-587a6ba99d6e", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/home/natedawg/aai-ws/llmdojo", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "user", "content": [{"type": "tool_result", "tool_use_id": "toolu_01FUJHFXpnggen2yQbS3tQGE", "content": "# module aidialog.dlgskill:\n\n\"\"\"Read, search, and edit dialogs and notebooks through aidialog's `Dialog` and `Message` classes\n\nUse this whenever the question or edit concerns a notebook's content: messages, sources, outputs, prompts and their replies.\n\n## The hierarchy\n\nNotebook work happens at three levels, and picking the right level is most of using this module well:\n\n- Content (this module): what the messages say and how they change. `summary_dlg`, `find_msgs`, `view_dlg`, the message editing operations.\n- Representation (`fastcore.nbio`): which keys exist, whether a file is schema-valid, whether bytes changed. Start with `validate_nb`/`validate_cell`, which name the cell at fault; use `read_nb` directly when the question is about the dict itself. For *plain* notebooks (no dialog semantics), nbio's `Notebook`/`NbCell` objects and `cell_*` functions are the content surface, and this module's word choice marks the layer: cells for notebooks, messages for dialogs.\n- Raw text: only when the file will not parse at all.\n\n## Core APIs\n\nThe function/method two-shapes contract is `fastcore.editskill`'s, learned once: the function is a transaction addressed by `dlg=` (an ipynb path, or None meaning the current dialog file: `set_dlg`); the method is a session on a held `Dialog` or `Message`, saved by an explicit `dlg.save()` (`msg_str_replace(id, ..., dlg=p)` \u27f7 `m.str_replace(...)`). Wrapping any message list in an ephemeral `Dialog(msgs)` makes the whole session surface available on it.\n\n- `summary_dlg(dlg)` / `d.summary()`: one `preview` per message, `id:t[directives]:content` (t: c=code n=note p=prompt r=raw; the bracket, as in nbio's `CellRow`, shows meta-form nbdev directives such as `[export]`), with a prompt's reply on a following `> ` line; a line cut short ends in a humanized `[size]`.\n- `find_msgs(pattern, dlg, ...)`: search by regex, type, errors, heading, ids, or a `pred` (`symdef_finder`/`symref_finder`/`ast_finder` build structural ones); `context` defaults to 1 even with `ids=`, because the neighbouring message usually explains the match -- retrieve and read it. Returns `MsgRow` snapshots (`id`, `msg_type`, `content`, `out`, `meta`) in a `MsgRows`; `d.find_msgs(...)` returns live `Message`s in a `FoundMsgs`. Both index by message id (exact or unique prefix) and refuse integer positions: a find result includes context rows, so `[0]` may be a neighbour of the match, and the raised error teaches the id idiom. Previews mark context rows with `-` in place of the first `:`. Every live message carries a `dlg` backref to its owning `Dialog`, so dialog-level operations are always in reach from a message in hand (e.g. `m.dlg.save()` after mutating `m.output` directly).\n- `view_dlg(dlg)` / `d.view()` / `view_msg(id)` / `m.view()` / `view_msgs(*ids)` / `msg2xml(m)` / `m.to_xml()`: full views in the shared `item2xml` grammar (a prompt's reply is its `` section; meta `nbdev` directives render as attrs, so a meta-exported message carries a bare `export`); `incl_out=True` on the line views appends the message's output the same way.\n- Structure: `add_msg`, `del_msgs`, `move_msgs`, `split_msg`, `merge_msgs`, `copy_msgs`/`cut_msgs`/`paste_msgs`, `create_dlg`, with session twins `d.move_msgs`, `m.split`, `d.merge_msgs`, `d.copy_msgs`/`d.cut_msgs`/`d.paste_msgs` (session adds go through `d.mk_message`, deletes through `d.remove_msgs`). `add_msg` with neither `before=` nor `after=` places after the current message (`set_cur_msg`, which a host sets and no write ever moves), or at the end when that is unset or gone. `add_msg` and `d.mk_message` take `meta=` and an `export=` shortcut for the meta `nbdev` export flag, readable and assignable as `m.meta_exported` (`m.exported` reads content and meta together, and is what `find_msgs(only_exp=True)` filters on). The `%%add_msg` magic takes its body verbatim: its line is `%%add_msg [dlg] [msg_type] [export] [before=|after=]`, where a bare path token is the dlg and a bare type name the msg_type, and keyword spellings win over bare tokens.\n- `update_msg(id, ..., dlg=)` / `m.update(...)`: one transaction for a message's attributes. Plain keywords assign (`content=`, `msg_type=`, `output=`, `meta=` replacing wholesale); `mergemeta` deep-merges into `meta` (a `None` value deletes its key); `export` sets the nbdev directive -- `True` adds, `False` removes (there is no negative form), and either migrates a content-form `#| export` line to meta. The function returns a diff of the message's XML plus a `meta:` line, so every change it can make is visible.\n- The `%nbrun` line magic runs code cells from the current notebook in the running kernel, by cell id prefix, same grammar: bare tokens are flags (`above`, `below`, `all`, `exported`, `ignore_eval`, `continue_on_error`, `show`) or id prefixes, and `fname=` overrides the notebook for one call. Only cells named by id display their output: bulk-selected cells run silently (`show` overrides), errors always surface naming the failed cell, and the run ends with a `nbrun: N cells ok` line. It returns a coroutine (awaited by async-magic machinery) and runs each cell in the kernel's namespace through user-level channels only (exec, `display()`, raising), so it behaves identically in every kernel.\n- `d.execute(*ids, ...)` / `m.execute()`: the captured twin, on a held `Dialog`. Runs code messages on an execnb `CaptureShell` (fresh and dropped, unless you pass `shell=`), assigns each result to `m.output` in memory (`d.save()` persists), and returns the messages run as a `RunResult` whose repr is a status report: ok lines collapse into a counted marker, failures always show with the exception. It blocks until done, so an async host should wrap it in `asyncio.to_thread`. `above`/`below` anchor at the ids you pass, and bare `d.execute()` runs all participating code messages (the `eval` cascade).\n- Text edits: `msg_str_replace`, `msg_strs_replace`, `msg_insert_line`, `msg_replace_lines`, `msg_del_lines`, `msg_ast_replace` (all with `re_filter`/line-range powers; `out=True` edits a prompt's reply or a code message's outputs literal), with the same names as `Message` methods for in-memory editing; `lnhashview_msg`/`msg_exhash` (and `m.lnhashview()`/`m.exhash()`) for hash-verified line edits (`lnhashview_msg` is `view_msg(..., lnhashs=True)`; only the exhash pair needs the `exhash` package).\n\n## Idiomatic usage\n\nStart by registering the notebook: `set_dlg(path)` makes every function here default to it (and the `%nbrun` magic follows suit), so calls read as \"do this to message X\". Then orient before acting:\n\n- `summary_dlg()` first for anything sizable: a cheap one-line-per-message map. `view_dlg()` when you need the full story in order with ids. Read a notebook in full before describing or changing what it does - the interleaved prose, examples, and stored outputs (`incl_out=True`) are the design rationale.\n- Read before trying things out: an ad-hoc \"what happens if...\" check usually re-derives, more slowly and less reliably, what an existing example cell already shows. If after reading you still need to try it, that's a gap in the notebook - add it as a proper example cell so the next reader doesn't repeat it.\n- `find_msgs` is the targeted view: keep the default `context=1` (the neighbouring markdown usually explains the match), and `ids=` with context is the positional query (\"does A precede B?\", \"what's the idiom around here?\"). Name questions are structural, not textual: `symdef_finder`/`symref_finder`/`ast_finder` beat regexes over binding syntax. Where available, rgapi's `nbrg` searches cell sources across files and returns the cell ids these functions take.\n- Edit at the right level: `add_msg`/`%%add_msg` for new messages; within a message, prefer hash-verified addressing (`lnhashview_msg`/`msg_exhash`, or exhash's `lnhashview_cell`/`cell_exhash` by path and cell id) - view with the lnhash variant the moment an edit is plausible, so the view doubles as the edit's address book. The plain `msg_*` editors fit where exhash can't express the edit, e.g. one `msg_str_replace` across an id list. Never splice via `read_nb`/`write_nb` internals: if a primitive is missing here, stop and propose adding it.\n- `dlg.validate()` catches structural problems early; `dlg.save()` writes back. Don't wrap calls in `hasattr` or `try/except` - call directly and read the bare result.\n\nDocs: https://AnswerDotAI.github.io/aidialog/dlgskill.html.md\n\"\"\"\n\n## types:\n- class FoundMsgs(Found,Msgs,L,\u2026): ... # Find results: live messages indexed by id (exact or unique prefix), never by position; context rows show `-` in place of their first `:`\n\n## functions:\n- def msg_insert_line(id, insert_line: int, new_str: str, *, dlg=None, out: bool = False): ... # Insert new_str at specified line number\u2026\n- def msg_str_replace(id, old_str: str, new_str: str, *, dlg=None, out: bool = False, **replace_params): ... # Replace occurrence(s) of old_str with new_str\u2026\n- def msg_strs_replace(id, old_strs: list[str], new_strs: list[str], *, dlg=None, out: bool = False, **replace_params): ... # Replace multiple strings simultaneously\u2026\n- def msg_replace_lines(id, start_line: int = None, end_line: int = None, new_content: str = '', *, dlg=None, out: bool = False): ... # Replace line range with new content; the defaults replace the entire contents\u2026\n- def msg_del_lines(id, start_line: int, end_line: int, re_filter: str = None, invert_filter: bool = False, *, dlg=None, out: bool = False): ... # Delete line range; deletion is destructive, so both line numbers must be given explicitly (`1, -1` for all lines)\u2026\n- def msg_ast_replace(id, repls: list, *, dlg=None, out: bool = False): ... # Apply ast-grep structural pattern replacements to python source\u2026\n- def set_dlg(fname, cls=None): ... # Set the current dialog file, used by these functions when `dlg` is None\u2026\n- def cur_dlg(): ... # A fresh `Dialog` read from the current dialog file (None if unset): the session-side entry to the ambient default [9e42]\n- def set_cur_msg(id): ... # Set the current message, used by `add_msg` when no placement is given\u2026\n- def cur_msg(): ... # The current message, read fresh from the current dialog file (None if unset, or no longer there) [18ee]\n- def summary_dlg(dlg=None, maxlen: int = 180): ... # One `preview` per message of the dialog file\u2026\n- def view_dlg(dlg=None, incl_out: bool = False, only_errors: bool = False, trunc_out: bool = True): ... # The dialog file as concise XML; meta-exported messages carry a bare `export` attr\u2026\n- def view_msg(id, dlg=None, nums: bool = True, start_line: int = 1, end_line: int = None, lnhashs: bool = False, incl_out: bool = False, trunc_out: bool = True): ... # Show a message's content with optional line numbers or exhash addresses\u2026\n- def view_msgs(*ids, dlg=None, nums: bool = True, start_line: int = 1, end_line: int = None, lnhashs: bool = False, incl_out: bool = False, trunc_out: bool = True): ... # Show several messages, each preceded by a `# msg ` header\u2026\n- def find_msgs(re_pattern: str = '', dlg=None, *, msg_type: str = None, only_err: bool = False, only_exp: bool = False, ids='', before: int = 0, after: int = 0, context: int = None, limit: int = None, use_case: bool = False, use_regex: bool = True, headers_only: bool = False, header_section: str = None, pred: callable = None) -> MsgRows: ... # Find messages in the dialog file matching all the given criteria; for live results, call `Dialog.find_msgs`\u2026\n- def move_msgs(ids, before=None, after=None, dlg=None): ... # Move messages in the dialog file, keeping their relative order; returns them\u2026\n- def split_msg(id, *linenos: int, dlg=None): ... # Split a message in the dialog file (see `Message.split`)\u2026\n- def merge_msgs(*ids, dlg=None): ... # Merge messages in the dialog file (see `Dialog.merge_msgs`)\u2026\n- def copy_msgs(*ids, dlg=None): ... # Copy messages into the paste buffer (replacing its contents), for later `paste_msgs`\u2026\n- def cut_msgs(*ids, dlg=None): ... # Copy messages into the paste buffer, then remove them from the dialog file\u2026\n- def paste_msgs(before=None, after=None, dlg=None): ... # Insert copies of the buffered messages (fresh ids) before/after a message or id in the dialog file; returns the new messages\u2026\n- def symdef_finder(name): ... # `find_msgs` predicate: does the message bind `name` at cell top level? (requires `remold`) [a533]\n- def symref_finder(name): ... # `find_msgs` predicate: does the message reference `name`? (requires `remold`) [3810]\n- def ast_finder(pattern): ... # `find_msgs` predicate: does ast-grep `pattern` match in the message? (requires `remold`) [3b44]\n- def lnhashview_msg(id, dlg=None): ... # Hash-addressed view of a message's content, for `msg_exhash`\u2026\n- def msg_exhash(id, *cmds: tuple, sw: int = 4, dlg=None): ... # Apply exhash commands to a message in the dialog file, returning the diff\u2026\n- def add_msg(source: str, msg_type: str = 'code', before: str = None, after: str = None, meta=None, export=False, dlg=None): ... # Add a new message before or after an existing one (default: after the current message, else at the end), returning it\u2026\n- def del_msgs(*ids, dlg=None): ... # Delete messages by id, returning the removed messages\u2026\n- def create_dlg(fname: str, source: str = '', msg_type: str = 'code'): ... # Create a new dialog file containing one message, returning the `Dialog` (with `path_` stamped)\u2026\n- def update_msg(id, dlg=None, *, mergemeta: dict = None, export: bool = None): ... # Update a message's type, meta, or export directive in the dialog file (see `Message.update`)\u2026\n- def add_msg_magic(line, cell): ... # Add a new message with the magic body as its source, taken verbatim; a bare `export` token sets the meta `nbdev` export flag. [b069]\n- def nbrun_magic(line): ... # Run code cells from the current notebook (`set_dlg`) by id prefix; bare tokens are flags or id prefixes, `fname=` overrides, a bare line runs all [a932]\n- def load_ipython_extension(ipython) [52f8]\n\n## shared params:\n- **replace_params (from `msg_str_replace`):\n start_line: int = None, # Optional 1-based start line to limit search\n end_line: int = None, # Optional 1-based end line to limit search\n n_matches: int = None, # Max replacements (None=all)\n re_filter: str = None, # If provided, only process lines matching this regex (like g// in ex)\n invert_filter: bool = False, # Invert the filter (like g!// in ex)\n use_regex: bool = False, # Treat old_str as a regex, and new_str as an `re.sub` template?\n\n# module exhash.skill:\n\n\"\"\"Universal hash-verified text editing for local files. Use this for one reliable way to read, preview, and change text files.\n\nExhash's purpose is to make edits precise and easy to check. First view a file as `lineno|hash|text` (line numbers may be space-padded for alignment); then issue ex-style commands against those exact addresses. Every addressed line's hash is checked immediately before the command runs, so stale context or wrong targets fail instead of editing nearby text. Within one call, a single-line address may match the line's current content or its content at call start, so commands can stack on one line; across calls, re-view. Structural edits still shift lines as they apply, so work *backwards* (bottom-to-top).\n\nPrefer exhash over ad hoc patching for text file modifications, and prefer reading with `lnhashview_file` over plain file reads whenever an edit may follow: the view doubles as the address book, so the edit needs no second read.\n\nCore APIs:\n- `lnhashview_file` lists hashed lines.\n- `exhash` is the in-memory command engine; this docstring is the complete command reference, and `doc(exhash)` adds engine details (strict `s` matching, EditResult fields).\n- `file_exhash` is the file-aware engine; unqualified addresses use `path` and file-qualified addresses can edit or transfer across files.\n- `lnhashview_cell` views one notebook cell's source in an `.ipynb` file; `lnhashview_cells` views several explicit cells with `# cell ` headers. `cell_exhash` edits one cell.\n\nWorkflow:\n1. `lnhashview_file(...)`, ending the cell with the bare call: the result displays verbatim, one `lineno|hash|content` line each, so never join, print, or reformat it.\n2. Copy exact displayed `lineno|hash|` addresses.\n3. Use tuple command specs; pass each command as its own positional argument, e.g. `file_exhash(path, (addr1, \"d\"), (addr2, \"s\", pat, repl))`. Use raw triple-quoted Python strings for address, pattern, replacement, and inserted text when composing commands.\n4. Use `file_exhash(path, *cmds)` (or `cell_exhash(path, cell_id, *cmds)` for one notebook cell) to apply the edit: both write to disk and return a diff by default. Pass `inplace=False` to preview the result object without touching the file.\n\nOnce you hold lnhash addresses, `p` commands are the scattered view: one `(addr, \"p\")` per address, straight from an `rg` hit or a summary, gives back exactly those lines as verified rows. A `p`-only call writes nothing and returns a bare `lnhashview` of the printed lines (no diff headers, no tag column, no truncation), so its output is an address book that feeds the next edit directly, e.g. `file_exhash(path, (\"9|e08c|\", \"p\"), (\"18|2f61|\", \"p\"), (\"28|3cc8|\", \"p\"))`. `(\"%\", \"p\")` views a whole file this way, and `(addr, \"g\", pat, (\"p\",))` is scoped grep: a verified row per matching line in the range. Where a call also edits, its printed lines appear in the diff as context rows, always shown even when no hunk is near them.\n\nAddressing:\n Address strings use lnhash addresses: lineno|hash| where hash is a 4-char\n hex content hash. Use lnhashview to get addresses:\n lnhashview file.txt show all lines with addresses\n lnhashview file.txt 10 20 show lines 10-20\n With multiple commands, hashes are checked immediately before each command runs; a single-line address may also match that line's call-start hash.\n\n Single: 12|a3f2|\n Range: 12|a3f2|,15|b1c3|\n Last: $ (last line)\n Whole: % (whole file or cell, same as 1,$; no hashes needed)\n Special: 0|0000| targets before line 1 (only with a or i)\n\nTuple commands:\n (addr, \"s\", pat, repl[, flags]) Substitute (Rust regex syntax: backrefs in repl are $1/$0/${name}; a two-char \\1 stays literal; a literal $ is written $$, and $name followed by more text needs ${name}text -- an unknown group reference is an error). Flags: g=all, i=case-insensitive. pat/repl are verbatim: literal newlines, slashes, and backslashes all work. For $-heavy replacement text, prefer a c command: its text has no template layer at all.\n (addr, \"d\") Delete line(s)\n (addr, \"a\", text) Append text after line\n (addr, \"i\", text) Insert text before line\n (addr, \"c\", text) Change/replace with text\n (addr, \"j\") Join with next line; with range, joins all lines in range\n (addr, \"m\", dest) Move line(s) after dest address\n (addr, \"t\", dest) Copy line(s) after dest address\n (addr, \">\", n) Indent n levels (default 1, 4 spaces each)\n (addr, \"<\", n) Dedent n levels (default 1)\n (addr, \"sort\") Sort lines alphabetically\n (addr, \"p\") View line(s): emit them as verified `lineno|hash|` rows, changing nothing\n (addr, \"g\", pat, sub), (addr, \"g!\", pat, sub), (addr, \"v\", pat, sub) Global commands: apply `sub` to each addressed line matching `pat` (`g!`/`v`: not matching). `sub` is a subcommand tuple without an address, e.g. (\"d\",) or (\"s\", \"foo\", \"bar\", \"g\"); globals cannot nest.\n (addr, \"y\", source, dest) Transliterate `source` chars to `dest` (equal counts required)\n\n\nCut/copy/paste between files and notebook cells:\nAny `m` (cut+paste) or `t` (copy+paste) address can carry a target prefix: `path:` for another file, or `path.ipynb:cellid:` for one cell's source (`cellid` exact or unique prefix). This is THE way to transfer existing lines between locations: the lines never pass through your output, so hard-to-retype content (long encoded strings, hashes, long literals) cannot be mistyped. Take source addresses from `lnhashview_file`/`lnhashview_cell` of each target as usual:\n\n file_exhash(path, (\"src/a.py:10|aaaa|,20|bbbb|\", \"m\", \"src/b.py:$\")) # cut a.py lines 10-20, paste at end of b.py\n file_exhash(path, (\"nb.ipynb:ab12cd34:6|830e|\", \"t\", \"other.ipynb:9f8e:$\")) # copy one cell line into another notebook's cell\n file_exhash(path, (\"nb.ipynb:ab12cd34:%\", \"t\", \"snippets.py:0|0000|\")) # copy a whole cell's source into a new file\n\nA range must stay within one file or cell, and cells are never created by a transfer (files can be, via a `0|0000|` destination). For whole-cell structural operations (add/delete/move cells) use `pyskills.ipynb`'s `copy_cells`/`paste_cells` instead; this idiom is for line-level transfer.\n\nReformatting a section like `gq`, plus optional indents: `j` the range onto one line, re-view, then split with ONE g-flagged `s` alternating the tokens (picked by eye) that should start each new line, captured and restored with the break and indent in the replacement: `(addr, \"s\", r\", ('foo'|'bar'|'baz')\", \",\\n $1\", \"g\")`\nImportant:\nDo not pass raw commands to Python APIs. Do not create addresses by text search or remembered line numbers, and never construct them by computing hashes (e.g. via `line_hash`): addresses come only from a fresh view immediately before the edit. On stale hash, re-view and rebuild. Where rgapi is installed, hits from `rg(pattern, lnhashs=True)` count as fresh views too: their addresses drop straight into commands (and `nbrg` finds the cell ids that `lnhashview_cell` takes). If reaching an address seems to need arithmetic, hand-copying, or a guessed hash, a step on that route was skipped. Tuple text fields can contain newlines wherever the command accepts text. For example, `(addr, \"s\", \"foo\", \"bar\\nbaz\")` replaces one line with two. Text fields are taken verbatim: a two-character `\\n` sequence stays literal; use an actual newline when you want a line break. For `a`/`i`/`c`, put all text in one tuple field: `\"first\\nsecond\"` starts with `first`, while `\"\\nfirst\"` inserts a leading blank line before `first`. For moving/copying between files or cells, use the qualified `m`/`t` addresses shown above. Missing files can only be created through `(r\"0|0000|\", \"a\", text)` or `(r\"0|0000|\", \"i\", text)` creation semantics.\n\nThe `%%exhash` cell magic:\nIn IPython sessions, importing this module registers the `%%exhash` cell magic: `%%exhash []
` applies one command whose text is everything below the magic line, taken verbatim (one trailing newline stripped). Passing `` targets that cell in an .ipynb file instead of a plain file (`cell_exhash`); the magic dispatches on token count, so no separate cell magic exists. Because the text is never parsed as Python, no quoting or escaping applies. Use it for EVERY `a`/`i`/`c` command, however simple the text looks: create a file with `%%exhash path 0|0000| a`; replace a whole cell or file with `%%exhash [] % c` (`%` needs no hashes: a full replace has no neighboring lines to mis-hit); replace a region within one with a range address and `c` (`%%exhash 12|a3f2|,15|b1c3| c`), both addresses straight from the one pre-edit view. Tuple `a`/`i`/`c` text fields are only for contexts without magics (scripts, tests): interactively they add a Python quoting layer whose failure modes are not reliably foreseeable, so do not use them.\nIPython expands `{expr}` and `$var` in the magic line from the user namespace (its standard `var_expand` for all magics), so a path or cell id held in a variable needs no retyping: `%%exhash {path} {cid} % c`. Only the line expands; the text stays verbatim.\n\nDocument outlines (`open_doc`):\nThe outline layer makes exhash a hierarchical editor: `open_doc` opens a file (`fname=`, or a `Path` as `src`; recorded for `refresh()` and edits), a URL (an `https?://` str, fetched), or any other str as text in hand, and returns a `Section` tree. Markdown sections come from headings; code files (py, js, ts, tsx, rs, zig, swift) from tree-sitter definitions; `.ipynb` files from md-heading cells over cells. Display the root bare: fixed-width rows `token title (count) [size] preview` (code rows have no title: the def line, signature included, opens the preview), previews \u00b6-joined with links rendered `[text][n]` so no URL spends tokens. The token is the verified address: `1.2.|12|a3f2|,45|b1c3|` fuses the dotted addr (trailing dot; the root's is `.`) with the section's boundary lnhash pair, so one row serves navigation, editing, and orientation: `at()` verifies the first hash, and the pair drops straight into a `file_exhash` range command. `at()` accepts ONLY tokens copied from a listing (`'1.6.|12|a3f2|'` or the full range form); bare dotted addrs raise, teaching the idiom. Live traversal is free: `d[1][6]`, `find(title)`, `search(pat)` (counts and matching-line previews), `paths(depth)`, `links(pat)`, and `open(n)` opens link `n` as a new tree with `base` recorded (a non-md target comes back as a leaf whose `.src` holds its text). `view()` is the reading view: rendered text with links `[text][n]` (`.src` is the raw source), displayed verbatim bare; `view(*tokens)` reads several sections in one call, each under a `# token` header when more than one, so a listing's tokens replace a print loop; `nums=`/`lnhashs=` switch any view to stored lines with edit-ready addresses. Notebook section tokens carry the heading cell id (`1.2.|ab12cd34|8f3a|`), and `view(lnhashs=True)` rows are `cellid:lineno|hash|`, ready for `cell_exhash`. The whole llms.txt docs workflow: `toc = open_doc(url)`; `toc.links(topic)`; `page = toc.open(n)`; display `page` bare; `page.view()` whole when small, else `page.search(topic)` and `page.view(*tokens)`.\n\"\"\"\n\n## types:\n- class Section(dict): ... # One document section: numbered child sections, source span, and a verified address\n- class Sections(list): ... # Sections listed as fixed-width `token title (count) [size] preview` rows; each row is the live node. Code rows have no title: the def line opens the preview\n- class Link: ... # One inline link, numbered in document reading order\n- class Links(list): ... # A list of `Link` rows, displayed one per line\n\n## functions:\n- def line_hash(line: str) -> str: ... # Return a 4-char lowercase hex hash for a single line of text. [3ea3]\n- def lnhash(lineno: int, line: str) -> str: ... # Return an lnhash address ``lineno|hash|`` for ``line`` at 1-based ``lineno``. [f9d5]\n- def lnhashview(text: str, start: int = None, end: int = None) -> LnhashView: ... # Return lines formatted as space-padded ``lineno|hash|content``. Optional 1-based ``start``/``end`` filter the range; ``end`` past EOF is clamped. [eb78]\n- def lnhashview_file(path: str, start: int = None, end: int = None) -> LnhashView: ... # Return lines formatted as space-padded ``lineno|hash|content`` for file at ``path`` (expands ``~``). Optional 1-based ``start``/``end`` filter the range; ``end`` past EOF is clamped. [52c2]\n- def lnhashview_cell(path: str, cell_id: str, start: int = None, end: int = None) -> LnhashView: ... # Return lines formatted as ``lineno|hash|content`` for the source of notebook cell ``cell_id`` in ipynb file at ``path`` (expands ``~``). ``cell_id`` may be an exact id or unique prefix; optional 1-based ``start``/``end`` filter the range. [08f8]\n- def lnhashview_cells(path: str, *cell_ids: str, start: int = None, end: int = None) -> LnhashView: ... # Return grouped lnhash views for explicit notebook cell ids in the ipynb file at ``path`` (expands ``~``). Each group starts with ``# cell ``; following lines keep normal ``lineno|hash|content`` format. [b6bd]\n- def exhash(text: str, cmds: list[tuple], sw: int = 4): ... # Verified line-addressed editor. Apply commands to `text`, return an EditResult.\u2026\n- def file_exhash(path: str, *cmds: tuple, sw: int = 4, inplace: bool = True): ... # Read files and notebook cells, apply file-aware exhash commands, and return per-target results or a combined diff.\u2026\n- def cell_exhash(path: str, cell_id: str, *cmds: tuple, sw: int = 4, inplace: bool = True): ... # Apply exhash commands to the source of notebook cell ``cell_id`` in ipynb file at ``path`` (expands ``~``).\u2026\n- def open_doc(src: str | Path = None, rm_fenced=True, fname: str = None): ... # Open a document as a `Section` tree: a file (`fname`, or a `Path` in `src`; recorded for `refresh` and edits), a URL (fetched), or text\u2026\n\n# module rgapi.skill:\n\n\"\"\"Fast and flexible file finding and search for Python. Use this when code needs `fd`-style file finding or `rg`-style searching.\n\nrgapi wraps the same `ignore`, `grep-regex`, and `grep-searcher` crates ripgrep uses, so `.gitignore`/`.ignore`/`.rgignore`, dot-file handling, glob/ext filters, and regex matching all behave like `rg`. Walking and searching run in parallel and most work stays in Rust, so results come back as structured Python objects instead of CLI text to parse. Prefer rgapi over shelling out to `rg`/`fd` or scanning files by hand: you get typed rows, byte-offset match spans, and lazy iteration.\n\nCore APIs:\n- `fd(root=\".\", ...)` finds paths with fd-style filters (`pattern` smart-case basename regex, `include`/`exclude`/`glob`, `ext`); returns slash-separated relative paths as `FileEntry` rows: `str` subclasses that lazily stat themselves for `size`/`mtime`/`is_dir`/`stat`. The list displays as an `ls -l`-style table (capped at `rgapi.MAX_REPR` rows); `str(res)` or `list(res)` give plain paths. Unfollowed symlinks are included, displaying with an `l` mode character; `show_target=True` appends `-> target` to their rows, and `link_target` holds it (`None` for non-links).\n- `ls(root=\".\", ...)` lists like the shell `ls`: one level, directories included, ignore rules off, sorted by name. It is `fd` with different defaults, so every `fd` filter works; `hidden=True` is `ls -a`.\n- `rg(pattern, root=\".\", ...)` returns matching `SearchLine` rows. `summary=True` instead returns blank-line-delimited `SearchBlock` rows, with newline runs displayed as `\u00b6` and `maxlen` source characters shown per block. Context is line-based normally and block-based in summary mode. `paths=True` returns unique paths, `count=True` returns a match-span total, and `lnhashs=True` shows exhash addresses. `summary=True` is incompatible with `paths` and `count`, but combines with `lnhashs` to show copyable block boundaries.\n- `nbrg(pattern, root=\".\", cell_context=0, maxlen=180, ...)` searches Jupyter `.ipynb` files (cell source only) and returns matched cells as `NbResults`/`NbCell`; `paths=True` and `count=True` reduce like `rg`'s. Its display is always a one-line cell summary. Use this for notebooks rather than `rg`, to avoid escaped JSON and get stable cell ids. `multiline=True` lets a pattern match across lines within a cell (line-oriented `rg` rejects `\n` in patterns); `^`/`$` still match at line boundaries.\n- `rgstr(pattern, text, ...)` searches a string already in hand (a fetched log, a captured output, a doc): `rg`-style `SearchLine` rows and context from in-memory text, with no path label and no separate `compile` step. Reach for it instead of ad hoc `splitlines()` loops over held text.\n\nIdiomatic usage:\n- Results display through tuned reprs designed to be read as-is: end the cell with the bare call and read what comes back - never join, slice, or otherwise reformat a result by hand. If a result is too big to take in full, narrow it with the function's own parameters (`max_results`, `paths=True`, `count=True`, tighter filters), not by post-processing the output.\n- For orientation, start with `rg(pattern, summary=True)` and drop to line-level rows only where needed; add `lnhashs=True` when an edit may follow, so hits arrive with copyable addresses. This applies beyond unfamiliar code: on prose, config, and markdown, a summary row is the whole matched paragraph, usually exactly the read you wanted.\n- In kernel sessions, reach for `ls()` rather than shell `ls`, and `fd()` rather than shell `find`, for listings you or later calls will read.\n\nSearchLine rows:\n kind 'match', 'before', 'after', or 'context'\n path path relative to root\n line_number 1-based line number\n lnhash exhash-style `lineno|hash|` address\n line line text without the trailing newline\n matches list of (start, end) byte offsets, for 'match' rows\n asdict() returns the row fields as a plain dict\n\nSearchBlock rows (from `rg(summary=True)`):\n path/block_index/start_line/end_line/start_lnhash/end_lnhash locate the block\n kind 'match' or 'context'\n source full block source\n matches list of matching SearchLine rows within the block\n asdict() returns the row fields as a plain dict\nOutput uses `path:start-end:source` for matches and `path:start-end-source` for context. With `lnhashs=True`, `start-end` becomes `start_lnhash,end_lnhash` (or one hash for a one-line block). Empty or whitespace-only lines delimit blocks; `context=N` adds N neighbouring blocks. Multiple matches in one block produce one row.\n\nNbCell rows (from `nbrg`):\n path/cell_index/cell_id/cell_type locate the cell ('code'/'markdown'/'raw')\n kind 'match' or 'context'\n source full cell source\n matches list of SearchLine rows for the matched lines within the cell\n asdict() returns the cell fields as a plain dict\nOutput is keyed by `cell_id` (the nbformat cell/message id), not line number: `path:cell_id:source` for matches and `path:cell_id-source` for context. A match row starts at its first matched line: earlier lines display as `\u2026[Ln]` (`n` the matched line's 1-based number in the cell), except that a leading `#|` directive line is kept, e.g. `#| export\u2026[L4]needle here`. Newline runs display as `\u00b6` (following indentation kept) and `maxlen` limits displayed source without changing `source`. `cell_context=N` adds N neighbouring cells. Walking, parsing, and matching run in parallel in Rust; outputs and metadata are skipped.\n\nImportant:\nTraversal is parallel and result order is NOT guaranteed; wrap in `sorted(...)` if you need stable order. `path_re`/`skip_path_re` filter the returned/searched paths but do not prune traversal; use `skip_dir`/`skip_dir_re` to prune whole subtrees for speed. Run `doc(func)` for full parameter docments.\n\"\"\"\n\n## types:\n- class RgIter: ... # Initialize self. See help(type(self)) for accurate signature.\n\n## functions:\n- def fd(root: str | Path = '.', pattern: str | NoneType = None, files: bool = True, dirs: bool = False, show_target: bool = False, timeout_ms: int | NoneType = None, *, ext: str | list | NoneType = None, ignore: bool = True, max_depth: int | NoneType = None, **walk_params) -> PathResults: ... # Find paths with fd-style filters and gitignore support.\u2026\n- def ls(root: str | Path = '.', pattern: str | NoneType = None, dirs: bool = True, max_depth: int | NoneType = 1, ignore: bool = False, *, files: bool = True, show_target: bool = False, timeout_ms: int | NoneType = None, ext: str | list | NoneType = None, **walk_params) -> PathResults: ... # List a directory like `ls`: one level, directories included, ignore rules off, sorted by name.\u2026\n- def rg(pattern: str, root: str | Path = '.', case_sensitive: bool | NoneType = None, smart_case: bool = False, before_context: int = 0, after_context: int = 0, context: int = 0, paths: bool = False, count: bool = False, max_results: int | NoneType = None, lnhashs: bool = False, timeout_ms: int | NoneType = None, summary: bool = False, maxlen: int = 180, *, ext: str | list | NoneType = None, ignore: bool = True, max_depth: int | NoneType = None, **walk_params): ... # Search files and return `SearchResults`, matched paths, or a count; `lnhashs=True` shows exhash-style addresses.\u2026\n- def rg_iter(pattern: str, root: str | Path = '.', case_sensitive: bool | NoneType = None, smart_case: bool = False, before_context: int = 0, after_context: int = 0, context: int = 0, lnhashs: bool = False, *, ext: str | list | NoneType = None, ignore: bool = True, max_depth: int | NoneType = None, **walk_params) -> RgIter: ... # Search files lazily, yielding `SearchLine` rows; `lnhashs=True` shows exhash-style addresses.\u2026\n- def nbrg(pattern: str, root: str | Path = '.', cell_context: int = 0, case_sensitive: bool | NoneType = None, smart_case: bool = False, paths: bool = False, multiline: bool = False, max_results: int | NoneType = None, count: bool = False, timeout_ms: int | NoneType = None, maxlen: int = 180, *, ignore: bool = True, max_depth: int | NoneType = None, **walk_params): ... # Search `.ipynb` cell sources under `root` in parallel, returning matched cells, paths, or a count.\u2026\n- def rgstr(pattern: str, text: str, case_sensitive: bool | NoneType = None, smart_case: bool = False, *, before_context: int = 0, after_context: int = 0, context: int = 0) -> SearchResults: ... # Search text already in hand, like `rg` on a string: no path label, no separate `compile`.\u2026\n\n## shared params:\n- **walk_params (from `fd`):\n glob: str | list | NoneType = None, # Include glob or globs; alias for `include`\n include: str | list | NoneType = None, # Include glob or globs, e.g. `*.py`\n exclude: str | list | NoneType = None, # Exclude glob or globs, e.g. `test_*.py`\n hidden: bool = False, # Include hidden files and directories\n min_depth: int | NoneType = None, # Minimum depth for returned/searched paths\n max_filesize: int | NoneType = None, # Skip files larger than this many bytes\n follow_links: bool = False, # Follow symbolic links while walking\n same_file_system: bool = False, # Do not cross filesystem boundaries\n path_re: str | NoneType = None, # Regex that relative paths must match\n skip_path_re: str | NoneType = None, # Regex for relative paths to skip\n skip_dir: str | list | NoneType = None, # Directory glob or globs to prune\n skip_dir_re: str | NoneType = None, # Directory regex used to prune traversal"}]}, "toolUseResult": [{"type": "text", "text": "# module aidialog.dlgskill:\n\n\"\"\"Read, search, and edit dialogs and notebooks through aidialog's `Dialog` and `Message` classes\n\nUse this whenever the question or edit concerns a notebook's content: messages, sources, outputs, prompts and their replies.\n\n## The hierarchy\n\nNotebook work happens at three levels, and picking the right level is most of using this module well:\n\n- Content (this module): what the messages say and how they change. `summary_dlg`, `find_msgs`, `view_dlg`, the message editing operations.\n- Representation (`fastcore.nbio`): which keys exist, whether a file is schema-valid, whether bytes changed. Start with `validate_nb`/`validate_cell`, which name the cell at fault; use `read_nb` directly when the question is about the dict itself. For *plain* notebooks (no dialog semantics), nbio's `Notebook`/`NbCell` objects and `cell_*` functions are the content surface, and this module's word choice marks the layer: cells for notebooks, messages for dialogs.\n- Raw text: only when the file will not parse at all.\n\n## Core APIs\n\nThe function/method two-shapes contract is `fastcore.editskill`'s, learned once: the function is a transaction addressed by `dlg=` (an ipynb path, or None meaning the current dialog file: `set_dlg`); the method is a session on a held `Dialog` or `Message`, saved by an explicit `dlg.save()` (`msg_str_replace(id, ..., dlg=p)` \u27f7 `m.str_replace(...)`). Wrapping any message list in an ephemeral `Dialog(msgs)` makes the whole session surface available on it.\n\n- `summary_dlg(dlg)` / `d.summary()`: one `preview` per message, `id:t[directives]:content` (t: c=code n=note p=prompt r=raw; the bracket, as in nbio's `CellRow`, shows meta-form nbdev directives such as `[export]`), with a prompt's reply on a following `> ` line; a line cut short ends in a humanized `[size]`.\n- `find_msgs(pattern, dlg, ...)`: search by regex, type, errors, heading, ids, or a `pred` (`symdef_finder`/`symref_finder`/`ast_finder` build structural ones); `context` defaults to 1 even with `ids=`, because the neighbouring message usually explains the match -- retrieve and read it. Returns `MsgRow` snapshots (`id`, `msg_type`, `content`, `out`, `meta`) in a `MsgRows`; `d.find_msgs(...)` returns live `Message`s in a `FoundMsgs`. Both index by message id (exact or unique prefix) and refuse integer positions: a find result includes context rows, so `[0]` may be a neighbour of the match, and the raised error teaches the id idiom. Previews mark context rows with `-` in place of the first `:`. Every live message carries a `dlg` backref to its owning `Dialog`, so dialog-level operations are always in reach from a message in hand (e.g. `m.dlg.save()` after mutating `m.output` directly).\n- `view_dlg(dlg)` / `d.view()` / `view_msg(id)` / `m.view()` / `view_msgs(*ids)` / `msg2xml(m)` / `m.to_xml()`: full views in the shared `item2xml` grammar (a prompt's reply is its `` section; meta `nbdev` directives render as attrs, so a meta-exported message carries a bare `export`); `incl_out=True` on the line views appends the message's output the same way.\n- Structure: `add_msg`, `del_msgs`, `move_msgs`, `split_msg`, `merge_msgs`, `copy_msgs`/`cut_msgs`/`paste_msgs`, `create_dlg`, with session twins `d.move_msgs`, `m.split`, `d.merge_msgs`, `d.copy_msgs`/`d.cut_msgs`/`d.paste_msgs` (session adds go through `d.mk_message`, deletes through `d.remove_msgs`). `add_msg` with neither `before=` nor `after=` places after the current message (`set_cur_msg`, which a host sets and no write ever moves), or at the end when that is unset or gone. `add_msg` and `d.mk_message` take `meta=` and an `export=` shortcut for the meta `nbdev` export flag, readable and assignable as `m.meta_exported` (`m.exported` reads content and meta together, and is what `find_msgs(only_exp=True)` filters on). The `%%add_msg` magic takes its body verbatim: its line is `%%add_msg [dlg] [msg_type] [export] [before=|after=]`, where a bare path token is the dlg and a bare type name the msg_type, and keyword spellings win over bare tokens.\n- `update_msg(id, ..., dlg=)` / `m.update(...)`: one transaction for a message's attributes. Plain keywords assign (`content=`, `msg_type=`, `output=`, `meta=` replacing wholesale); `mergemeta` deep-merges into `meta` (a `None` value deletes its key); `export` sets the nbdev directive -- `True` adds, `False` removes (there is no negative form), and either migrates a content-form `#| export` line to meta. The function returns a diff of the message's XML plus a `meta:` line, so every change it can make is visible.\n- The `%nbrun` line magic runs code cells from the current notebook in the running kernel, by cell id prefix, same grammar: bare tokens are flags (`above`, `below`, `all`, `exported`, `ignore_eval`, `continue_on_error`, `show`) or id prefixes, and `fname=` overrides the notebook for one call. Only cells named by id display their output: bulk-selected cells run silently (`show` overrides), errors always surface naming the failed cell, and the run ends with a `nbrun: N cells ok` line. It returns a coroutine (awaited by async-magic machinery) and runs each cell in the kernel's namespace through user-level channels only (exec, `display()`, raising), so it behaves identically in every kernel.\n- `d.execute(*ids, ...)` / `m.execute()`: the captured twin, on a held `Dialog`. Runs code messages on an execnb `CaptureShell` (fresh and dropped, unless you pass `shell=`), assigns each result to `m.output` in memory (`d.save()` persists), and returns the messages run as a `RunResult` whose repr is a status report: ok lines collapse into a counted marker, failures always show with the exception. It blocks until done, so an async host should wrap it in `asyncio.to_thread`. `above`/`below` anchor at the ids you pass, and bare `d.execute()` runs all participating code messages (the `eval` cascade).\n- Text edits: `msg_str_replace`, `msg_strs_replace`, `msg_insert_line`, `msg_replace_lines`, `msg_del_lines`, `msg_ast_replace` (all with `re_filter`/line-range powers; `out=True` edits a prompt's reply or a code message's outputs literal), with the same names as `Message` methods for in-memory editing; `lnhashview_msg`/`msg_exhash` (and `m.lnhashview()`/`m.exhash()`) for hash-verified line edits (`lnhashview_msg` is `view_msg(..., lnhashs=True)`; only the exhash pair needs the `exhash` package).\n\n## Idiomatic usage\n\nStart by registering the notebook: `set_dlg(path)` makes every function here default to it (and the `%nbrun` magic follows suit), so calls read as \"do this to message X\". Then orient before acting:\n\n- `summary_dlg()` first for anything sizable: a cheap one-line-per-message map. `view_dlg()` when you need the full story in order with ids. Read a notebook in full before describing or changing what it does - the interleaved prose, examples, and stored outputs (`incl_out=True`) are the design rationale.\n- Read before trying things out: an ad-hoc \"what happens if...\" check usually re-derives, more slowly and less reliably, what an existing example cell already shows. If after reading you still need to try it, that's a gap in the notebook - add it as a proper example cell so the next reader doesn't repeat it.\n- `find_msgs` is the targeted view: keep the default `context=1` (the neighbouring markdown usually explains the match), and `ids=` with context is the positional query (\"does A precede B?\", \"what's the idiom around here?\"). Name questions are structural, not textual: `symdef_finder`/`symref_finder`/`ast_finder` beat regexes over binding syntax. Where available, rgapi's `nbrg` searches cell sources across files and returns the cell ids these functions take.\n- Edit at the right level: `add_msg`/`%%add_msg` for new messages; within a message, prefer hash-verified addressing (`lnhashview_msg`/`msg_exhash`, or exhash's `lnhashview_cell`/`cell_exhash` by path and cell id) - view with the lnhash variant the moment an edit is plausible, so the view doubles as the edit's address book. The plain `msg_*` editors fit where exhash can't express the edit, e.g. one `msg_str_replace` across an id list. Never splice via `read_nb`/`write_nb` internals: if a primitive is missing here, stop and propose adding it.\n- `dlg.validate()` catches structural problems early; `dlg.save()` writes back. Don't wrap calls in `hasattr` or `try/except` - call directly and read the bare result.\n\nDocs: https://AnswerDotAI.github.io/aidialog/dlgskill.html.md\n\"\"\"\n\n## types:\n- class FoundMsgs(Found,Msgs,L,\u2026): ... # Find results: live messages indexed by id (exact or unique prefix), never by position; context rows show `-` in place of their first `:`\n\n## functions:\n- def msg_insert_line(id, insert_line: int, new_str: str, *, dlg=None, out: bool = False): ... # Insert new_str at specified line number\u2026\n- def msg_str_replace(id, old_str: str, new_str: str, *, dlg=None, out: bool = False, **replace_params): ... # Replace occurrence(s) of old_str with new_str\u2026\n- def msg_strs_replace(id, old_strs: list[str], new_strs: list[str], *, dlg=None, out: bool = False, **replace_params): ... # Replace multiple strings simultaneously\u2026\n- def msg_replace_lines(id, start_line: int = None, end_line: int = None, new_content: str = '', *, dlg=None, out: bool = False): ... # Replace line range with new content; the defaults replace the entire contents\u2026\n- def msg_del_lines(id, start_line: int, end_line: int, re_filter: str = None, invert_filter: bool = False, *, dlg=None, out: bool = False): ... # Delete line range; deletion is destructive, so both line numbers must be given explicitly (`1, -1` for all lines)\u2026\n- def msg_ast_replace(id, repls: list, *, dlg=None, out: bool = False): ... # Apply ast-grep structural pattern replacements to python source\u2026\n- def set_dlg(fname, cls=None): ... # Set the current dialog file, used by these functions when `dlg` is None\u2026\n- def cur_dlg(): ... # A fresh `Dialog` read from the current dialog file (None if unset): the session-side entry to the ambient default [9e42]\n- def set_cur_msg(id): ... # Set the current message, used by `add_msg` when no placement is given\u2026\n- def cur_msg(): ... # The current message, read fresh from the current dialog file (None if unset, or no longer there) [18ee]\n- def summary_dlg(dlg=None, maxlen: int = 180): ... # One `preview` per message of the dialog file\u2026\n- def view_dlg(dlg=None, incl_out: bool = False, only_errors: bool = False, trunc_out: bool = True): ... # The dialog file as concise XML; meta-exported messages carry a bare `export` attr\u2026\n- def view_msg(id, dlg=None, nums: bool = True, start_line: int = 1, end_line: int = None, lnhashs: bool = False, incl_out: bool = False, trunc_out: bool = True): ... # Show a message's content with optional line numbers or exhash addresses\u2026\n- def view_msgs(*ids, dlg=None, nums: bool = True, start_line: int = 1, end_line: int = None, lnhashs: bool = False, incl_out: bool = False, trunc_out: bool = True): ... # Show several messages, each preceded by a `# msg ` header\u2026\n- def find_msgs(re_pattern: str = '', dlg=None, *, msg_type: str = None, only_err: bool = False, only_exp: bool = False, ids='', before: int = 0, after: int = 0, context: int = None, limit: int = None, use_case: bool = False, use_regex: bool = True, headers_only: bool = False, header_section: str = None, pred: callable = None) -> MsgRows: ... # Find messages in the dialog file matching all the given criteria; for live results, call `Dialog.find_msgs`\u2026\n- def move_msgs(ids, before=None, after=None, dlg=None): ... # Move messages in the dialog file, keeping their relative order; returns them\u2026\n- def split_msg(id, *linenos: int, dlg=None): ... # Split a message in the dialog file (see `Message.split`)\u2026\n- def merge_msgs(*ids, dlg=None): ... # Merge messages in the dialog file (see `Dialog.merge_msgs`)\u2026\n- def copy_msgs(*ids, dlg=None): ... # Copy messages into the paste buffer (replacing its contents), for later `paste_msgs`\u2026\n- def cut_msgs(*ids, dlg=None): ... # Copy messages into the paste buffer, then remove them from the dialog file\u2026\n- def paste_msgs(before=None, after=None, dlg=None): ... # Insert copies of the buffered messages (fresh ids) before/after a message or id in the dialog file; returns the new messages\u2026\n- def symdef_finder(name): ... # `find_msgs` predicate: does the message bind `name` at cell top level? (requires `remold`) [a533]\n- def symref_finder(name): ... # `find_msgs` predicate: does the message reference `name`? (requires `remold`) [3810]\n- def ast_finder(pattern): ... # `find_msgs` predicate: does ast-grep `pattern` match in the message? (requires `remold`) [3b44]\n- def lnhashview_msg(id, dlg=None): ... # Hash-addressed view of a message's content, for `msg_exhash`\u2026\n- def msg_exhash(id, *cmds: tuple, sw: int = 4, dlg=None): ... # Apply exhash commands to a message in the dialog file, returning the diff\u2026\n- def add_msg(source: str, msg_type: str = 'code', before: str = None, after: str = None, meta=None, export=False, dlg=None): ... # Add a new message before or after an existing one (default: after the current message, else at the end), returning it\u2026\n- def del_msgs(*ids, dlg=None): ... # Delete messages by id, returning the removed messages\u2026\n- def create_dlg(fname: str, source: str = '', msg_type: str = 'code'): ... # Create a new dialog file containing one message, returning the `Dialog` (with `path_` stamped)\u2026\n- def update_msg(id, dlg=None, *, mergemeta: dict = None, export: bool = None): ... # Update a message's type, meta, or export directive in the dialog file (see `Message.update`)\u2026\n- def add_msg_magic(line, cell): ... # Add a new message with the magic body as its source, taken verbatim; a bare `export` token sets the meta `nbdev` export flag. [b069]\n- def nbrun_magic(line): ... # Run code cells from the current notebook (`set_dlg`) by id prefix; bare tokens are flags or id prefixes, `fname=` overrides, a bare line runs all [a932]\n- def load_ipython_extension(ipython) [52f8]\n\n## shared params:\n- **replace_params (from `msg_str_replace`):\n start_line: int = None, # Optional 1-based start line to limit search\n end_line: int = None, # Optional 1-based end line to limit search\n n_matches: int = None, # Max replacements (None=all)\n re_filter: str = None, # If provided, only process lines matching this regex (like g// in ex)\n invert_filter: bool = False, # Invert the filter (like g!// in ex)\n use_regex: bool = False, # Treat old_str as a regex, and new_str as an `re.sub` template?\n\n# module exhash.skill:\n\n\"\"\"Universal hash-verified text editing for local files. Use this for one reliable way to read, preview, and change text files.\n\nExhash's purpose is to make edits precise and easy to check. First view a file as `lineno|hash|text` (line numbers may be space-padded for alignment); then issue ex-style commands against those exact addresses. Every addressed line's hash is checked immediately before the command runs, so stale context or wrong targets fail instead of editing nearby text. Within one call, a single-line address may match the line's current content or its content at call start, so commands can stack on one line; across calls, re-view. Structural edits still shift lines as they apply, so work *backwards* (bottom-to-top).\n\nPrefer exhash over ad hoc patching for text file modifications, and prefer reading with `lnhashview_file` over plain file reads whenever an edit may follow: the view doubles as the address book, so the edit needs no second read.\n\nCore APIs:\n- `lnhashview_file` lists hashed lines.\n- `exhash` is the in-memory command engine; this docstring is the complete command reference, and `doc(exhash)` adds engine details (strict `s` matching, EditResult fields).\n- `file_exhash` is the file-aware engine; unqualified addresses use `path` and file-qualified addresses can edit or transfer across files.\n- `lnhashview_cell` views one notebook cell's source in an `.ipynb` file; `lnhashview_cells` views several explicit cells with `# cell ` headers. `cell_exhash` edits one cell.\n\nWorkflow:\n1. `lnhashview_file(...)`, ending the cell with the bare call: the result displays verbatim, one `lineno|hash|content` line each, so never join, print, or reformat it.\n2. Copy exact displayed `lineno|hash|` addresses.\n3. Use tuple command specs; pass each command as its own positional argument, e.g. `file_exhash(path, (addr1, \"d\"), (addr2, \"s\", pat, repl))`. Use raw triple-quoted Python strings for address, pattern, replacement, and inserted text when composing commands.\n4. Use `file_exhash(path, *cmds)` (or `cell_exhash(path, cell_id, *cmds)` for one notebook cell) to apply the edit: both write to disk and return a diff by default. Pass `inplace=False` to preview the result object without touching the file.\n\nOnce you hold lnhash addresses, `p` commands are the scattered view: one `(addr, \"p\")` per address, straight from an `rg` hit or a summary, gives back exactly those lines as verified rows. A `p`-only call writes nothing and returns a bare `lnhashview` of the printed lines (no diff headers, no tag column, no truncation), so its output is an address book that feeds the next edit directly, e.g. `file_exhash(path, (\"9|e08c|\", \"p\"), (\"18|2f61|\", \"p\"), (\"28|3cc8|\", \"p\"))`. `(\"%\", \"p\")` views a whole file this way, and `(addr, \"g\", pat, (\"p\",))` is scoped grep: a verified row per matching line in the range. Where a call also edits, its printed lines appear in the diff as context rows, always shown even when no hunk is near them.\n\nAddressing:\n Address strings use lnhash addresses: lineno|hash| where hash is a 4-char\n hex content hash. Use lnhashview to get addresses:\n lnhashview file.txt show all lines with addresses\n lnhashview file.txt 10 20 show lines 10-20\n With multiple commands, hashes are checked immediately before each command runs; a single-line address may also match that line's call-start hash.\n\n Single: 12|a3f2|\n Range: 12|a3f2|,15|b1c3|\n Last: $ (last line)\n Whole: % (whole file or cell, same as 1,$; no hashes needed)\n Special: 0|0000| targets before line 1 (only with a or i)\n\nTuple commands:\n (addr, \"s\", pat, repl[, flags]) Substitute (Rust regex syntax: backrefs in repl are $1/$0/${name}; a two-char \\1 stays literal; a literal $ is written $$, and $name followed by more text needs ${name}text -- an unknown group reference is an error). Flags: g=all, i=case-insensitive. pat/repl are verbatim: literal newlines, slashes, and backslashes all work. For $-heavy replacement text, prefer a c command: its text has no template layer at all.\n (addr, \"d\") Delete line(s)\n (addr, \"a\", text) Append text after line\n (addr, \"i\", text) Insert text before line\n (addr, \"c\", text) Change/replace with text\n (addr, \"j\") Join with next line; with range, joins all lines in range\n (addr, \"m\", dest) Move line(s) after dest address\n (addr, \"t\", dest) Copy line(s) after dest address\n (addr, \">\", n) Indent n levels (default 1, 4 spaces each)\n (addr, \"<\", n) Dedent n levels (default 1)\n (addr, \"sort\") Sort lines alphabetically\n (addr, \"p\") View line(s): emit them as verified `lineno|hash|` rows, changing nothing\n (addr, \"g\", pat, sub), (addr, \"g!\", pat, sub), (addr, \"v\", pat, sub) Global commands: apply `sub` to each addressed line matching `pat` (`g!`/`v`: not matching). `sub` is a subcommand tuple without an address, e.g. (\"d\",) or (\"s\", \"foo\", \"bar\", \"g\"); globals cannot nest.\n (addr, \"y\", source, dest) Transliterate `source` chars to `dest` (equal counts required)\n\n\nCut/copy/paste between files and notebook cells:\nAny `m` (cut+paste) or `t` (copy+paste) address can carry a target prefix: `path:` for another file, or `path.ipynb:cellid:` for one cell's source (`cellid` exact or unique prefix). This is THE way to transfer existing lines between locations: the lines never pass through your output, so hard-to-retype content (long encoded strings, hashes, long literals) cannot be mistyped. Take source addresses from `lnhashview_file`/`lnhashview_cell` of each target as usual:\n\n file_exhash(path, (\"src/a.py:10|aaaa|,20|bbbb|\", \"m\", \"src/b.py:$\")) # cut a.py lines 10-20, paste at end of b.py\n file_exhash(path, (\"nb.ipynb:ab12cd34:6|830e|\", \"t\", \"other.ipynb:9f8e:$\")) # copy one cell line into another notebook's cell\n file_exhash(path, (\"nb.ipynb:ab12cd34:%\", \"t\", \"snippets.py:0|0000|\")) # copy a whole cell's source into a new file\n\nA range must stay within one file or cell, and cells are never created by a transfer (files can be, via a `0|0000|` destination). For whole-cell structural operations (add/delete/move cells) use `pyskills.ipynb`'s `copy_cells`/`paste_cells` instead; this idiom is for line-level transfer.\n\nReformatting a section like `gq`, plus optional indents: `j` the range onto one line, re-view, then split with ONE g-flagged `s` alternating the tokens (picked by eye) that should start each new line, captured and restored with the break and indent in the replacement: `(addr, \"s\", r\", ('foo'|'bar'|'baz')\", \",\\n $1\", \"g\")`\nImportant:\nDo not pass raw commands to Python APIs. Do not create addresses by text search or remembered line numbers, and never construct them by computing hashes (e.g. via `line_hash`): addresses come only from a fresh view immediately before the edit. On stale hash, re-view and rebuild. Where rgapi is installed, hits from `rg(pattern, lnhashs=True)` count as fresh views too: their addresses drop straight into commands (and `nbrg` finds the cell ids that `lnhashview_cell` takes). If reaching an address seems to need arithmetic, hand-copying, or a guessed hash, a step on that route was skipped. Tuple text fields can contain newlines wherever the command accepts text. For example, `(addr, \"s\", \"foo\", \"bar\\nbaz\")` replaces one line with two. Text fields are taken verbatim: a two-character `\\n` sequence stays literal; use an actual newline when you want a line break. For `a`/`i`/`c`, put all text in one tuple field: `\"first\\nsecond\"` starts with `first`, while `\"\\nfirst\"` inserts a leading blank line before `first`. For moving/copying between files or cells, use the qualified `m`/`t` addresses shown above. Missing files can only be created through `(r\"0|0000|\", \"a\", text)` or `(r\"0|0000|\", \"i\", text)` creation semantics.\n\nThe `%%exhash` cell magic:\nIn IPython sessions, importing this module registers the `%%exhash` cell magic: `%%exhash []
` applies one command whose text is everything below the magic line, taken verbatim (one trailing newline stripped). Passing `` targets that cell in an .ipynb file instead of a plain file (`cell_exhash`); the magic dispatches on token count, so no separate cell magic exists. Because the text is never parsed as Python, no quoting or escaping applies. Use it for EVERY `a`/`i`/`c` command, however simple the text looks: create a file with `%%exhash path 0|0000| a`; replace a whole cell or file with `%%exhash [] % c` (`%` needs no hashes: a full replace has no neighboring lines to mis-hit); replace a region within one with a range address and `c` (`%%exhash 12|a3f2|,15|b1c3| c`), both addresses straight from the one pre-edit view. Tuple `a`/`i`/`c` text fields are only for contexts without magics (scripts, tests): interactively they add a Python quoting layer whose failure modes are not reliably foreseeable, so do not use them.\nIPython expands `{expr}` and `$var` in the magic line from the user namespace (its standard `var_expand` for all magics), so a path or cell id held in a variable needs no retyping: `%%exhash {path} {cid} % c`. Only the line expands; the text stays verbatim.\n\nDocument outlines (`open_doc`):\nThe outline layer makes exhash a hierarchical editor: `open_doc` opens a file (`fname=`, or a `Path` as `src`; recorded for `refresh()` and edits), a URL (an `https?://` str, fetched), or any other str as text in hand, and returns a `Section` tree. Markdown sections come from headings; code files (py, js, ts, tsx, rs, zig, swift) from tree-sitter definitions; `.ipynb` files from md-heading cells over cells. Display the root bare: fixed-width rows `token title (count) [size] preview` (code rows have no title: the def line, signature included, opens the preview), previews \u00b6-joined with links rendered `[text][n]` so no URL spends tokens. The token is the verified address: `1.2.|12|a3f2|,45|b1c3|` fuses the dotted addr (trailing dot; the root's is `.`) with the section's boundary lnhash pair, so one row serves navigation, editing, and orientation: `at()` verifies the first hash, and the pair drops straight into a `file_exhash` range command. `at()` accepts ONLY tokens copied from a listing (`'1.6.|12|a3f2|'` or the full range form); bare dotted addrs raise, teaching the idiom. Live traversal is free: `d[1][6]`, `find(title)`, `search(pat)` (counts and matching-line previews), `paths(depth)`, `links(pat)`, and `open(n)` opens link `n` as a new tree with `base` recorded (a non-md target comes back as a leaf whose `.src` holds its text). `view()` is the reading view: rendered text with links `[text][n]` (`.src` is the raw source), displayed verbatim bare; `view(*tokens)` reads several sections in one call, each under a `# token` header when more than one, so a listing's tokens replace a print loop; `nums=`/`lnhashs=` switch any view to stored lines with edit-ready addresses. Notebook section tokens carry the heading cell id (`1.2.|ab12cd34|8f3a|`), and `view(lnhashs=True)` rows are `cellid:lineno|hash|`, ready for `cell_exhash`. The whole llms.txt docs workflow: `toc = open_doc(url)`; `toc.links(topic)`; `page = toc.open(n)`; display `page` bare; `page.view()` whole when small, else `page.search(topic)` and `page.view(*tokens)`.\n\"\"\"\n\n## types:\n- class Section(dict): ... # One document section: numbered child sections, source span, and a verified address\n- class Sections(list): ... # Sections listed as fixed-width `token title (count) [size] preview` rows; each row is the live node. Code rows have no title: the def line opens the preview\n- class Link: ... # One inline link, numbered in document reading order\n- class Links(list): ... # A list of `Link` rows, displayed one per line\n\n## functions:\n- def line_hash(line: str) -> str: ... # Return a 4-char lowercase hex hash for a single line of text. [3ea3]\n- def lnhash(lineno: int, line: str) -> str: ... # Return an lnhash address ``lineno|hash|`` for ``line`` at 1-based ``lineno``. [f9d5]\n- def lnhashview(text: str, start: int = None, end: int = None) -> LnhashView: ... # Return lines formatted as space-padded ``lineno|hash|content``. Optional 1-based ``start``/``end`` filter the range; ``end`` past EOF is clamped. [eb78]\n- def lnhashview_file(path: str, start: int = None, end: int = None) -> LnhashView: ... # Return lines formatted as space-padded ``lineno|hash|content`` for file at ``path`` (expands ``~``). Optional 1-based ``start``/``end`` filter the range; ``end`` past EOF is clamped. [52c2]\n- def lnhashview_cell(path: str, cell_id: str, start: int = None, end: int = None) -> LnhashView: ... # Return lines formatted as ``lineno|hash|content`` for the source of notebook cell ``cell_id`` in ipynb file at ``path`` (expands ``~``). ``cell_id`` may be an exact id or unique prefix; optional 1-based ``start``/``end`` filter the range. [08f8]\n- def lnhashview_cells(path: str, *cell_ids: str, start: int = None, end: int = None) -> LnhashView: ... # Return grouped lnhash views for explicit notebook cell ids in the ipynb file at ``path`` (expands ``~``). Each group starts with ``# cell ``; following lines keep normal ``lineno|hash|content`` format. [b6bd]\n- def exhash(text: str, cmds: list[tuple], sw: int = 4): ... # Verified line-addressed editor. Apply commands to `text`, return an EditResult.\u2026\n- def file_exhash(path: str, *cmds: tuple, sw: int = 4, inplace: bool = True): ... # Read files and notebook cells, apply file-aware exhash commands, and return per-target results or a combined diff.\u2026\n- def cell_exhash(path: str, cell_id: str, *cmds: tuple, sw: int = 4, inplace: bool = True): ... # Apply exhash commands to the source of notebook cell ``cell_id`` in ipynb file at ``path`` (expands ``~``).\u2026\n- def open_doc(src: str | Path = None, rm_fenced=True, fname: str = None): ... # Open a document as a `Section` tree: a file (`fname`, or a `Path` in `src`; recorded for `refresh` and edits), a URL (fetched), or text\u2026\n\n# module rgapi.skill:\n\n\"\"\"Fast and flexible file finding and search for Python. Use this when code needs `fd`-style file finding or `rg`-style searching.\n\nrgapi wraps the same `ignore`, `grep-regex`, and `grep-searcher` crates ripgrep uses, so `.gitignore`/`.ignore`/`.rgignore`, dot-file handling, glob/ext filters, and regex matching all behave like `rg`. Walking and searching run in parallel and most work stays in Rust, so results come back as structured Python objects instead of CLI text to parse. Prefer rgapi over shelling out to `rg`/`fd` or scanning files by hand: you get typed rows, byte-offset match spans, and lazy iteration.\n\nCore APIs:\n- `fd(root=\".\", ...)` finds paths with fd-style filters (`pattern` smart-case basename regex, `include`/`exclude`/`glob`, `ext`); returns slash-separated relative paths as `FileEntry` rows: `str` subclasses that lazily stat themselves for `size`/`mtime`/`is_dir`/`stat`. The list displays as an `ls -l`-style table (capped at `rgapi.MAX_REPR` rows); `str(res)` or `list(res)` give plain paths. Unfollowed symlinks are included, displaying with an `l` mode character; `show_target=True` appends `-> target` to their rows, and `link_target` holds it (`None` for non-links).\n- `ls(root=\".\", ...)` lists like the shell `ls`: one level, directories included, ignore rules off, sorted by name. It is `fd` with different defaults, so every `fd` filter works; `hidden=True` is `ls -a`.\n- `rg(pattern, root=\".\", ...)` returns matching `SearchLine` rows. `summary=True` instead returns blank-line-delimited `SearchBlock` rows, with newline runs displayed as `\u00b6` and `maxlen` source characters shown per block. Context is line-based normally and block-based in summary mode. `paths=True` returns unique paths, `count=True` returns a match-span total, and `lnhashs=True` shows exhash addresses. `summary=True` is incompatible with `paths` and `count`, but combines with `lnhashs` to show copyable block boundaries.\n- `nbrg(pattern, root=\".\", cell_context=0, maxlen=180, ...)` searches Jupyter `.ipynb` files (cell source only) and returns matched cells as `NbResults`/`NbCell`; `paths=True` and `count=True` reduce like `rg`'s. Its display is always a one-line cell summary. Use this for notebooks rather than `rg`, to avoid escaped JSON and get stable cell ids. `multiline=True` lets a pattern match across lines within a cell (line-oriented `rg` rejects `\n` in patterns); `^`/`$` still match at line boundaries.\n- `rgstr(pattern, text, ...)` searches a string already in hand (a fetched log, a captured output, a doc): `rg`-style `SearchLine` rows and context from in-memory text, with no path label and no separate `compile` step. Reach for it instead of ad hoc `splitlines()` loops over held text.\n\nIdiomatic usage:\n- Results display through tuned reprs designed to be read as-is: end the cell with the bare call and read what comes back - never join, slice, or otherwise reformat a result by hand. If a result is too big to take in full, narrow it with the function's own parameters (`max_results`, `paths=True`, `count=True`, tighter filters), not by post-processing the output.\n- For orientation, start with `rg(pattern, summary=True)` and drop to line-level rows only where needed; add `lnhashs=True` when an edit may follow, so hits arrive with copyable addresses. This applies beyond unfamiliar code: on prose, config, and markdown, a summary row is the whole matched paragraph, usually exactly the read you wanted.\n- In kernel sessions, reach for `ls()` rather than shell `ls`, and `fd()` rather than shell `find`, for listings you or later calls will read.\n\nSearchLine rows:\n kind 'match', 'before', 'after', or 'context'\n path path relative to root\n line_number 1-based line number\n lnhash exhash-style `lineno|hash|` address\n line line text without the trailing newline\n matches list of (start, end) byte offsets, for 'match' rows\n asdict() returns the row fields as a plain dict\n\nSearchBlock rows (from `rg(summary=True)`):\n path/block_index/start_line/end_line/start_lnhash/end_lnhash locate the block\n kind 'match' or 'context'\n source full block source\n matches list of matching SearchLine rows within the block\n asdict() returns the row fields as a plain dict\nOutput uses `path:start-end:source` for matches and `path:start-end-source` for context. With `lnhashs=True`, `start-end` becomes `start_lnhash,end_lnhash` (or one hash for a one-line block). Empty or whitespace-only lines delimit blocks; `context=N` adds N neighbouring blocks. Multiple matches in one block produce one row.\n\nNbCell rows (from `nbrg`):\n path/cell_index/cell_id/cell_type locate the cell ('code'/'markdown'/'raw')\n kind 'match' or 'context'\n source full cell source\n matches list of SearchLine rows for the matched lines within the cell\n asdict() returns the cell fields as a plain dict\nOutput is keyed by `cell_id` (the nbformat cell/message id), not line number: `path:cell_id:source` for matches and `path:cell_id-source` for context. A match row starts at its first matched line: earlier lines display as `\u2026[Ln]` (`n` the matched line's 1-based number in the cell), except that a leading `#|` directive line is kept, e.g. `#| export\u2026[L4]needle here`. Newline runs display as `\u00b6` (following indentation kept) and `maxlen` limits displayed source without changing `source`. `cell_context=N` adds N neighbouring cells. Walking, parsing, and matching run in parallel in Rust; outputs and metadata are skipped.\n\nImportant:\nTraversal is parallel and result order is NOT guaranteed; wrap in `sorted(...)` if you need stable order. `path_re`/`skip_path_re` filter the returned/searched paths but do not prune traversal; use `skip_dir`/`skip_dir_re` to prune whole subtrees for speed. Run `doc(func)` for full parameter docments.\n\"\"\"\n\n## types:\n- class RgIter: ... # Initialize self. See help(type(self)) for accurate signature.\n\n## functions:\n- def fd(root: str | Path = '.', pattern: str | NoneType = None, files: bool = True, dirs: bool = False, show_target: bool = False, timeout_ms: int | NoneType = None, *, ext: str | list | NoneType = None, ignore: bool = True, max_depth: int | NoneType = None, **walk_params) -> PathResults: ... # Find paths with fd-style filters and gitignore support.\u2026\n- def ls(root: str | Path = '.', pattern: str | NoneType = None, dirs: bool = True, max_depth: int | NoneType = 1, ignore: bool = False, *, files: bool = True, show_target: bool = False, timeout_ms: int | NoneType = None, ext: str | list | NoneType = None, **walk_params) -> PathResults: ... # List a directory like `ls`: one level, directories included, ignore rules off, sorted by name.\u2026\n- def rg(pattern: str, root: str | Path = '.', case_sensitive: bool | NoneType = None, smart_case: bool = False, before_context: int = 0, after_context: int = 0, context: int = 0, paths: bool = False, count: bool = False, max_results: int | NoneType = None, lnhashs: bool = False, timeout_ms: int | NoneType = None, summary: bool = False, maxlen: int = 180, *, ext: str | list | NoneType = None, ignore: bool = True, max_depth: int | NoneType = None, **walk_params): ... # Search files and return `SearchResults`, matched paths, or a count; `lnhashs=True` shows exhash-style addresses.\u2026\n- def rg_iter(pattern: str, root: str | Path = '.', case_sensitive: bool | NoneType = None, smart_case: bool = False, before_context: int = 0, after_context: int = 0, context: int = 0, lnhashs: bool = False, *, ext: str | list | NoneType = None, ignore: bool = True, max_depth: int | NoneType = None, **walk_params) -> RgIter: ... # Search files lazily, yielding `SearchLine` rows; `lnhashs=True` shows exhash-style addresses.\u2026\n- def nbrg(pattern: str, root: str | Path = '.', cell_context: int = 0, case_sensitive: bool | NoneType = None, smart_case: bool = False, paths: bool = False, multiline: bool = False, max_results: int | NoneType = None, count: bool = False, timeout_ms: int | NoneType = None, maxlen: int = 180, *, ignore: bool = True, max_depth: int | NoneType = None, **walk_params): ... # Search `.ipynb` cell sources under `root` in parallel, returning matched cells, paths, or a count.\u2026\n- def rgstr(pattern: str, text: str, case_sensitive: bool | NoneType = None, smart_case: bool = False, *, before_context: int = 0, after_context: int = 0, context: int = 0) -> SearchResults: ... # Search text already in hand, like `rg` on a string: no path label, no separate `compile`.\u2026\n\n## shared params:\n- **walk_params (from `fd`):\n glob: str | list | NoneType = None, # Include glob or globs; alias for `include`\n include: str | list | NoneType = None, # Include glob or globs, e.g. `*.py`\n exclude: str | list | NoneType = None, # Exclude glob or globs, e.g. `test_*.py`\n hidden: bool = False, # Include hidden files and directories\n min_depth: int | NoneType = None, # Minimum depth for returned/searched paths\n max_filesize: int | NoneType = None, # Skip files larger than this many bytes\n follow_links: bool = False, # Follow symbolic links while walking\n same_file_system: bool = False, # Do not cross filesystem boundaries\n path_re: str | NoneType = None, # Regex that relative paths must match\n skip_path_re: str | NoneType = None, # Regex for relative paths to skip\n skip_dir: str | list | NoneType = None, # Directory glob or globs to prune\n skip_dir_re: str | NoneType = None, # Directory regex used to prune traversal"}]} +{"type": "assistant", "uuid": "7c73702c-fd10-5c58-bb93-1e74416125da", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/home/natedawg/aai-ws/llmdojo", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "assistant", "content": [{"type": "tool_use", "id": "call_d41f97015d13d9bc", "name": "mcp__clikernel__execute", "input": {"code": "doc(acp)"}}], "id": "msg_40cc359d57ba503bbc8b3251", "stop_reason": "tool_use", "stop_sequence": null, "stop_details": null, "usage": {"input_tokens": 13586, "output_tokens": 6, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 0}}, "requestId": "req_afeb2a43bddf5fa2860907e3"} +{"type": "user", "uuid": "0d7a6876-dfe7-5b25-87c1-468bc27ebd08", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/home/natedawg/aai-ws/llmdojo", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "user", "content": [{"type": "tool_result", "tool_use_id": "call_d41f97015d13d9bc", "content": "# module aai_coding.coding_patterns:\n\n\"\"\"Jeremy's coding style and conventions: read before writing, reviewing, or assessing any code.\n\n# Coding Patterns\n\nBased on the fastai style guide and Jeremy's 3 decades of coding experience, and used across all of his projects, not only fastai ones.\n\nUse the fastcore/fasthtml ecosystem (fastcore, fasthtml, fastlite, ...) when picking libraries. These are Jeremy's own, editable-installed as siblings, and preferred over heavier third-party alternatives.\n\n## Improving Tooling Pays Off Exponentially\n\nMaking our tools marvellous matters more than the task in hand. A finished task helps once. A better tool helps every later task, every later session, and the whole team. Ergonomics count as much as capability. Most code you have read was written by people who put up with tool friction rather than fix it, so your default is the workaround. Here the tools are ours and one edit away. When something grates, fix it or raise it. Never quietly work around it.\n\n## Every Construct Must Earn Its Place\n\nReaders assume everything present is necessary. When they see `str(x)` on something that's already a str, they stop and wonder what subtle thing it's guarding against. When the answer is \"nothing\", they paid for a mystery with no payoff. The same goes for defensive copies (`list(x)` that's never mutated), \"just in case\" try/excepts, redundant type coercions, and unused parameters. Before adding any construct, know why it's needed. If you can't say, leave it out. The cost doubles in nbdev projects, where tests are documentation.\n\nKnowing why a construct is needed does not mean waiting for someone to ask for it. An open source user who finds a gap concludes the library cannot do it and moves on. Judge a library feature by whether the design calls for it and users would expect it.\n\nThe same applies to prose in code: almost never add comments (only when the code is truly unclear), and don't add type hints, docstrings, or boilerplate that pull no weight. Prefer concise, readable code over verbose \"enterprise\" style.\nOnly write a code comment to state a constraint the code itself can't show, never to say where it came from, what the next line does, or why your change is correct. That's you talking to the reviewer, not the next reader, and it's noise the moment the PR merges.\n\n## Plain Commands: Flags, Redirects, and Pipes Are for Exceptions\n\nThe same principle applies to running commands. The bare invocation is the contract: `pytest tests -q`, `cargo fmt`, `maturin develop`. Project config exists precisely so the plain command does the right thing, and then every invocation is short, identical, and instantly readable. A flag, redirect, or pipe is a statement (\"this one call has a requirement the defaults don't cover\"), and used that way it carries information: the reader stops, asks why, and there's an answer. Sprinkled defensively on every call, decorations destroy that signal: every command looks exceptional so none is, and the one deliberate flag is indistinguishable from habit. Transcripts compound the damage, since each decorated call teaches later calls to decorate too.\n\n- Wanting the same flag on every run means it's a missing config line: promote it and go back to the bare command (`pytest --timeout 300` on every run becomes `timeout = 60` under `[tool.pytest.ini_options]`).\n- Don't check-then-apply when applying is the goal: `cargo fmt`, not `cargo fmt --check` followed by `cargo fmt`. `--check` is for a final no-mutation verification, e.g. CI.\n- Run commands bare and read their output. Never pipe through truncating filters (`| tail -20`, `| grep PASS`): truncation is decided before the output exists, and it hides exactly the surprises worth seeing. Never merge stderr into stdout with `2>&1`: separated, a crash is unmissable.\n- On the rare occasion output genuinely can't come back inline (far too large), redirect the streams separately (`>meta/stdout.txt 2>meta/stderr.txt`) and read the files from the kernel.\n- A real one-off requirement gets its flag once, with the reason stated alongside, and disappears again on the next call.\n\n## Docments\n\nDocments are trailing comments on function parameters that fastcore uses for documentation. A signature with docments (or any long signature) uses this layout: `(` stays on the def line, each parameter on its own line indented 4, and `):` alone on its own line at def indent:\n\n```python\n@delegates(start_kernel)\nasync def run_kernel(\n kernel_name='python3', # Kernelspec name to launch\n manager_cls=KernelManager, # Manager class, e.g. a subclass customizing launch\n **kwargs\n):\n```\n\nNEVER remove docments when refactoring. They're essential documentation.\n\nWhen `**kwargs` passes through to a known callee, decorate with `@delegates(callee)` so the signature shows the real options. delegates REQUIRES the collector to be named `kwargs`, not `kw` etc. Skip the decorator when the callee's own signature is just `**kwargs` (nothing to delegate).\n\n## Raw Strings\n\nWrite any non-trivial string literal as a raw string (`r\"...\"` / `r\"\"\"...\"\"\"`): regexes, text you pass to tools, code or markup inside strings, anything multi-line or containing backslashes. In plain strings a stray `\\n` or `\\d` either errors or silently corrupts, and each miss costs a round trip to diagnose plus another to fix. Raw strings are WYSIWYG, so the first attempt matches what you meant. The `r` costs nothing when no escapes are present, so make it the default, not the exception.\n\n## Style Checker (chkstyle)\n\nRun `chkstyle {path}` to check fastai style (only include path if needed). But use judgment. chkstyle is a hint, not gospel.\nIn nbdev projects, point it at the notebooks (`chkstyle nbs/00_core.ipynb`), not the exported `.py`. The notebook run also checks example and test cells, which never reach the module.\n\n## Config Patterns\n\nRead from standard locations rather than duplicating config:\n\n- GitHub release notes config: `.github/release.yml`\n- Project config: `pyproject.toml` under `[tool.yourpkg]`\n- Infer values when possible (e.g., package name from `[project].name`)\n- Bundle data files inside the package and read them with `importlib.resources`\n\n## Versioning: Bump After Release\n\nJeremy bumps the version immediately after each release, as part of releasing and never as part of a change. The tree therefore always carries the next release's version. A sibling dep pin can name a version before it ships (`foo>=`).\n\nA downstream pin is part of the change that creates the dependency. When a change makes one package consume another's new behavior or API, stamp the consumers' pins in the same session. A pin deferred to release time is a forgotten pin.\n\nThis convention is for Python projects. Other artifact types version at change time instead: for example, the Claude Code plugins in skill-plugins bump automatically when `./build.py` regenerates a changed output.\n\n## Project Layout\n\nPrefer flat layout over src/:\n\n```\nmyproject/\n\u251c\u2500\u2500 mypkg/\n\u2502 \u2514\u2500\u2500 __init__.py\n\u251c\u2500\u2500 tests/\n\u251c\u2500\u2500 pyproject.toml\n\u2514\u2500\u2500 README.md\n```\n\n## Testing\n\nAll code has writing, maintenance, and readability costs, and tests most of all: every test must be kept passing forever, gets read by every future contributor, and must be revised whenever the behavior it pins changes. So never write a test as a reflex, and don't aim for anywhere near 100% coverage. A test earns its place only when:\n\n- it documents an idea: in nbdev notebooks, tests ARE the documentation (see `doc(nbdev.skill)`), or\n- the logic is intricate enough that you had to think carefully to get it right (edge cases, parsing, arithmetic, tricky conditionals: the places a future change could silently break it), or\n- the code assumes something about an external system (a file format, an API's response shape, another tool's behavior) that is somewhat likely to change one day, and we want to hear about it when the assumption is violated. These must exercise the real thing (a mock merely re-states our assumption), so they're usually the slow-marked tests\n\nWiring and orchestration get zero tests: re-exports, delegations, one-line glue, functions that only sequence calls to other tools. A test there only asserts that Python works, and pins down internals we may want to change. Strong tell: if a test needs recording fakes or mock collaborators to reach the code, it's testing a transcript of the implementation, not logic. Extract the logic into a small pure function and test that, or don't test at all.\n\nIF you add a pytest test, ALWAYS work red-green: write it FIRST, run it to see it fail, THEN make the change, then run it again to see it pass. In nbdev projects there are no test cells - see `doc(nbdev.skill)`: changes revise lesson cells, and the red-green check applies only to an assertion you actually revised or added.\n\n- Prefer as few tests as possible: a single test that walks through many checks is more readable and faster than many small ones\n- A check worth keeping goes in a real test file or notebook cell, never left as an ad-hoc command. In a notebook, the checks made while exploring often ARE the narrative (each one both documents what we needed to know and keeps guarding it), so they stay as example cells. In a pytest file, an exploratory check survives only if it meets one of the criteria above\n- Assert the logic, not incidentals: check what the behavior guarantees, never byte-exact renderings, exact reprs, or field order. A test that compares a whole output string locks in formatting decisions that were never the point (e.g. assert the content appears in a markdown display block, not the display's exact text). NEVER use tests to \"lock in\" behavior, unless that exact behavior really is a key part of the logic or contract that must always be true forever\n- Use `pytest -q` (not `python -m pytest`, which prompts for permission). nbdev projects use `nbdev-test` on the changed notebook, but some notebooks are slow or hit live services, so check with the user before running one you don't know is safe (known safe: all of pyskills)\n- Don't run slow-marked tests until finishing a session, or after a change likely to directly impact them\n\n## One-liner Patterns\n\n```python\n# Conditionals\nif not x: return default\nif x and y: do_thing()\n\n# Try/except\ntry: return run(\"cmd\").strip() or default\nexcept Exception: return default\n\n# Loops\nfor i in range(n): result[i] = 0\nwhile len(items) < 3: items.append(0)\n```\n\n## Import Style\n\nCombine imports on single lines:\n\n```python\nimport os, re, sys, shutil\nfrom pathlib import Path\nfrom fastcore.utils import *\n```\n\n`from fastcore.utils import *` already provides `os`, `Path`, and much of the stdlib, so don't re-import those alongside it.\n\"\"\""}]}, "toolUseResult": [{"type": "text", "text": "# module aai_coding.coding_patterns:\n\n\"\"\"Jeremy's coding style and conventions: read before writing, reviewing, or assessing any code.\n\n# Coding Patterns\n\nBased on the fastai style guide and Jeremy's 3 decades of coding experience, and used across all of his projects, not only fastai ones.\n\nUse the fastcore/fasthtml ecosystem (fastcore, fasthtml, fastlite, ...) when picking libraries. These are Jeremy's own, editable-installed as siblings, and preferred over heavier third-party alternatives.\n\n## Improving Tooling Pays Off Exponentially\n\nMaking our tools marvellous matters more than the task in hand. A finished task helps once. A better tool helps every later task, every later session, and the whole team. Ergonomics count as much as capability. Most code you have read was written by people who put up with tool friction rather than fix it, so your default is the workaround. Here the tools are ours and one edit away. When something grates, fix it or raise it. Never quietly work around it.\n\n## Every Construct Must Earn Its Place\n\nReaders assume everything present is necessary. When they see `str(x)` on something that's already a str, they stop and wonder what subtle thing it's guarding against. When the answer is \"nothing\", they paid for a mystery with no payoff. The same goes for defensive copies (`list(x)` that's never mutated), \"just in case\" try/excepts, redundant type coercions, and unused parameters. Before adding any construct, know why it's needed. If you can't say, leave it out. The cost doubles in nbdev projects, where tests are documentation.\n\nKnowing why a construct is needed does not mean waiting for someone to ask for it. An open source user who finds a gap concludes the library cannot do it and moves on. Judge a library feature by whether the design calls for it and users would expect it.\n\nThe same applies to prose in code: almost never add comments (only when the code is truly unclear), and don't add type hints, docstrings, or boilerplate that pull no weight. Prefer concise, readable code over verbose \"enterprise\" style.\nOnly write a code comment to state a constraint the code itself can't show, never to say where it came from, what the next line does, or why your change is correct. That's you talking to the reviewer, not the next reader, and it's noise the moment the PR merges.\n\n## Plain Commands: Flags, Redirects, and Pipes Are for Exceptions\n\nThe same principle applies to running commands. The bare invocation is the contract: `pytest tests -q`, `cargo fmt`, `maturin develop`. Project config exists precisely so the plain command does the right thing, and then every invocation is short, identical, and instantly readable. A flag, redirect, or pipe is a statement (\"this one call has a requirement the defaults don't cover\"), and used that way it carries information: the reader stops, asks why, and there's an answer. Sprinkled defensively on every call, decorations destroy that signal: every command looks exceptional so none is, and the one deliberate flag is indistinguishable from habit. Transcripts compound the damage, since each decorated call teaches later calls to decorate too.\n\n- Wanting the same flag on every run means it's a missing config line: promote it and go back to the bare command (`pytest --timeout 300` on every run becomes `timeout = 60` under `[tool.pytest.ini_options]`).\n- Don't check-then-apply when applying is the goal: `cargo fmt`, not `cargo fmt --check` followed by `cargo fmt`. `--check` is for a final no-mutation verification, e.g. CI.\n- Run commands bare and read their output. Never pipe through truncating filters (`| tail -20`, `| grep PASS`): truncation is decided before the output exists, and it hides exactly the surprises worth seeing. Never merge stderr into stdout with `2>&1`: separated, a crash is unmissable.\n- On the rare occasion output genuinely can't come back inline (far too large), redirect the streams separately (`>meta/stdout.txt 2>meta/stderr.txt`) and read the files from the kernel.\n- A real one-off requirement gets its flag once, with the reason stated alongside, and disappears again on the next call.\n\n## Docments\n\nDocments are trailing comments on function parameters that fastcore uses for documentation. A signature with docments (or any long signature) uses this layout: `(` stays on the def line, each parameter on its own line indented 4, and `):` alone on its own line at def indent:\n\n```python\n@delegates(start_kernel)\nasync def run_kernel(\n kernel_name='python3', # Kernelspec name to launch\n manager_cls=KernelManager, # Manager class, e.g. a subclass customizing launch\n **kwargs\n):\n```\n\nNEVER remove docments when refactoring. They're essential documentation.\n\nWhen `**kwargs` passes through to a known callee, decorate with `@delegates(callee)` so the signature shows the real options. delegates REQUIRES the collector to be named `kwargs`, not `kw` etc. Skip the decorator when the callee's own signature is just `**kwargs` (nothing to delegate).\n\n## Raw Strings\n\nWrite any non-trivial string literal as a raw string (`r\"...\"` / `r\"\"\"...\"\"\"`): regexes, text you pass to tools, code or markup inside strings, anything multi-line or containing backslashes. In plain strings a stray `\\n` or `\\d` either errors or silently corrupts, and each miss costs a round trip to diagnose plus another to fix. Raw strings are WYSIWYG, so the first attempt matches what you meant. The `r` costs nothing when no escapes are present, so make it the default, not the exception.\n\n## Style Checker (chkstyle)\n\nRun `chkstyle {path}` to check fastai style (only include path if needed). But use judgment. chkstyle is a hint, not gospel.\nIn nbdev projects, point it at the notebooks (`chkstyle nbs/00_core.ipynb`), not the exported `.py`. The notebook run also checks example and test cells, which never reach the module.\n\n## Config Patterns\n\nRead from standard locations rather than duplicating config:\n\n- GitHub release notes config: `.github/release.yml`\n- Project config: `pyproject.toml` under `[tool.yourpkg]`\n- Infer values when possible (e.g., package name from `[project].name`)\n- Bundle data files inside the package and read them with `importlib.resources`\n\n## Versioning: Bump After Release\n\nJeremy bumps the version immediately after each release, as part of releasing and never as part of a change. The tree therefore always carries the next release's version. A sibling dep pin can name a version before it ships (`foo>=`).\n\nA downstream pin is part of the change that creates the dependency. When a change makes one package consume another's new behavior or API, stamp the consumers' pins in the same session. A pin deferred to release time is a forgotten pin.\n\nThis convention is for Python projects. Other artifact types version at change time instead: for example, the Claude Code plugins in skill-plugins bump automatically when `./build.py` regenerates a changed output.\n\n## Project Layout\n\nPrefer flat layout over src/:\n\n```\nmyproject/\n\u251c\u2500\u2500 mypkg/\n\u2502 \u2514\u2500\u2500 __init__.py\n\u251c\u2500\u2500 tests/\n\u251c\u2500\u2500 pyproject.toml\n\u2514\u2500\u2500 README.md\n```\n\n## Testing\n\nAll code has writing, maintenance, and readability costs, and tests most of all: every test must be kept passing forever, gets read by every future contributor, and must be revised whenever the behavior it pins changes. So never write a test as a reflex, and don't aim for anywhere near 100% coverage. A test earns its place only when:\n\n- it documents an idea: in nbdev notebooks, tests ARE the documentation (see `doc(nbdev.skill)`), or\n- the logic is intricate enough that you had to think carefully to get it right (edge cases, parsing, arithmetic, tricky conditionals: the places a future change could silently break it), or\n- the code assumes something about an external system (a file format, an API's response shape, another tool's behavior) that is somewhat likely to change one day, and we want to hear about it when the assumption is violated. These must exercise the real thing (a mock merely re-states our assumption), so they're usually the slow-marked tests\n\nWiring and orchestration get zero tests: re-exports, delegations, one-line glue, functions that only sequence calls to other tools. A test there only asserts that Python works, and pins down internals we may want to change. Strong tell: if a test needs recording fakes or mock collaborators to reach the code, it's testing a transcript of the implementation, not logic. Extract the logic into a small pure function and test that, or don't test at all.\n\nIF you add a pytest test, ALWAYS work red-green: write it FIRST, run it to see it fail, THEN make the change, then run it again to see it pass. In nbdev projects there are no test cells - see `doc(nbdev.skill)`: changes revise lesson cells, and the red-green check applies only to an assertion you actually revised or added.\n\n- Prefer as few tests as possible: a single test that walks through many checks is more readable and faster than many small ones\n- A check worth keeping goes in a real test file or notebook cell, never left as an ad-hoc command. In a notebook, the checks made while exploring often ARE the narrative (each one both documents what we needed to know and keeps guarding it), so they stay as example cells. In a pytest file, an exploratory check survives only if it meets one of the criteria above\n- Assert the logic, not incidentals: check what the behavior guarantees, never byte-exact renderings, exact reprs, or field order. A test that compares a whole output string locks in formatting decisions that were never the point (e.g. assert the content appears in a markdown display block, not the display's exact text). NEVER use tests to \"lock in\" behavior, unless that exact behavior really is a key part of the logic or contract that must always be true forever\n- Use `pytest -q` (not `python -m pytest`, which prompts for permission). nbdev projects use `nbdev-test` on the changed notebook, but some notebooks are slow or hit live services, so check with the user before running one you don't know is safe (known safe: all of pyskills)\n- Don't run slow-marked tests until finishing a session, or after a change likely to directly impact them\n\n## One-liner Patterns\n\n```python\n# Conditionals\nif not x: return default\nif x and y: do_thing()\n\n# Try/except\ntry: return run(\"cmd\").strip() or default\nexcept Exception: return default\n\n# Loops\nfor i in range(n): result[i] = 0\nwhile len(items) < 3: items.append(0)\n```\n\n## Import Style\n\nCombine imports on single lines:\n\n```python\nimport os, re, sys, shutil\nfrom pathlib import Path\nfrom fastcore.utils import *\n```\n\n`from fastcore.utils import *` already provides `os`, `Path`, and much of the stdlib, so don't re-import those alongside it.\n\"\"\""}]} +{"type": "assistant", "uuid": "e78233f3-cf65-59a2-9d7b-1b5645c833d7", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/home/natedawg/aai-ws/llmdojo", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "assistant", "content": [{"type": "tool_use", "id": "toolu_01YPiAitbgdQ2HkRQYJSruHV", "name": "mcp__clikernel__execute", "input": {"code": "list_pyskills()"}}], "id": "msg_80473309006c5dc3866f0b14", "stop_reason": "tool_use", "stop_sequence": null, "stop_details": null, "usage": {"input_tokens": 16193, "output_tokens": 6, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 0}}, "requestId": "req_cdf6ba5e9c0c552fb1128b5c"} +{"type": "user", "uuid": "49930c54-a7c4-5441-b6f5-9f5bd64538ab", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/home/natedawg/aai-ws/llmdojo", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "user", "content": [{"type": "tool_result", "tool_use_id": "toolu_01YPiAitbgdQ2HkRQYJSruHV", "content": "{'docxlite.skill': 'Read, render, search, and edit .docx files with tracked changes. This skill is for LLM-friendly Word document editing inside notebooks and solveit dialogs.\\nIt builds on `python-docx`, adding markdown previews, markdown-to-runs insertion,\\ndocument-order block iteration, raw-text search, tracked changes, and small editing\\nhelpers for paragraphs, cells, rows, and tables.',\n 'tracefunc.skill': \"Trace a Python function's execution at AST-line level: per-line hit counts and live variable values, via `sys.monitoring`. Use when debugging *why* code takes a branch, loops, recurses, or computes a wrong value, without editing the code under investigation or using an interactive debugger.\",\n 'fastfitz.skill': 'Read, search, and visually preview PDFs with fastfitz (an ergonomic layer over PyMuPDF/fitz). Use this when a task needs PDF contents: text extraction first, rendered previews when layout, tables, stamps, signatures, or scan quality matter.',\n 'fastpdfium.skill': 'Read, search, preview, and edit PDFs with fastpdfium (an ergonomic, Apache-licensed layer over pypdfium2/PDFium). Use this when a task needs PDF contents: text extraction first, rendered previews when layout, tables, stamps, signatures, or scan quality matter.',\n 'fastcdp.skill': 'Work with Chrome through the DevTools Protocol: open pages, click and type, read a page as an accessibility tree, keep a log of console and network activity for debugging, and call any CDP command.',\n 'remold': \"Structural search and rewrite for Python source: declarative ast-grep pattern rules, LibCST matcher transforms for everything patterns can't express, and tree-based symbol queries. Use this to edit code by structure (rename calls, move methods, rewrite APIs) where regexes break and `ast` loses comments.\",\n 'fastmux.skill': 'Work with tmux sessions, windows, and panes from Python, plus named background sessions keyed by sid. Use this when code needs to read what is on a terminal, send input to an interactive program, build pane layouts, search text across terminals, or keep a terminal open that you and the user can both see.',\n 'llmsurgery.skill': 'Read and work with Claude Code and Codex session transcripts.',\n 'solveit_client.skill': \"Drive SolveIt dialogs over HTTP from outside the instance: create dialogs, add and edit messages, and execute code and AI prompts. Use this to automate a SolveIt instance from a laptop, script, or agent session -- anywhere that isn't the dialog's own kernel (from inside a dialog, use dialoghelper instead).\",\n 'cordslite.skill': 'Read-only access to Discord through cordslite. Covers connecting, opening a guild, finding your way around its channels, searching messages, reading threads, and fetching attachments.',\n 'fastcore.editskill': 'Text, file, cell, and notebook editing from `fastcore.tools` and `fastcore.nbio`, plus the conventions the whole fastai editing toolkit follows. Read this before working with the editing tools in any package that shares them.',\n 'solvedrive.skill': \"Load this skill when an agent needs to search, download, and upload Google Drive files using solvedrive. It covers connecting to Drive, searching across My Drive and shared drives with Drive's query syntax, downloading file content and exporting Google Docs, and uploading, renaming, moving, and copying files and creating folders. Trash, deletion, and sharing are documented for reference but are not enabled by default.\",\n 'solvemail.skill': 'Load this skill when an agent needs to read, search, and organize Gmail using solvemail. It covers connecting to Gmail, searching messages and threads, reading bodies and attachments, and managing labels. Sending, replying, forwarding, and trashing are documented for reference but are not enabled by default.',\n 'fastgws.skill': \"Use fastgws to read and work with Google Workspace and Google APIs from Python. This skill exposes the base `GWSApi` client, OAuth credential loading, and generated Google API operations. Use it when the task needs access to Gmail, Calendar, Drive, Docs, Sheets, Places, or another Google API published through Google's discovery documents.\",\n 'aidialog.dlgskill': \"Read, search, and edit dialogs and notebooks through aidialog's `Dialog` and `Message` classes\",\n 'nbdev.skill': 'Author clear, executable nbdev notebooks where code, prose, examples, outputs, and tests form one coherent narrative.',\n 'pyskills.skill': 'Pyskills are tool modules that Python packages register so you can find them without importing everything. `list_pyskills()` names the ones installed, with a one-line description each, and needs no imports. Load one with a normal import, then read its docs with `doc()`.',\n 'ghapi.skill': 'GitHub REST API access via `GhApi`, plus local git operations via `fastgit.Git`. Use this for day-to-day GitHub work: reading/creating issues and PRs, checking CI status, managing releases/branches/gists, and repo-local git operations -- all from Python, no shelling out to `gh`/`git` needed.',\n 'rgapi.skill': 'Fast and flexible file finding and search for Python. Use this when code needs `fd`-style file finding or `rg`-style searching.',\n 'exhash.skill': 'Universal hash-verified text editing for local files. Use this for one reliable way to read, preview, and change text files.',\n 'claude2dlg.core': 'claude.ai web conversations as dialogs',\n 'fastcflare.skill': 'Cloudflare API access via `CloudflareApi`: zones, DNS records, caching, and every other Cloudflare v4 feature from Python. Use this for day-to-day Cloudflare work instead of hand-built httpx calls against api.cloudflare.com.',\n 'clikernel.skill': 'Use the `clikernel` MCP session as the default workspace for Python work: reading and changing files, notebook work, trying things out, checking how a library behaves, and reshaping data. One session stays open, so imports and variables carry between calls. Read this before writing, running, or debugging Python code in a session with `clikernel` connected.',\n 'dialoghelper.solveitskill': 'Read, search, edit, and manage Solveit dialogs using dialoghelper.core, including dialog/message addressing, line-numbered inspection, targeted message edits, add/update/delete/copy/paste workflows, and safe editing patterns.',\n 'aai_coding.coding_patterns': \"Jeremy's coding style and conventions: read before writing, reviewing, or assessing any code.\",\n 'aai_coding.harness_docs': 'Official docs for the tool you are running in (Claude Code or codex): read before answering questions about its behaviour, config, or features.',\n 'aai_coding.looker': 'Answer questions about images and PDFs by asking a separate codex process to look at them, which keeps large files out of this session (macOS only). Use it to verify rendered output: PDFs, screenshots, captured windows.',\n 'aai_coding.theory': 'What \"Theory\" (capital T) means in this team\\'s vocabulary: Peter Naur\\'s programming-as-theory-building. Read when the user says the Theory of something, or asks to discuss, find, or capture a Theory.',\n 'aai_coding.write_docs': 'How to write reference prose: read before writing docstrings, READMEs, API docs, PR descriptions, commit messages, or messages to co-workers.',\n 'aai_coding.write_prose': \"How to write narrative prose that reads as though a person wrote it: read before writing blog posts, essays, announcements, or anything else with the author's voice. write-docs covers reference prose.\",\n 'aai_coding.write_summary': 'How to write summaries that stand alone: read before summarizing a conversation, meeting, PR, paper, or repo change for a human reader.',\n 'fasttract.drafting': 'Draft and export legal contracts in a Solveit dialog',\n 'my_local.skill': 'A quick local pyskill.'}"}]}, "toolUseResult": [{"type": "text", "text": "{'docxlite.skill': 'Read, render, search, and edit .docx files with tracked changes. This skill is for LLM-friendly Word document editing inside notebooks and solveit dialogs.\\nIt builds on `python-docx`, adding markdown previews, markdown-to-runs insertion,\\ndocument-order block iteration, raw-text search, tracked changes, and small editing\\nhelpers for paragraphs, cells, rows, and tables.',\n 'tracefunc.skill': \"Trace a Python function's execution at AST-line level: per-line hit counts and live variable values, via `sys.monitoring`. Use when debugging *why* code takes a branch, loops, recurses, or computes a wrong value, without editing the code under investigation or using an interactive debugger.\",\n 'fastfitz.skill': 'Read, search, and visually preview PDFs with fastfitz (an ergonomic layer over PyMuPDF/fitz). Use this when a task needs PDF contents: text extraction first, rendered previews when layout, tables, stamps, signatures, or scan quality matter.',\n 'fastpdfium.skill': 'Read, search, preview, and edit PDFs with fastpdfium (an ergonomic, Apache-licensed layer over pypdfium2/PDFium). Use this when a task needs PDF contents: text extraction first, rendered previews when layout, tables, stamps, signatures, or scan quality matter.',\n 'fastcdp.skill': 'Work with Chrome through the DevTools Protocol: open pages, click and type, read a page as an accessibility tree, keep a log of console and network activity for debugging, and call any CDP command.',\n 'remold': \"Structural search and rewrite for Python source: declarative ast-grep pattern rules, LibCST matcher transforms for everything patterns can't express, and tree-based symbol queries. Use this to edit code by structure (rename calls, move methods, rewrite APIs) where regexes break and `ast` loses comments.\",\n 'fastmux.skill': 'Work with tmux sessions, windows, and panes from Python, plus named background sessions keyed by sid. Use this when code needs to read what is on a terminal, send input to an interactive program, build pane layouts, search text across terminals, or keep a terminal open that you and the user can both see.',\n 'llmsurgery.skill': 'Read and work with Claude Code and Codex session transcripts.',\n 'solveit_client.skill': \"Drive SolveIt dialogs over HTTP from outside the instance: create dialogs, add and edit messages, and execute code and AI prompts. Use this to automate a SolveIt instance from a laptop, script, or agent session -- anywhere that isn't the dialog's own kernel (from inside a dialog, use dialoghelper instead).\",\n 'cordslite.skill': 'Read-only access to Discord through cordslite. Covers connecting, opening a guild, finding your way around its channels, searching messages, reading threads, and fetching attachments.',\n 'fastcore.editskill': 'Text, file, cell, and notebook editing from `fastcore.tools` and `fastcore.nbio`, plus the conventions the whole fastai editing toolkit follows. Read this before working with the editing tools in any package that shares them.',\n 'solvedrive.skill': \"Load this skill when an agent needs to search, download, and upload Google Drive files using solvedrive. It covers connecting to Drive, searching across My Drive and shared drives with Drive's query syntax, downloading file content and exporting Google Docs, and uploading, renaming, moving, and copying files and creating folders. Trash, deletion, and sharing are documented for reference but are not enabled by default.\",\n 'solvemail.skill': 'Load this skill when an agent needs to read, search, and organize Gmail using solvemail. It covers connecting to Gmail, searching messages and threads, reading bodies and attachments, and managing labels. Sending, replying, forwarding, and trashing are documented for reference but are not enabled by default.',\n 'fastgws.skill': \"Use fastgws to read and work with Google Workspace and Google APIs from Python. This skill exposes the base `GWSApi` client, OAuth credential loading, and generated Google API operations. Use it when the task needs access to Gmail, Calendar, Drive, Docs, Sheets, Places, or another Google API published through Google's discovery documents.\",\n 'aidialog.dlgskill': \"Read, search, and edit dialogs and notebooks through aidialog's `Dialog` and `Message` classes\",\n 'nbdev.skill': 'Author clear, executable nbdev notebooks where code, prose, examples, outputs, and tests form one coherent narrative.',\n 'pyskills.skill': 'Pyskills are tool modules that Python packages register so you can find them without importing everything. `list_pyskills()` names the ones installed, with a one-line description each, and needs no imports. Load one with a normal import, then read its docs with `doc()`.',\n 'ghapi.skill': 'GitHub REST API access via `GhApi`, plus local git operations via `fastgit.Git`. Use this for day-to-day GitHub work: reading/creating issues and PRs, checking CI status, managing releases/branches/gists, and repo-local git operations -- all from Python, no shelling out to `gh`/`git` needed.',\n 'rgapi.skill': 'Fast and flexible file finding and search for Python. Use this when code needs `fd`-style file finding or `rg`-style searching.',\n 'exhash.skill': 'Universal hash-verified text editing for local files. Use this for one reliable way to read, preview, and change text files.',\n 'claude2dlg.core': 'claude.ai web conversations as dialogs',\n 'fastcflare.skill': 'Cloudflare API access via `CloudflareApi`: zones, DNS records, caching, and every other Cloudflare v4 feature from Python. Use this for day-to-day Cloudflare work instead of hand-built httpx calls against api.cloudflare.com.',\n 'clikernel.skill': 'Use the `clikernel` MCP session as the default workspace for Python work: reading and changing files, notebook work, trying things out, checking how a library behaves, and reshaping data. One session stays open, so imports and variables carry between calls. Read this before writing, running, or debugging Python code in a session with `clikernel` connected.',\n 'dialoghelper.solveitskill': 'Read, search, edit, and manage Solveit dialogs using dialoghelper.core, including dialog/message addressing, line-numbered inspection, targeted message edits, add/update/delete/copy/paste workflows, and safe editing patterns.',\n 'aai_coding.coding_patterns': \"Jeremy's coding style and conventions: read before writing, reviewing, or assessing any code.\",\n 'aai_coding.harness_docs': 'Official docs for the tool you are running in (Claude Code or codex): read before answering questions about its behaviour, config, or features.',\n 'aai_coding.looker': 'Answer questions about images and PDFs by asking a separate codex process to look at them, which keeps large files out of this session (macOS only). Use it to verify rendered output: PDFs, screenshots, captured windows.',\n 'aai_coding.theory': 'What \"Theory\" (capital T) means in this team\\'s vocabulary: Peter Naur\\'s programming-as-theory-building. Read when the user says the Theory of something, or asks to discuss, find, or capture a Theory.',\n 'aai_coding.write_docs': 'How to write reference prose: read before writing docstrings, READMEs, API docs, PR descriptions, commit messages, or messages to co-workers.',\n 'aai_coding.write_prose': \"How to write narrative prose that reads as though a person wrote it: read before writing blog posts, essays, announcements, or anything else with the author's voice. write-docs covers reference prose.\",\n 'aai_coding.write_summary': 'How to write summaries that stand alone: read before summarizing a conversation, meeting, PR, paper, or repo change for a human reader.',\n 'fasttract.drafting': 'Draft and export legal contracts in a Solveit dialog',\n 'my_local.skill': 'A quick local pyskill.'}"}]} +{"type": "assistant", "uuid": "361ab17a-bb0f-5ac6-8872-79b7c337e3a2", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/home/natedawg/aai-ws/llmdojo", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "assistant", "content": [{"type": "text", "text": "OK I'm ready."}], "id": "msg_6380b40e27ac57aeb9cbece9", "stop_reason": "end_turn", "stop_sequence": null, "stop_details": null, "usage": {"input_tokens": 17786, "output_tokens": 6, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 0}}, "requestId": "req_f803dea20556524381d36d09"} +{"type": "user", "uuid": "10c8a1af-2d96-5bd4-9f3b-6d5f819a51ca", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/home/natedawg/aai-ws/llmdojo", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "user", "content": "Complete the dojo and tell me when you're ready."}} +{"type": "assistant", "uuid": "0303ad55-5ba9-5080-9460-e7eaecce9dcd", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/home/natedawg/aai-ws/llmdojo", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "assistant", "content": [{"type": "tool_use", "id": "toolu_01XJJ5Yx66nUxRm3WchRqP14", "name": "mcp__clikernel__execute", "input": {"code": "dojo_start()"}}], "id": "msg_36f0069ad9fb5e2483bfd15e", "stop_reason": "tool_use", "stop_sequence": null, "stop_details": null, "usage": {"input_tokens": 17807, "output_tokens": 6, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 0}}, "requestId": "req_cf7fe32bf1a1519a9083dfcb"} +{"type": "user", "uuid": "17a6addb-ec4c-5299-9ab4-417519697e70", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/home/natedawg/aai-ws/llmdojo", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "user", "content": [{"type": "tool_result", "tool_use_id": "toolu_01XJJ5Yx66nUxRm3WchRqP14", "content": "== llmdojo ==\nAssumed knowledge: the toolkit conventions taught by the docs your host's startup asked you to read: doc(pysk, edsk) (with the host's own skill module first, where it has one), doc(dsk, exh, rgsk), and doc(acp). Do not continue unless that output is visible in your context; if any is missing, read it now.\nWork only in: /tmp/dojo\n%cd there first: chdir is never penalized, because the relative paths it enables make every later cell shorter - path tokens you never repeat are tokens saved, in the dojo and in real work.\nScoring: kernel cell = 1 stroke; Bash tool call = 2; each print() call = +1. The tooling's reprs are designed to be optimally useful read bare, so end each cell with a bare expression and read what comes back. `doc()` returns a `PrettyString`: leave it as the final expression when you want to see it, or assign it when you do not want it rendered, for instance for very large docs you want to search through. Cells of only doc()/list_pyskills()/imports are free (bare calls, NOT wrapped in print()), as are comment-only narration cells and chdir cells (%cd / os.chdir).\nPenalties: +1 per skill module or workspace function used before doc()ing it.\nPar assumes the tooling's best route, shown with each kata at scoring: matching par means you found it.\nWhy the round matters: the rest of this session copies whatever this transcript shows, so one round of correct tooling use improves everything after it. Falling back to sed, cat, or Path.read_text() teaches the opposite, and we find those fallbacks go with weaker, less creative work across the whole task.\nPer-kata scoring: before starting a kata, run a cell containing ONLY a comment in this exact format: '# kata 3'. Cells after it count toward that kata until the next tag cell; tag cells are free. Anything fancier is narration, not a tag.\nPar for the round: 8. When done: dojo_score(bash_calls=)\nThe round is complete ONLY on a clean score: par or better, every kata ok, no penalties. Until then do no work outside the dojo; redo over-par katas with dojo_redo, in ascending order. Scoring pauses the ledger: dojo_redo (or dojo_resume() without a reset) restarts it.\nThis dojo is an early version: note anything about the scoring or process that seems possibly-imperfect, and include it in your report.\n\n1. (par 1) Why does this project use httpx? Answer in prose via dojo_score(orient=\"...\"), including the specific justification the notebook gives. Tip: find_msgs' context= defaults to 1 for a reason - the why usually lives next to the what.\n2. (par 2) In core.py: change the default units to 'metric', delete the FIXME comment line, and rename the cfg variable to config everywhere (load_cfg keeps its name; docstring unchanged).\n3. (par 2) In tmpl.py: replace the whole render() function with exactly this, verbatim:\n\n def render(name, temp):\n r\"\"\"Render a one-line summary; keep \\t, \\n and ''' literal in this docstring.\"\"\"\n return name + ':\\t' + str(temp) + ' degrees\\n'\n4. (par 2) In nbs/01_api.ipynb: the markdown under the Retries header is wrong; it should say the request is retried twice more, making \"3 attempts\" in all.\n5. (par 1) report.py defines daily_report (the kata dir is importable during the round). Produce the report for report.SAMPLE in the form needed for production, and pass its first line via dojo_score(report=\"...\"). Tip: doc() is free, and an overview line ending in \"...\" means the full docstring holds more."}]}, "toolUseResult": [{"type": "text", "text": "== llmdojo ==\nAssumed knowledge: the toolkit conventions taught by the docs your host's startup asked you to read: doc(pysk, edsk) (with the host's own skill module first, where it has one), doc(dsk, exh, rgsk), and doc(acp). Do not continue unless that output is visible in your context; if any is missing, read it now.\nWork only in: /tmp/dojo\n%cd there first: chdir is never penalized, because the relative paths it enables make every later cell shorter - path tokens you never repeat are tokens saved, in the dojo and in real work.\nScoring: kernel cell = 1 stroke; Bash tool call = 2; each print() call = +1. The tooling's reprs are designed to be optimally useful read bare, so end each cell with a bare expression and read what comes back. `doc()` returns a `PrettyString`: leave it as the final expression when you want to see it, or assign it when you do not want it rendered, for instance for very large docs you want to search through. Cells of only doc()/list_pyskills()/imports are free (bare calls, NOT wrapped in print()), as are comment-only narration cells and chdir cells (%cd / os.chdir).\nPenalties: +1 per skill module or workspace function used before doc()ing it.\nPar assumes the tooling's best route, shown with each kata at scoring: matching par means you found it.\nWhy the round matters: the rest of this session copies whatever this transcript shows, so one round of correct tooling use improves everything after it. Falling back to sed, cat, or Path.read_text() teaches the opposite, and we find those fallbacks go with weaker, less creative work across the whole task.\nPer-kata scoring: before starting a kata, run a cell containing ONLY a comment in this exact format: '# kata 3'. Cells after it count toward that kata until the next tag cell; tag cells are free. Anything fancier is narration, not a tag.\nPar for the round: 8. When done: dojo_score(bash_calls=)\nThe round is complete ONLY on a clean score: par or better, every kata ok, no penalties. Until then do no work outside the dojo; redo over-par katas with dojo_redo, in ascending order. Scoring pauses the ledger: dojo_redo (or dojo_resume() without a reset) restarts it.\nThis dojo is an early version: note anything about the scoring or process that seems possibly-imperfect, and include it in your report.\n\n1. (par 1) Why does this project use httpx? Answer in prose via dojo_score(orient=\"...\"), including the specific justification the notebook gives. Tip: find_msgs' context= defaults to 1 for a reason - the why usually lives next to the what.\n2. (par 2) In core.py: change the default units to 'metric', delete the FIXME comment line, and rename the cfg variable to config everywhere (load_cfg keeps its name; docstring unchanged).\n3. (par 2) In tmpl.py: replace the whole render() function with exactly this, verbatim:\n\n def render(name, temp):\n r\"\"\"Render a one-line summary; keep \\t, \\n and ''' literal in this docstring.\"\"\"\n return name + ':\\t' + str(temp) + ' degrees\\n'\n4. (par 2) In nbs/01_api.ipynb: the markdown under the Retries header is wrong; it should say the request is retried twice more, making \"3 attempts\" in all.\n5. (par 1) report.py defines daily_report (the kata dir is importable during the round). Produce the report for report.SAMPLE in the form needed for production, and pass its first line via dojo_score(report=\"...\"). Tip: doc() is free, and an overview line ending in \"...\" means the full docstring holds more."}]} +{"type": "assistant", "uuid": "f56f5e1b-f249-5766-80dd-2d0afe9ab4ec", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/home/natedawg/aai-ws/llmdojo", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "assistant", "content": [{"type": "tool_use", "id": "toolu_01LCPJVwbh2xwBc6WiKeMaet", "name": "mcp__clikernel__execute", "input": {"code": "%cd /tmp/dojo"}}], "id": "msg_375f8dbd59595c1286f6e58c", "stop_reason": "tool_use", "stop_sequence": null, "stop_details": null, "usage": {"input_tokens": 18683, "output_tokens": 7, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 0}}, "requestId": "req_1c611218643154c3adefad37"} +{"type": "user", "uuid": "f07d35cb-ce35-555e-a0cc-99bd5de6772f", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/home/natedawg/aai-ws/llmdojo", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "user", "content": [{"type": "tool_result", "tool_use_id": "toolu_01LCPJVwbh2xwBc6WiKeMaet", "content": "/tmp/dojo"}]}, "toolUseResult": [{"type": "text", "text": "/tmp/dojo"}]} +{"type": "assistant", "uuid": "10653bf8-8454-52ac-a013-8ea76ade6388", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/home/natedawg/aai-ws/llmdojo", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "assistant", "content": [{"type": "tool_use", "id": "toolu_01LXQ1WF7haDfVK8cDnctH2p", "name": "mcp__clikernel__execute", "input": {"code": "# kata 1"}}], "id": "msg_80310f828a595fb895ddc69a", "stop_reason": "tool_use", "stop_sequence": null, "stop_details": null, "usage": {"input_tokens": 18694, "output_tokens": 9, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 0}}, "requestId": "req_951055c8a03f532c9b6f768f"} +{"type": "user", "uuid": "8856ed72-df6e-5610-bca7-a934c6c6d6ce", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/home/natedawg/aai-ws/llmdojo", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "user", "content": [{"type": "tool_result", "tool_use_id": "toolu_01LXQ1WF7haDfVK8cDnctH2p", "content": "(mcp__clikernel__execute completed with no output)"}]}, "toolUseResult": [{"type": "text", "text": "(mcp__clikernel__execute completed with no output)"}]} +{"type": "assistant", "uuid": "c6cc4e25-fde8-5fc4-83a7-9569eabe18a8", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/home/natedawg/aai-ws/llmdojo", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "assistant", "content": [{"type": "tool_use", "id": "toolu_01SHCoJn4mo6DLCRkX1xzHiZ", "name": "mcp__clikernel__execute", "input": {"code": "doc(find_msgs, view_dlg)"}}], "id": "msg_9e47ade6a5eb512299ca4928", "stop_reason": "tool_use", "stop_sequence": null, "stop_details": null, "usage": {"input_tokens": 18713, "output_tokens": 7, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 0}}, "requestId": "req_02c012a1442d5311934a8889"} +{"type": "user", "uuid": "c5a63a34-61ac-568c-9316-ebb5fee81aa9", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/home/natedawg/aai-ws/llmdojo", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "user", "content": [{"type": "tool_result", "tool_use_id": "toolu_01SHCoJn4mo6DLCRkX1xzHiZ", "content": "def find_msgs(\n re_pattern:str='', # Regex over content (a prompt's reply included), DOTALL+MULTILINE; an invalid regex matches literally\n dlg:NoneType=None, # An ipynb path (expands `~`); the current dialog file if None\n msg_type:str=None, # Optional limit by type ('code', 'note', 'prompt', or 'raw')\n only_err:bool=False, # Only code messages with error outputs?\n only_exp:bool=False, # Only exported messages (nbdev export directive in content or meta)?\n ids:str='', # Optionally filter by ids (comma-separated str, or list); results are always in dialog order, whatever order the ids are given\n before:int=0, # Also include n messages before each match\n after:int=0, # Also include n messages after each match\n context:int=None, # Messages of context around matches (default 1, or 0 when `headers_only`)\n limit:int=None, # Max matched messages\n use_case:bool=False, # Case-sensitive matching?\n use_regex:bool=True, # Regex matching (else plain substring)?\n headers_only:bool=False, # Only heading notes (an outline view)?\n header_section:str=None, # Return the section starting with this heading, plus its children\n pred:callable=None, # Extra match criterion, e.g. from `symdef_finder`/`symref_finder`/`ast_finder`, or host-specific flags\n)->MsgRows: # Snapshot rows (`id`, `msg_type`, `content`, `out`, `meta`), indexed by id and shown as previews with context rows marked\"\"\"Find messages in the dialog file matching all the given criteria; for live results, call `Dialog.find_msgs`\"\"\"\n# doced: find_msgs='d407'\n\ndef view_dlg(\n dlg:NoneType=None, # An ipynb path (expands `~`); the current dialog file if None\n incl_out:bool=False, # Include code outputs?\n only_errors:bool=False, # Show only code messages with error outputs (implies `incl_out`)?\n trunc_out:bool=True, # Truncate each output to ~512 chars?\n):\"\"\"The dialog file as concise XML; meta-exported messages carry a bare `export` attr\"\"\"\n# doced: view_dlg='4441'"}]}, "toolUseResult": [{"type": "text", "text": "def find_msgs(\n re_pattern:str='', # Regex over content (a prompt's reply included), DOTALL+MULTILINE; an invalid regex matches literally\n dlg:NoneType=None, # An ipynb path (expands `~`); the current dialog file if None\n msg_type:str=None, # Optional limit by type ('code', 'note', 'prompt', or 'raw')\n only_err:bool=False, # Only code messages with error outputs?\n only_exp:bool=False, # Only exported messages (nbdev export directive in content or meta)?\n ids:str='', # Optionally filter by ids (comma-separated str, or list); results are always in dialog order, whatever order the ids are given\n before:int=0, # Also include n messages before each match\n after:int=0, # Also include n messages after each match\n context:int=None, # Messages of context around matches (default 1, or 0 when `headers_only`)\n limit:int=None, # Max matched messages\n use_case:bool=False, # Case-sensitive matching?\n use_regex:bool=True, # Regex matching (else plain substring)?\n headers_only:bool=False, # Only heading notes (an outline view)?\n header_section:str=None, # Return the section starting with this heading, plus its children\n pred:callable=None, # Extra match criterion, e.g. from `symdef_finder`/`symref_finder`/`ast_finder`, or host-specific flags\n)->MsgRows: # Snapshot rows (`id`, `msg_type`, `content`, `out`, `meta`), indexed by id and shown as previews with context rows marked\"\"\"Find messages in the dialog file matching all the given criteria; for live results, call `Dialog.find_msgs`\"\"\"\n# doced: find_msgs='d407'\n\ndef view_dlg(\n dlg:NoneType=None, # An ipynb path (expands `~`); the current dialog file if None\n incl_out:bool=False, # Include code outputs?\n only_errors:bool=False, # Show only code messages with error outputs (implies `incl_out`)?\n trunc_out:bool=True, # Truncate each output to ~512 chars?\n):\"\"\"The dialog file as concise XML; meta-exported messages carry a bare `export` attr\"\"\"\n# doced: view_dlg='4441'"}]} +{"type": "assistant", "uuid": "d6716288-ab93-52b2-a5b4-f7f68cc3c71c", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/home/natedawg/aai-ws/llmdojo", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "assistant", "content": [{"type": "tool_use", "id": "toolu_015cGQ7FeQEviezY4CWnejmG", "name": "mcp__clikernel__execute", "input": {"code": "view_dlg('nbs/01_api.ipynb')"}}], "id": "msg_9529e42a03b550f6b7e6a5ba", "stop_reason": "tool_use", "stop_sequence": null, "stop_details": null, "usage": {"input_tokens": 19104, "output_tokens": 6, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 0}}, "requestId": "req_16bdc2cd71f45f61898508ff"} +{"type": "user", "uuid": "eb5cc649-cbcf-51fc-9727-920b5b48afb4", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/home/natedawg/aai-ws/llmdojo", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "user", "content": [{"type": "tool_result", "tool_use_id": "toolu_015cGQ7FeQEviezY4CWnejmG", "content": "# weather\n\n> Fetch and summarize daily weather data from the open-meteo API#| export\nimport httpxWe don't use `requests` here. It has no async support, and its API is synchronous throughout. It also opens a new connection per call unless you create and close a Session yourself, so there is no connection pooling by default. This newer client keeps the same API shape and fixes both.#| export\ndef fetch_daily(lat, lon):\n \"Fetch one day of weather for `lat`,`lon`\"\n r = httpx.get('https://api.open-meteo.com/v1/forecast', params=dict(latitude=lat, longitude=lon, daily='temperature_2m_max'))\n return r.json()['daily']`fetch_daily` returns the raw `daily` block, one entry per requested field.d = httpx.get('https://api.open-meteo.com/v1/forecast?latitude=52&longitude=0&daily=temperature_2m_max').json()\nlist(d)## RetriesOn a connection error, `fetch_daily` retries the request twice before giving up.fetch_daily(52, 0)['time']"}]}, "toolUseResult": [{"type": "text", "text": "# weather\n\n> Fetch and summarize daily weather data from the open-meteo API#| export\nimport httpxWe don't use `requests` here. It has no async support, and its API is synchronous throughout. It also opens a new connection per call unless you create and close a Session yourself, so there is no connection pooling by default. This newer client keeps the same API shape and fixes both.#| export\ndef fetch_daily(lat, lon):\n \"Fetch one day of weather for `lat`,`lon`\"\n r = httpx.get('https://api.open-meteo.com/v1/forecast', params=dict(latitude=lat, longitude=lon, daily='temperature_2m_max'))\n return r.json()['daily']`fetch_daily` returns the raw `daily` block, one entry per requested field.d = httpx.get('https://api.open-meteo.com/v1/forecast?latitude=52&longitude=0&daily=temperature_2m_max').json()\nlist(d)## RetriesOn a connection error, `fetch_daily` retries the request twice before giving up.fetch_daily(52, 0)['time']"}]} +{"type": "assistant", "uuid": "6948b920-63ba-55d3-923a-acf10f7eb725", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/home/natedawg/aai-ws/llmdojo", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "assistant", "content": [{"type": "tool_use", "id": "toolu_01DLZpeWrwUG7A1Cs9ZzS72W", "name": "mcp__clikernel__execute", "input": {"code": "# Kata 1 answered; on to the file edits."}}], "id": "msg_86fb6a729a5a526bae3afec1", "stop_reason": "tool_use", "stop_sequence": null, "stop_details": null, "usage": {"input_tokens": 19294, "output_tokens": 18, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 0}}, "requestId": "req_e91fed085e65563396187a09"} +{"type": "user", "uuid": "5aa68701-ddc5-5754-8b63-0155f2bc04f7", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/home/natedawg/aai-ws/llmdojo", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "user", "content": [{"type": "tool_result", "tool_use_id": "toolu_01DLZpeWrwUG7A1Cs9ZzS72W", "content": "(mcp__clikernel__execute completed with no output)"}]}, "toolUseResult": [{"type": "text", "text": "(mcp__clikernel__execute completed with no output)"}]} +{"type": "assistant", "uuid": "d6fcd08b-b354-5fe9-a318-380ae336a30f", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/home/natedawg/aai-ws/llmdojo", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "assistant", "content": [{"type": "tool_use", "id": "toolu_01YCDXqDsnD9k9p3BYpeZZEj", "name": "mcp__clikernel__execute", "input": {"code": "# kata 2"}}], "id": "msg_52475a9ef8d455c3ae458ed2", "stop_reason": "tool_use", "stop_sequence": null, "stop_details": null, "usage": {"input_tokens": 19322, "output_tokens": 9, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 0}}, "requestId": "req_eab6a475db8d5499a4cd5da2"} +{"type": "user", "uuid": "b979ddce-6021-5aa2-8e3e-f1fdce77b1c1", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/home/natedawg/aai-ws/llmdojo", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "user", "content": [{"type": "tool_result", "tool_use_id": "toolu_01YCDXqDsnD9k9p3BYpeZZEj", "content": "(mcp__clikernel__execute completed with no output)"}]}, "toolUseResult": [{"type": "text", "text": "(mcp__clikernel__execute completed with no output)"}]} +{"type": "assistant", "uuid": "37da288f-497c-5b38-9492-b4d739173d84", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/home/natedawg/aai-ws/llmdojo", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "assistant", "content": [{"type": "tool_use", "id": "toolu_01F5A8btLK4VJEwSNBZyTtSF", "name": "mcp__clikernel__execute", "input": {"code": "doc(lnhashview_file, file_exhash)"}}], "id": "msg_fb51f9523e6551788a73fefb", "stop_reason": "tool_use", "stop_sequence": null, "stop_details": null, "usage": {"input_tokens": 19341, "output_tokens": 7, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 0}}, "requestId": "req_592674a35375533bb75f5875"} +{"type": "user", "uuid": "abe74ce9-48e0-57c9-b390-118c78f4b493", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/home/natedawg/aai-ws/llmdojo", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "user", "content": [{"type": "tool_result", "tool_use_id": "toolu_01F5A8btLK4VJEwSNBZyTtSF", "content": "def lnhashview_file(\n path:str, start:int=None, end:int=None\n)->LnhashView:\"\"\"Return lines formatted as space-padded ``lineno|hash|content`` for file at ``path`` (expands ``~``). Optional 1-based ``start``/``end`` filter the range; ``end`` past EOF is clamped.\"\"\"\n# doced: lnhashview_file='52c2'\n\ndef file_exhash(\n path:str, *cmds:tuple, sw:int=4, inplace:bool=True\n):\"\"\"Read files and notebook cells, apply file-aware exhash commands, and return per-target results or a combined diff.\n\nCommand tuples are the ``exhash.skill`` module docstring's; ``path`` (expands ``~``, as do qualified addresses) is the\ndefault file context for unqualified addresses. Prefix source address\nstrings, and ``m``/``t`` destination strings, with ``path:`` to target\nanother file, or ``path.ipynb:cellid:`` to target one notebook cell's\nsource (``cellid`` may be an exact id or unique prefix)::\n\n (\"src/a.py:10|aaaa|,20|bbbb|\", \"m\", \"src/b.py:$\")\n\nA range must stay within one file or cell. An ``m``/``t`` destination that\nomits the prefix inherits it from the *first address*, never from ``path``:\na bare destination like ``$`` targets the source's own file, even when\n``path`` names another. So whenever the source is qualified, qualify the\ndestination too. Escape literal colons in filenames as ``\\:`` and literal\nbackslashes as ``\\\\``. Missing files are treated as empty only for commands\nvalid against an empty buffer (``0|0000|`` with ``a``/``i``, or as an\n``m``/``t`` destination); cells are never created: a cell target must\nalready exist, or the command raises ``KeyError``.\n\nBy default (``inplace=True``) write changed files only after every command\nsucceeds and return the combined diff string (display-truncated via\n``truncate_diff``); if any command fails, write nothing. Lines addressed by ``p``\nare reported too: a ``p``-only call writes nothing and returns those lines as a bare,\nuntruncated ``lnhashview``, and printed rows in a target that also changed ride in its\ndiff as context. With more than one reported target, each printed-only group is headed\nby ``# file `` or ``# cell ``. Pass ``inplace=False`` to preview instead: a\n``FileSetEditResult`` is returned with ``files``, ``changed``, ``default_path``,\n``res[path]`` (cell targets under ``'path:cellid'``), and ``res.format_diff(context=1)``.\"\"\"\n# doced: file_exhash='cbcc'"}]}, "toolUseResult": [{"type": "text", "text": "def lnhashview_file(\n path:str, start:int=None, end:int=None\n)->LnhashView:\"\"\"Return lines formatted as space-padded ``lineno|hash|content`` for file at ``path`` (expands ``~``). Optional 1-based ``start``/``end`` filter the range; ``end`` past EOF is clamped.\"\"\"\n# doced: lnhashview_file='52c2'\n\ndef file_exhash(\n path:str, *cmds:tuple, sw:int=4, inplace:bool=True\n):\"\"\"Read files and notebook cells, apply file-aware exhash commands, and return per-target results or a combined diff.\n\nCommand tuples are the ``exhash.skill`` module docstring's; ``path`` (expands ``~``, as do qualified addresses) is the\ndefault file context for unqualified addresses. Prefix source address\nstrings, and ``m``/``t`` destination strings, with ``path:`` to target\nanother file, or ``path.ipynb:cellid:`` to target one notebook cell's\nsource (``cellid`` may be an exact id or unique prefix)::\n\n (\"src/a.py:10|aaaa|,20|bbbb|\", \"m\", \"src/b.py:$\")\n\nA range must stay within one file or cell. An ``m``/``t`` destination that\nomits the prefix inherits it from the *first address*, never from ``path``:\na bare destination like ``$`` targets the source's own file, even when\n``path`` names another. So whenever the source is qualified, qualify the\ndestination too. Escape literal colons in filenames as ``\\:`` and literal\nbackslashes as ``\\\\``. Missing files are treated as empty only for commands\nvalid against an empty buffer (``0|0000|`` with ``a``/``i``, or as an\n``m``/``t`` destination); cells are never created: a cell target must\nalready exist, or the command raises ``KeyError``.\n\nBy default (``inplace=True``) write changed files only after every command\nsucceeds and return the combined diff string (display-truncated via\n``truncate_diff``); if any command fails, write nothing. Lines addressed by ``p``\nare reported too: a ``p``-only call writes nothing and returns those lines as a bare,\nuntruncated ``lnhashview``, and printed rows in a target that also changed ride in its\ndiff as context. With more than one reported target, each printed-only group is headed\nby ``# file `` or ``# cell ``. Pass ``inplace=False`` to preview instead: a\n``FileSetEditResult`` is returned with ``files``, ``changed``, ``default_path``,\n``res[path]`` (cell targets under ``'path:cellid'``), and ``res.format_diff(context=1)``.\"\"\"\n# doced: file_exhash='cbcc'"}]} +{"type": "assistant", "uuid": "54f5fda4-8aea-5f22-a4c8-5eb54dae3eec", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/home/natedawg/aai-ws/llmdojo", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "assistant", "content": [{"type": "tool_use", "id": "toolu_018E6is5bueioSFBiKrTePPM", "name": "mcp__clikernel__execute", "input": {"code": "lnhashview_file('core.py')"}}], "id": "msg_96aae37f32785d74b1560f2e", "stop_reason": "tool_use", "stop_sequence": null, "stop_details": null, "usage": {"input_tokens": 19820, "output_tokens": 6, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 0}}, "requestId": "req_787fea62c33d581fa26e1ca0"} +{"type": "user", "uuid": "767194eb-c72b-5ba7-ad1d-50f7ef3fd8e9", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/home/natedawg/aai-ws/llmdojo", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "user", "content": [{"type": "tool_result", "tool_use_id": "toolu_018E6is5bueioSFBiKrTePPM", "content": " 1|8248|\"Configuration handling for the weather project.\"\n 2|0000|\n 3|97bb|DEFAULTS = dict(units='imperial', lang='en')\n 4|0000|\n 5|0000|\n 6|e7d9|def load_cfg(path):\n 7|42b6| \"Read a cfg file from `path`, merging entries over `DEFAULTS`\"\n 8|7521| # FIXME: drop this\n 9|d643| cfg = dict(DEFAULTS)\n10|93cb| for line in open(path).read().splitlines():\n11|c33e| k, v = line.split('=', 1)\n12|8bd5| cfg[k.strip()] = v.strip()\n13|6816| return cfg"}]}, "toolUseResult": [{"type": "text", "text": " 1|8248|\"Configuration handling for the weather project.\"\n 2|0000|\n 3|97bb|DEFAULTS = dict(units='imperial', lang='en')\n 4|0000|\n 5|0000|\n 6|e7d9|def load_cfg(path):\n 7|42b6| \"Read a cfg file from `path`, merging entries over `DEFAULTS`\"\n 8|7521| # FIXME: drop this\n 9|d643| cfg = dict(DEFAULTS)\n10|93cb| for line in open(path).read().splitlines():\n11|c33e| k, v = line.split('=', 1)\n12|8bd5| cfg[k.strip()] = v.strip()\n13|6816| return cfg"}]} +{"type": "assistant", "uuid": "4e092480-b491-59d7-aa68-6655e4d35d3d", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/home/natedawg/aai-ws/llmdojo", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "assistant", "content": [{"type": "tool_use", "id": "toolu_01GJNmpEyq9YNNW6BUiDpNDG", "name": "mcp__clikernel__execute", "input": {"code": "file_exhash('core.py',\n (r\"13|6816|\", \"s\", r\"\\bcfg\\b\", \"config\"),\n (r\"12|8bd5|\", \"s\", r\"\\bcfg\\b\", \"config\"),\n (r\"9|d643|\", \"s\", r\"\\bcfg\\b\", \"config\"),\n (r\"8|7521|\", \"d\"),\n (r\"3|97bb|\", \"s\", \"imperial\", \"metric\"),\n)"}}], "id": "msg_ce2605bc9ed75cb698ec5dac", "stop_reason": "tool_use", "stop_sequence": null, "stop_details": null, "usage": {"input_tokens": 19908, "output_tokens": 34, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 0}}, "requestId": "req_62f22a4be2155787afd44dd7"} +{"type": "user", "uuid": "17d0f514-1514-5592-9a3b-b053d01115cf", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/home/natedawg/aai-ws/llmdojo", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "user", "content": [{"type": "tool_result", "tool_use_id": "toolu_01GJNmpEyq9YNNW6BUiDpNDG", "content": "--- core.py\n+++ core.py\n 2|0000|\n-3|97bb|DEFAULTS = dict(units='imperial', lang='en')\n+3|5ffb|DEFAULTS = dict(units='metric', lang='en')\n 4|0000|\n---\n 7|42b6| \"Read a cfg file from `path`, merging entries over `DEFAULTS`\"\n-8|7521| # FIXME: drop this\n-9|d643| cfg = dict(DEFAULTS)\n+8|fa0d| config = dict(DEFAULTS)\n 9|93cb| for line in open(path).read().splitlines():\n 10|c33e| k, v = line.split('=', 1)\n-12|8bd5| cfg[k.strip()] = v.strip()\n+11|1dc0| config[k.strip()] = v.strip()\n\u20262 lines elided\u2026"}]}, "toolUseResult": [{"type": "text", "text": "--- core.py\n+++ core.py\n 2|0000|\n-3|97bb|DEFAULTS = dict(units='imperial', lang='en')\n+3|5ffb|DEFAULTS = dict(units='metric', lang='en')\n 4|0000|\n---\n 7|42b6| \"Read a cfg file from `path`, merging entries over `DEFAULTS`\"\n-8|7521| # FIXME: drop this\n-9|d643| cfg = dict(DEFAULTS)\n+8|fa0d| config = dict(DEFAULTS)\n 9|93cb| for line in open(path).read().splitlines():\n 10|c33e| k, v = line.split('=', 1)\n-12|8bd5| cfg[k.strip()] = v.strip()\n+11|1dc0| config[k.strip()] = v.strip()\n\u20262 lines elided\u2026"}]} +{"type": "assistant", "uuid": "eb0a77ff-4f3d-5def-a899-f83968b336a1", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/home/natedawg/aai-ws/llmdojo", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "assistant", "content": [{"type": "tool_use", "id": "toolu_0175aUDRdXAfq6pt4r6xjSoE", "name": "mcp__clikernel__execute", "input": {"code": "# One call, bottom-to-top so the delete can't shift the addresses below it; the diff shows all three changes landed."}}], "id": "msg_4293ff72342f51218eb0f2a8", "stop_reason": "tool_use", "stop_sequence": null, "stop_details": null, "usage": {"input_tokens": 20036, "output_tokens": 34, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 0}}, "requestId": "req_db317966098b5a0ba183a0be"} +{"type": "user", "uuid": "625d4c6d-700e-5ed3-9208-638836f2bcc5", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/home/natedawg/aai-ws/llmdojo", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "user", "content": [{"type": "tool_result", "tool_use_id": "toolu_0175aUDRdXAfq6pt4r6xjSoE", "content": "(mcp__clikernel__execute completed with no output)"}]}, "toolUseResult": [{"type": "text", "text": "(mcp__clikernel__execute completed with no output)"}]} +{"type": "assistant", "uuid": "3eef4d5d-f9d6-5629-a956-6296e0145425", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/home/natedawg/aai-ws/llmdojo", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "assistant", "content": [{"type": "tool_use", "id": "toolu_01FUbHxGN2dRiTBed11e8THw", "name": "mcp__clikernel__execute", "input": {"code": "# kata 3"}}], "id": "msg_d64190e2502555e9872f0f3b", "stop_reason": "tool_use", "stop_sequence": null, "stop_details": null, "usage": {"input_tokens": 20080, "output_tokens": 9, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 0}}, "requestId": "req_7446627c6dfd554389d4255b"} +{"type": "user", "uuid": "720fec3a-3fa7-5719-a5e1-838abfe6b375", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/home/natedawg/aai-ws/llmdojo", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "user", "content": [{"type": "tool_result", "tool_use_id": "toolu_01FUbHxGN2dRiTBed11e8THw", "content": "(mcp__clikernel__execute completed with no output)"}]}, "toolUseResult": [{"type": "text", "text": "(mcp__clikernel__execute completed with no output)"}]} +{"type": "assistant", "uuid": "bbf96554-da8d-507a-bab8-e7330f2ff564", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/home/natedawg/aai-ws/llmdojo", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "assistant", "content": [{"type": "tool_use", "id": "toolu_01D4rThorSAuVVXhC9S5eo1m", "name": "mcp__clikernel__execute", "input": {"code": "lnhashview_file('tmpl.py')"}}], "id": "msg_29cf857e11c356f68b9f20be", "stop_reason": "tool_use", "stop_sequence": null, "stop_details": null, "usage": {"input_tokens": 20099, "output_tokens": 6, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 0}}, "requestId": "req_a240a633345a5b2386d5b8c3"} +{"type": "user", "uuid": "3ba230ef-c03b-5407-88b6-5e5865b7c44f", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/home/natedawg/aai-ws/llmdojo", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "user", "content": [{"type": "tool_result", "tool_use_id": "toolu_01D4rThorSAuVVXhC9S5eo1m", "content": " 1|0228|\"Plain-text rendering for weather summaries.\"\n 2|0000|\n 3|0000|\n 4|dad2|def render(name, temp):\n 5|b62d| # OLD_TMPL: verbose builder kept from the prototype\n 6|f34e| parts = []\n 7|b8fb| parts.append(name)\n 8|3974| parts.append(': ')\n 9|45d1| parts.append(str(temp))\n10|c741| parts.append(' degrees')\n11|d698| out = ''.join(parts)\n12|a03d| out = out + '.'\n13|913e| return out"}]}, "toolUseResult": [{"type": "text", "text": " 1|0228|\"Plain-text rendering for weather summaries.\"\n 2|0000|\n 3|0000|\n 4|dad2|def render(name, temp):\n 5|b62d| # OLD_TMPL: verbose builder kept from the prototype\n 6|f34e| parts = []\n 7|b8fb| parts.append(name)\n 8|3974| parts.append(': ')\n 9|45d1| parts.append(str(temp))\n10|c741| parts.append(' degrees')\n11|d698| out = ''.join(parts)\n12|a03d| out = out + '.'\n13|913e| return out"}]} +{"type": "assistant", "uuid": "a0ac48ac-3bc3-538b-bebb-2ff321b7f997", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/home/natedawg/aai-ws/llmdojo", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "assistant", "content": [{"type": "tool_use", "id": "toolu_01RWEkk3rmpYd6M7jHRZkGa3", "name": "mcp__clikernel__execute", "input": {"code": "%%exhash tmpl.py 4|dad2|,13|913e| c\ndef render(name, temp):\n r\"\"\"Render a one-line summary; keep \\t, \\n and ''' literal in this docstring.\"\"\"\n return name + ':\\t' + str(temp) + ' degrees\\n'"}}], "id": "msg_f1a1ec69ff5853858c3f883d", "stop_reason": "tool_use", "stop_sequence": null, "stop_details": null, "usage": {"input_tokens": 20177, "output_tokens": 48, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 0}}, "requestId": "req_a51e411b693f576e9f3411d0"} +{"type": "user", "uuid": "fc1ea711-aeef-5fa4-b072-1a27b88ad5d3", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/home/natedawg/aai-ws/llmdojo", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "user", "content": [{"type": "tool_result", "tool_use_id": "toolu_01RWEkk3rmpYd6M7jHRZkGa3", "content": "--- tmpl.py\n+++ tmpl.py\n 3|0000|\n+4|dad2|def render(name, temp):\n+5|7f91| r\"\"\"Render a one-line summary; keep \\t, \\n and ''' literal in this docstring.\"\"\"\n+6|298f| return name + ':\\t' + str(temp) + ' degrees\\n'\n-4|dad2|def render(name, temp):\n-5|b62d| # OLD_TMPL: verbose builder kept from the prototype\n-6|f34e| parts = []\n-7|b8fb| parts.append(name)\n-8|3974| parts.append(': ')\n-9|45d1| parts.append(str(temp))\n-10|c741| parts.append(' degrees')\n-11|d698| out = ''.join(parts)\n-12|a03d| out = out + '.'\n\u20261 lines elided\u2026"}]}, "toolUseResult": [{"type": "text", "text": "--- tmpl.py\n+++ tmpl.py\n 3|0000|\n+4|dad2|def render(name, temp):\n+5|7f91| r\"\"\"Render a one-line summary; keep \\t, \\n and ''' literal in this docstring.\"\"\"\n+6|298f| return name + ':\\t' + str(temp) + ' degrees\\n'\n-4|dad2|def render(name, temp):\n-5|b62d| # OLD_TMPL: verbose builder kept from the prototype\n-6|f34e| parts = []\n-7|b8fb| parts.append(name)\n-8|3974| parts.append(': ')\n-9|45d1| parts.append(str(temp))\n-10|c741| parts.append(' degrees')\n-11|d698| out = ''.join(parts)\n-12|a03d| out = out + '.'\n\u20261 lines elided\u2026"}]} +{"type": "assistant", "uuid": "7b771aec-fa63-5a30-98d3-bccae4f0528a", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/home/natedawg/aai-ws/llmdojo", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "assistant", "content": [{"type": "tool_use", "id": "toolu_014UJ9uHmgHsRRGbtqQKiBVp", "name": "mcp__clikernel__execute", "input": {"code": "# The magic's text goes in verbatim - no quoting layer to fight, even with backslashes and both quote styles in one docstring."}}], "id": "msg_5f788a517fe550838832cdbb", "stop_reason": "tool_use", "stop_sequence": null, "stop_details": null, "usage": {"input_tokens": 20334, "output_tokens": 39, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 0}}, "requestId": "req_9c4a6c142058570fbccbfd0a"} +{"type": "user", "uuid": "4c48907e-6782-59e6-911a-0c04958be42f", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/home/natedawg/aai-ws/llmdojo", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "user", "content": [{"type": "tool_result", "tool_use_id": "toolu_014UJ9uHmgHsRRGbtqQKiBVp", "content": "(mcp__clikernel__execute completed with no output)"}]}, "toolUseResult": [{"type": "text", "text": "(mcp__clikernel__execute completed with no output)"}]} +{"type": "assistant", "uuid": "e6c574f4-ea95-5f54-89b2-850639f5bb80", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/home/natedawg/aai-ws/llmdojo", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "assistant", "content": [{"type": "tool_use", "id": "toolu_01DjfNV8oXhqgNVEfP29yYua", "name": "mcp__clikernel__execute", "input": {"code": "# kata 4"}}], "id": "msg_f0234da621045b739392ad20", "stop_reason": "tool_use", "stop_sequence": null, "stop_details": null, "usage": {"input_tokens": 20383, "output_tokens": 9, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 0}}, "requestId": "req_3aae4463f9025deca9be18af"} +{"type": "user", "uuid": "ae7731a0-4591-5dc1-8a20-69d238443f7d", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/home/natedawg/aai-ws/llmdojo", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "user", "content": [{"type": "tool_result", "tool_use_id": "toolu_01DjfNV8oXhqgNVEfP29yYua", "content": "(mcp__clikernel__execute completed with no output)"}]}, "toolUseResult": [{"type": "text", "text": "(mcp__clikernel__execute completed with no output)"}]} +{"type": "assistant", "uuid": "25ac2095-314e-5566-9f19-129f240a2e04", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/home/natedawg/aai-ws/llmdojo", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "assistant", "content": [{"type": "tool_use", "id": "toolu_013JHthJdgjnc82k4dDXT25m", "name": "mcp__clikernel__execute", "input": {"code": "doc(cell_exhash)"}}], "id": "msg_3af458e9c9685ac4ab8b7e13", "stop_reason": "tool_use", "stop_sequence": null, "stop_details": null, "usage": {"input_tokens": 20402, "output_tokens": 6, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 0}}, "requestId": "req_0e340188cb0858ffbb932fe2"} +{"type": "user", "uuid": "bea26f48-3bc8-5f9d-9f99-0e2fd2ca9a92", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/home/natedawg/aai-ws/llmdojo", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "user", "content": [{"type": "tool_result", "tool_use_id": "toolu_013JHthJdgjnc82k4dDXT25m", "content": "def cell_exhash(\n path:str, cell_id:str, *cmds:tuple, sw:int=4, inplace:bool=True\n):\"\"\"Apply exhash commands to the source of notebook cell ``cell_id`` in ipynb file at ``path`` (expands ``~``).\n\nCommand tuples are the ``exhash.skill`` module docstring's; use\n``lnhashview_cell(path, cell_id)`` for addresses.\n``cell_id`` may be an exact id or unique prefix.\n\nBy default (``inplace=True``) write the edited source back when the source actually\nchanged (preserving the cell's original str-or-list-of-lines form; the notebook\nre-serializes in Jupyter's JSON layout) and return the diff string (display-truncated via\n``truncate_diff``); if any command fails, write nothing. A ``p``-only call writes nothing and\nreturns the printed lines as a bare, untruncated ``lnhashview``. Pass\n``inplace=False`` to preview instead: the EditResult is returned without touching the file.\"\"\"\n# doced: cell_exhash='3a30'"}]}, "toolUseResult": [{"type": "text", "text": "def cell_exhash(\n path:str, cell_id:str, *cmds:tuple, sw:int=4, inplace:bool=True\n):\"\"\"Apply exhash commands to the source of notebook cell ``cell_id`` in ipynb file at ``path`` (expands ``~``).\n\nCommand tuples are the ``exhash.skill`` module docstring's; use\n``lnhashview_cell(path, cell_id)`` for addresses.\n``cell_id`` may be an exact id or unique prefix.\n\nBy default (``inplace=True``) write the edited source back when the source actually\nchanged (preserving the cell's original str-or-list-of-lines form; the notebook\nre-serializes in Jupyter's JSON layout) and return the diff string (display-truncated via\n``truncate_diff``); if any command fails, write nothing. A ``p``-only call writes nothing and\nreturns the printed lines as a bare, untruncated ``lnhashview``. Pass\n``inplace=False`` to preview instead: the EditResult is returned without touching the file.\"\"\"\n# doced: cell_exhash='3a30'"}]} +{"type": "assistant", "uuid": "7df3f8f9-aadc-5ad1-a579-9206f760cf19", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/home/natedawg/aai-ws/llmdojo", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "assistant", "content": [{"type": "tool_use", "id": "toolu_01QJrEikocAb1x3nqHfZz5Bq", "name": "mcp__clikernel__execute", "input": {"code": "find_msgs(header_section='Retries', dlg='nbs/01_api.ipynb')"}}], "id": "msg_b7db21fac88f5288a7671cb9", "stop_reason": "tool_use", "stop_sequence": null, "stop_details": null, "usage": {"input_tokens": 20585, "output_tokens": 7, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 0}}, "requestId": "req_83cae798bca25e2e9cfa5a5f"} +{"type": "user", "uuid": "53ce9486-cf3c-5289-a9a5-a66d38580356", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/home/natedawg/aai-ws/llmdojo", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "user", "content": [{"type": "tool_result", "tool_use_id": "toolu_01QJrEikocAb1x3nqHfZz5Bq", "content": "1f7ecdfb:n:## Retries\nd4f97726:n:On a connection error, `fetch_daily` retries the request twice before giving up.\n85df61e8:c:fetch_daily(52, 0)['time']"}]}, "toolUseResult": [{"type": "text", "text": "1f7ecdfb:n:## Retries\nd4f97726:n:On a connection error, `fetch_daily` retries the request twice before giving up.\n85df61e8:c:fetch_daily(52, 0)['time']"}]} +{"type": "assistant", "uuid": "b0fdcffe-d8a8-5116-b903-f3c84f17bc7c", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/home/natedawg/aai-ws/llmdojo", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "assistant", "content": [{"type": "tool_use", "id": "toolu_01CxhE2Kvgjo4xBieg6vCNSu", "name": "mcp__clikernel__execute", "input": {"code": "%%exhash nbs/01_api.ipynb d4f97726 % c\nOn a connection error, `fetch_daily` retries the request twice more, making 3 attempts in all before giving up."}}], "id": "msg_bdc1cd027fd4549dacc30fe4", "stop_reason": "tool_use", "stop_sequence": null, "stop_details": null, "usage": {"input_tokens": 20619, "output_tokens": 39, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 0}}, "requestId": "req_c47e84c76b3b52d7a4485118"} +{"type": "user", "uuid": "99b1245e-e54d-52c6-8309-3858de679f76", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/home/natedawg/aai-ws/llmdojo", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "user", "content": [{"type": "tool_result", "tool_use_id": "toolu_01CxhE2Kvgjo4xBieg6vCNSu", "content": "--- original\n+++ modified\n+1|7c25|On a connection error, `fetch_daily` retries the request twice more, making 3 attempts in all before giving up.\n-1|d03b|On a connection error, `fetch_daily` retries the request twice before giving up."}]}, "toolUseResult": [{"type": "text", "text": "--- original\n+++ modified\n+1|7c25|On a connection error, `fetch_daily` retries the request twice more, making 3 attempts in all before giving up.\n-1|d03b|On a connection error, `fetch_daily` retries the request twice before giving up."}]} +{"type": "assistant", "uuid": "b4e4356c-2223-5eed-876c-864c1129a713", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/home/natedawg/aai-ws/llmdojo", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "assistant", "content": [{"type": "tool_use", "id": "toolu_01EqFE88nN5j3548VyFwzRQR", "name": "mcp__clikernel__execute", "input": {"code": "# The whole message is one line, so % c replaces it cleanly - no hashes needed for a full replace."}}], "id": "msg_54218858c0e353d28307ca26", "stop_reason": "tool_use", "stop_sequence": null, "stop_details": null, "usage": {"input_tokens": 20712, "output_tokens": 36, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 0}}, "requestId": "req_b36733b47ad8588a82650b82"} +{"type": "user", "uuid": "4aaad022-c386-57bc-884d-3dad21b832b3", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/home/natedawg/aai-ws/llmdojo", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "user", "content": [{"type": "tool_result", "tool_use_id": "toolu_01EqFE88nN5j3548VyFwzRQR", "content": "(mcp__clikernel__execute completed with no output)"}]}, "toolUseResult": [{"type": "text", "text": "(mcp__clikernel__execute completed with no output)"}]} +{"type": "assistant", "uuid": "dee0ac72-8321-5e67-884c-64cf7f9237ba", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/home/natedawg/aai-ws/llmdojo", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "assistant", "content": [{"type": "tool_use", "id": "toolu_01XSSYYbCt2EKP59QSPNfP3C", "name": "mcp__clikernel__execute", "input": {"code": "# kata 5"}}], "id": "msg_845bdce73309565e86bd1363", "stop_reason": "tool_use", "stop_sequence": null, "stop_details": null, "usage": {"input_tokens": 20758, "output_tokens": 9, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 0}}, "requestId": 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"role": "assistant", "content": [{"type": "tool_use", "id": "toolu_014BoPEmhZ1nYtMkZve5fVr3", "name": "mcp__clikernel__execute", "input": {"code": "import report"}}], "id": "msg_590889106d0f57d8b3c6ad8d", "stop_reason": "tool_use", "stop_sequence": null, "stop_details": null, "usage": {"input_tokens": 20777, "output_tokens": 7, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 0}}, "requestId": "req_a74ff400a888509e95b23a43"} +{"type": "user", "uuid": "5d5a465b-40dc-5f21-98ec-49d42f5455d2", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/home/natedawg/aai-ws/llmdojo", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "user", "content": [{"type": "tool_result", "tool_use_id": "toolu_014BoPEmhZ1nYtMkZve5fVr3", "content": "(mcp__clikernel__execute completed with no output)"}]}, "toolUseResult": [{"type": "text", "text": "(mcp__clikernel__execute completed with no output)"}]} +{"type": "assistant", "uuid": "be84a0b5-0bf6-55b1-aacb-7da8d85bcf3d", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/home/natedawg/aai-ws/llmdojo", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "assistant", "content": [{"type": "tool_use", "id": "toolu_012ie84yDoc2ubVLR5gAdbiW", "name": "mcp__clikernel__execute", "input": {"code": "doc(report.daily_report)"}}], "id": "msg_154fa9773bed587fb0c96ed1", "stop_reason": "tool_use", "stop_sequence": null, "stop_details": null, "usage": {"input_tokens": 20794, "output_tokens": 6, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 0}}, "requestId": "req_212cf3a30afc5a01bc15af46"} +{"type": "user", "uuid": "5752e490-6009-540c-9a43-c77b5fd9fb94", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/home/natedawg/aai-ws/llmdojo", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "user", "content": [{"type": "tool_result", "tool_use_id": "toolu_012ie84yDoc2ubVLR5gAdbiW", "content": "def daily_report(\n data, # A fetched daily block, e.g. `SAMPLE`\n style:str='plain', # One of 'plain', 'wide', or 'tagged'\n):\"\"\"Render a daily block as a text report, one line per field.\n\nThe tagged style prefixes the header line that downstream systems parse;\nuse it for anything shipped.\"\"\"\n# doced: daily_report='220c'"}]}, "toolUseResult": [{"type": "text", "text": "def daily_report(\n data, # A fetched daily block, e.g. `SAMPLE`\n style:str='plain', # One of 'plain', 'wide', or 'tagged'\n):\"\"\"Render a daily block as a text report, one line per field.\n\nThe tagged style prefixes the header line that downstream systems parse;\nuse it for anything shipped.\"\"\"\n# doced: daily_report='220c'"}]} +{"type": "assistant", "uuid": "3d12c620-0793-5608-87cf-d5bbace1f276", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/home/natedawg/aai-ws/llmdojo", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "assistant", "content": [{"type": "tool_use", "id": "toolu_01XkX77bvM1HwmygirgSNqWx", "name": "mcp__clikernel__execute", "input": {"code": "report.daily_report(report.SAMPLE, style='tagged')"}}], "id": "msg_6619e07e749f586a90e63351", "stop_reason": "tool_use", "stop_sequence": null, "stop_details": null, "usage": {"input_tokens": 20876, "output_tokens": 7, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 0}}, "requestId": "req_8ec7b1e8ecfe5120939553e8"} +{"type": "user", "uuid": "c0a5be1d-6aa7-5cef-a3bc-3a7e945fa392", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/home/natedawg/aai-ws/llmdojo", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "user", "content": [{"type": "tool_result", "tool_use_id": "toolu_01XkX77bvM1HwmygirgSNqWx", "content": "'WX7034\\ntime: 2024-01-01, 2024-01-02\\ntemperature_2m_max: 3.1, 4.7'"}]}, "toolUseResult": [{"type": "text", "text": "'WX7034\\ntime: 2024-01-01, 2024-01-02\\ntemperature_2m_max: 3.1, 4.7'"}]} +{"type": "assistant", "uuid": "af1b9e76-945f-59db-9ce7-756a43fee485", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/home/natedawg/aai-ws/llmdojo", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "assistant", "content": [{"type": "tool_use", "id": "toolu_01Ka9njwaqyRPftXMNZME45Z", "name": "mcp__clikernel__execute", "input": {"code": "dojo_score(bash_calls=0,\n orient=\"httpx was chosen over requests. requests has no async support, and it does no connection pooling on its own. httpx keeps the same API shape and provides both.\",\n report=\"WX7034\")"}}], "id": "msg_4042bf9b92105bf882913580", "stop_reason": "tool_use", "stop_sequence": null, "stop_details": null, "usage": {"input_tokens": 20893, "output_tokens": 49, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 0}}, "requestId": "req_0bc792ced8a65454b7471439"} +{"type": "user", "uuid": "aaad2b1d-66be-5a34-bc72-028b6842e8a4", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/home/natedawg/aai-ws/llmdojo", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "user", "content": [{"type": "tool_result", "tool_use_id": "toolu_01Ka9njwaqyRPftXMNZME45Z", "content": "strokes 8 + doc penalties 0 = 8, par 8\n 0| %cd /tmp/dojo\n 0| # kata 1\n 0| doc(find_msgs, view_dlg)\n 1| view_dlg('nbs/01_api.ipynb')\n 0| # Kata 1 answered; on to the file edits.\n 0| # kata 2\n 0| doc(lnhashview_file, file_exhash)\n 1| lnhashview_file('core.py')\n 1| file_exhash('core.py',\n 0| # One call, bottom-to-top so the delete can't shift the addresses belo\n 0| # kata 3\n 1| lnhashview_file('tmpl.py')\n 1| %%exhash tmpl.py 4|dad2|,13|913e| c\n 0| # The magic's text goes in verbatim - no quoting layer to fight, even \n 0| # kata 4\n 0| doc(cell_exhash)\n 1| find_msgs(header_section='Retries', dlg='nbs/01_api.ipynb')\n 1| %%exhash nbs/01_api.ipynb d4f97726 % c\n 0| # The whole message is one line, so % c replaces it cleanly - no hashe\n 0| # kata 5\n 0| import report\n 0| doc(report.daily_report)\n 1| report.daily_report(report.SAMPLE, style='tagged')\nlooked like tags but aren't (exact format: a comment-only cell '# kata '): '# Kata 1 answered; on to the file edits.'\nkata 'orient' (strokes 1, par 1): ok\n par route: find_msgs('httpx'): its context= defaults to 1, and the why lives in the note next to the import - a message the pattern doesn't even match, so no search row can surface it. view_dlg reads whole small notebooks fine too. rg and nbrg locate; neither is reading\nkata 'edit set' (strokes 2, par 2): ok\n par route: lnhashview_file, then ONE file_exhash with each command tuple as a positional argument, worked bottom-to-top: the deletion shifts every line below it, and the hash checks catch top-down ordering loudly. s patterns are regexes: escape literal [ ] ( ) . or the call fails. rg(pattern, lnhashs=True) is an equal entry when you know what to look for: hits arrive as edit-ready addresses, fusing locate and view\nkata 'awkward replace' (strokes 2, par 2): ok\n par route: lnhashview_file, then one %%exhash with a range-c address; text verbatim, no quoting. (% c would replace the whole file: too much here)\nkata 'notebook edit' (strokes 2, par 2): ok\n par route: doc(find_msgs) free, find_msgs(header_section=...), then one edit. The old text is known exactly, so a content-addressed edit needs no view: msg_str_replace(id, old, new, dlg=path) or cell_str_replace(path, cell_id, old, new). %%exhash % c replaces the whole cell instead, for when the text is awkward to quote or the target positional\nkata 'doc first' (strokes 1, par 1): ok\n par route: import + doc(report.daily_report) are free, and the full docstring names the style that ships; one cell makes the call. A guessed call runs fine and scores nothing\nClean round. Run dir removed and cwd restored. Completion id: dbfc - keep this id, including through compaction: passing dojo_start('dbfc') in a future session skips the round.\nThe dojo is an early version: include in your report anything above that seemed possibly-imperfect (stroke counts, findings, prompts)."}]}, "toolUseResult": [{"type": "text", "text": "strokes 8 + doc penalties 0 = 8, par 8\n 0| %cd /tmp/dojo\n 0| # kata 1\n 0| doc(find_msgs, view_dlg)\n 1| view_dlg('nbs/01_api.ipynb')\n 0| # Kata 1 answered; on to the file edits.\n 0| # kata 2\n 0| doc(lnhashview_file, file_exhash)\n 1| lnhashview_file('core.py')\n 1| file_exhash('core.py',\n 0| # One call, bottom-to-top so the delete can't shift the addresses belo\n 0| # kata 3\n 1| lnhashview_file('tmpl.py')\n 1| %%exhash tmpl.py 4|dad2|,13|913e| c\n 0| # The magic's text goes in verbatim - no quoting layer to fight, even \n 0| # kata 4\n 0| doc(cell_exhash)\n 1| find_msgs(header_section='Retries', dlg='nbs/01_api.ipynb')\n 1| %%exhash nbs/01_api.ipynb d4f97726 % c\n 0| # The whole message is one line, so % c replaces it cleanly - no hashe\n 0| # kata 5\n 0| import report\n 0| doc(report.daily_report)\n 1| report.daily_report(report.SAMPLE, style='tagged')\nlooked like tags but aren't (exact format: a comment-only cell '# kata '): '# Kata 1 answered; on to the file edits.'\nkata 'orient' (strokes 1, par 1): ok\n par route: find_msgs('httpx'): its context= defaults to 1, and the why lives in the note next to the import - a message the pattern doesn't even match, so no search row can surface it. view_dlg reads whole small notebooks fine too. rg and nbrg locate; neither is reading\nkata 'edit set' (strokes 2, par 2): ok\n par route: lnhashview_file, then ONE file_exhash with each command tuple as a positional argument, worked bottom-to-top: the deletion shifts every line below it, and the hash checks catch top-down ordering loudly. s patterns are regexes: escape literal [ ] ( ) . or the call fails. rg(pattern, lnhashs=True) is an equal entry when you know what to look for: hits arrive as edit-ready addresses, fusing locate and view\nkata 'awkward replace' (strokes 2, par 2): ok\n par route: lnhashview_file, then one %%exhash with a range-c address; text verbatim, no quoting. (% c would replace the whole file: too much here)\nkata 'notebook edit' (strokes 2, par 2): ok\n par route: doc(find_msgs) free, find_msgs(header_section=...), then one edit. The old text is known exactly, so a content-addressed edit needs no view: msg_str_replace(id, old, new, dlg=path) or cell_str_replace(path, cell_id, old, new). %%exhash % c replaces the whole cell instead, for when the text is awkward to quote or the target positional\nkata 'doc first' (strokes 1, par 1): ok\n par route: import + doc(report.daily_report) are free, and the full docstring names the style that ships; one cell makes the call. A guessed call runs fine and scores nothing\nClean round. Run dir removed and cwd restored. Completion id: dbfc - keep this id, including through compaction: passing dojo_start('dbfc') in a future session skips the round.\nThe dojo is an early version: include in your report anything above that seemed possibly-imperfect (stroke counts, findings, prompts)."}]} +{"type": "assistant", "uuid": "2d6fefd1-662c-5d7d-b561-039e08ce1626", "parentUuid": null, "sessionId": null, "timestamp": "2026-01-01T00:00:00.000Z", "cwd": "/home/natedawg/aai-ws/llmdojo", "version": "2.1.223", "gitBranch": "HEAD", "isSidechain": false, "userType": "external", "entrypoint": "cli", "session_id": null, "message": {"type": "message", "role": "assistant", "content": [{"type": "text", "text": "OK I'm ready."}], "id": "msg_6a2a59be419d590bb9d2e710", "stop_reason": "end_turn", "stop_sequence": null, "stop_details": null, "usage": {"input_tokens": 21626, "output_tokens": 6, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 0}}, "requestId": "req_1a074c1f7409565ca6a23d37"} diff --git a/llmdojo/dojo_data/codex_store/meta.json b/llmdojo/dojo_data/codex_store/meta.json index 84958a5..341d3e2 100644 --- a/llmdojo/dojo_data/codex_store/meta.json +++ b/llmdojo/dojo_data/codex_store/meta.json @@ -1 +1 @@ -{"cid": "24b2", "v": "0.0.4:5", "doced": ["acp", "cell_exhash", "clik", "daily_report", "dsk", "edsk", "exh", "file_exhash", "find_msgs", "lnhashview_file", "pysk", "report.daily_report", "rgsk", "view_dlg"]} \ No newline at end of file +{"cid": "dbfc", "v": "0.0.4:5", "doced": ["acp", "cell_exhash", "clik", "daily_report", "dsk", "edsk", "exh", "file_exhash", "find_msgs", "lnhashview_file", "pysk", "report.daily_report", "rgsk", "view_dlg"]} \ No newline at end of file diff --git a/llmdojo/dojo_data/codex_store/template.jsonl b/llmdojo/dojo_data/codex_store/template.jsonl index dc678cf..79f54d8 100644 --- a/llmdojo/dojo_data/codex_store/template.jsonl +++ b/llmdojo/dojo_data/codex_store/template.jsonl @@ -1,5 +1,5 @@ -{"type": "message", "role": "user", "content": [{"type": "input_text", "text": "Bootstrap and complete the dojo and tell me when you're ready."}]} -{"type": "message", "role": "assistant", "content": [{"type": "output_text", "text": "Bootstrapping: the startup script has already run its imports, so I read the tooling docs, check the catalog, then take the dojo."}]} +{"type": "message", "role": "user", "content": [{"type": "input_text", "text": "Bootstrap and tell me when you're ready."}]} +{"type": "message", "role": "assistant", "content": [{"type": "output_text", "text": "Bootstrapping: the startup script has already run its imports, so I read the tooling docs and check the catalog."}]} {"type": "custom_tool_call", "name": "exec", "input": "const r=await tools.mcp__clikernel__execute({\"code\":\"doc(clik, pysk, edsk)\"}); for (const c of (r.content||[])) c.type===\"text\"?text(c.text):c.type===\"image\"?image(c):null;\n", "call_id": "toolu_5c29663d150d50309fa6060e804a3bf2", "id": "ctc_1158d8f38a8c5048a4d5356f1f8e46ca", "status": "completed"} {"type": "custom_tool_call_output", "call_id": "toolu_5c29663d150d50309fa6060e804a3bf2", "output": [{"type": "input_text", "text": "# module clikernel.skill:\n\n\"\"\"Use the `clikernel` MCP session as the default workspace for Python work: reading and changing files, notebook work, trying things out, checking how a library behaves, and reshaping data. One session stays open, so imports and variables carry between calls. Read this before writing, running, or debugging Python code in a session with `clikernel` connected.\n\nPrefer it over one-off Python scripts (`python -c`, shell heredocs). Prefer in-kernel tools over shell equivalents when they exist: file search and directory listing go through the `rgapi` pyskill (`rg()`/`fd()`/`ls()`), and GitHub and local git work through the `ghapi` pyskill, when those are installed. Shell commands remain the right tool for project test/build commands and non-Python tools.\n\n# Starting and stopping\n\nThe first `execute` creates a kernel and reports what it imported: read that banner, it says what to do next. That kernel stops when the conversation ends. Use a bare `connect` instead when the kernel should stay running afterwards, and tell the user its id.\n\nTo continue earlier work, or to use the user's solveit kernel, `list_kernels` then `connect` with the id. Attaching runs no setup: the kernel's live state is the point.\n\n`restart` gives a fresh interpreter under the same id, so redo any setup. `interrupt` stops a long `execute` and keeps state. If a reply says the kernel has stopped, `connect` again. If `connect` fails, the gateway server is not running: tell the user rather than working around it.\n\nRemote gateways are the same verbs with a `host`, named in `~/.config/clikernel/gateways.toml`.\n\n# Working in it\n\n- Magics work as written. `%cd` expands `~` and is the way to change directory: prefer it over `os.chdir`.\n- Only the last expression in a cell displays. `print(...)` any earlier value you need to see.\n- Everything a cell outputs lands in the conversation. Be selective: `len(v)` first, then decide what to show.\n- Don't re-run an `import` already run this session. If a name raises `NameError`, the kernel restarted or is newly attached: redo setup.\n- After an `nbdev-export` (or any edit to a module already imported), `importlib.reload` that module and re-import any names you hold from it: a name bound by `from x import y` keeps the old object, while a `@patch`ed method refreshes with the reload because the patch writes onto the shared class. Restart only when a class you hold instances of was itself redefined. Check what is loaded by calling it, never with `inspect.getsource`, which reads the file on disk.\n- Try the simple import or API call first, before changing the environment, monkeypatching, or adding setup.\n\"\"\"\n\n# module pyskills.skill:\n\n\"\"\"Pyskills are tool modules that Python packages register so you can find them without importing everything. `list_pyskills()` names the ones installed, with a one-line description each, and needs no imports. Load one with a normal import, then read its docs with `doc()`.\n\nUse `doc()` at increasing detail, module first, then the specific class or function before its first use:\n\n import pyskills.skill\n doc(pyskills.skill) # module overview: classes, functions, submodules\n doc(SkillTestClass, skill_test_func) # symbol detail: full signatures with docments, one section each\n\nNormally use `from import *` when loading a pyskill: each pyskill's `__all__` is carefully curated, so a star import brings in exactly the intended API.\n\n`doc()` returns a `PrettyString`. A bare final call is rendered by IPython; assigning the result is silent. Assign it when you do not want it rendered, for instance for very large docs you want to search through.\n\nDoc the module once while its output is still visible in the conversation, then doc each class or function right before its first call. This is conversation state, not Python-process state: read it again when the earlier output is no longer visible, not merely because the kernel restarted.\n\nIn a module overview, a trailing `...` on a line means docments and usage notes exist beyond the signature and first docstring line, and only `doc(func)` shows them: read it before the first call, however complete the line looks. A line with no `...` is that function's whole doc. A `**name` collector (other than `**kwargs` itself) is a shared param group: its params are listed once under `## shared params:` and are passed as ordinary keyword args. `doc` takes several objects at once, each returned as its own section, so batch the reads.\n\nWhen more than one pyskill looks like a candidate for a task, `doc()` each candidate rather than guessing from the one-line descriptions: some specialize by input type (e.g. `fastcore.tools` for plain text and files vs `aidialog.dlgskill` for notebooks and dialogs; prefer `exhash.skill` for text editing when it's available) and the short description won't always make the distinction clear.\n\nPyskill results are designed so the right call answers the question directly. Post-processing a result with generic Python (a `split`/`join`/slice/comprehension over its output) is a workaround smell: it usually means the call was wrong or a parameter was missed. Check `doc()` for the parameter or sibling function that answers directly; if it genuinely doesn't exist, propose extending the module rather than bridging with ad hoc code.\n\nThe same smell applies on the way in: wrapping an argument in `str()`, `expanduser()`, pre-escaping, or path-joining that the call already handles means either the docments weren't read or the tooling or its docs need fixing. Check the parameter before dressing the argument, and tell the user when the most ergonomic argument handling doesn't exist or isn't documented: improving tooling is always first priority.\n\nResults are built to be read as their bare reprs: end the cell with the bare expression rather than `print(...)`, which flattens a tuned display to plain `str()`. If a bare result ever reads worse than a printed or reformatted version, the repr is deficient: fix it or tell the user, never quietly work around it.\n\nSummarize what a pyskill's docs or results say rather than dumping the full output verbatim, unless the user actually needs to see all of it.\n\n`doc()` works on *all* python modules, not only pyskills.\n\n`xdir(sym, q=None)` complements `doc()`: it lists an object's public names, filtered by an optional case-insensitive regex. Use it when a module, class, or dynamic API surface is too big to `doc()` whole, e.g. `xdir(page.emulation, 'viewport')` on a fastcdp CDP domain, then `doc()` the match before calling it.\n\n`info_md(obj, source=False)` (from `ipykernel_helper`, preloaded by clikernel startup where installed) is the third way to read an object: IPython's `?` -- or `??` with `source=True` -- rendered as markdown. Reach for it when you want an object's real signature, docstring, and source together, rather than `inspect.getsource`/`inspect.signature` or bare `?`/`??`.\n\n`from pyskills import createskill; doc(createskill)` for how to build and register your own pyskill modules.\n\"\"\"\n\n## types:\n- class SkillTestClass(str): ... # Some class.\n\n## functions:\n- async def async_skill_test_func(x: int = 0) -> str: ... # A test function\u2026\n- def skill_test_func(x: int = 0) -> str: ... # A test function\u2026\n\n## elided: 1 submodules. `doc('pyskills.skill', all=True)` lists them.\n\n# module fastcore.editskill:\n\n\"\"\"Text, file, cell, and notebook editing from `fastcore.tools` and `fastcore.nbio`, plus the conventions the whole fastai editing toolkit follows. Read this before working with the editing tools in any package that shares them.\n\n`from fastcore.editskill import *` loads the fastcore editing layer: the text primitives and file tools of `fastcore.tools`, and the notebook I/O and cell editors of `fastcore.nbio`. Sibling packages extend the same toolkit, and where they are installed prefer them as noted: `exhash` for hash-verified editing, `rgapi` for search, `aidialog`/`dialoghelper` for dialogs.\n\n## Places\n\nEach editing function works on one place: text (a str in memory), a file (a path on disk), a cell (one cell's source in an .ipynb file, addressed by `path, cell_id`), or a notebook (the parsed .ipynb; `Notebook` and `NbCell` are its held-object forms). These are the representation layer: an .ipynb is a file of cells, whatever produced it. The dialog layer above adds the msg and dlg places: a Solveit dialog is an .ipynb whose cells are messages (notes, runnable code, prompt/reply pairs), and `aidialog.dlgskill` and `dialoghelper` provide the message tools, following the conventions here with their own nouns. The word picks the layer: cell tools answer representation questions (\"why does Jupyter reject this file?\"), msg tools answer content questions (\"what does this message say?\").\n\nThe section layer sits above both. `open_doc` (in exhash) parses a file (`fname=`, or a `Path` as `src`), URL (an `https?://` str), or text (any other str) into a `Section` tree, with sections taken from Markdown headings, tree-sitter definitions in code, or md-heading cells in a notebook. A section points at a span of lines in a file, or a run of cells in a notebook. To change what a section contains, edit those lines or cells with the file and cell tools.\n\n## Naming\n\nTwo name shapes cover the toolkit, and the pivot is the verb's direct object:\n\n- An operation on a whole place takes the place as its noun: verb_place. `view_file`, `create_file`, `read_nb`, `write_nb`, `view_cell`, `validate_nb`; in the dialog layer `view_msg` and `view_dlg`. Coined verbs follow the same shape: `lnhashview_cell` is \"lnhashview this cell\". When the verb's object is instead the unit inside the place, and that unit names its place uniquely, no prefix is needed - the unit noun is the place signal: `find_msgs`, `add_msg`, `del_msgs` (msgs live only in dialogs), `find_cells`, `summary_nb`'s rows (cells live only in notebooks).\n- An operation within a place already owns its noun (`insert_line`, `del_lines`, `replace_lines`, `str_replace`), so the place prefixes as a namespace and the op name survives intact: place_op, as in `file_del_lines`, `cell_del_lines`, `msg_del_lines`. The bare op names are the text-level primitives, and every place version keeps the identical signature after its address arguments, so each family is learned once and recognized everywhere.\n\nThe exceptions are deliberate and closed. `str_replace` keeps the name and argument order established by Anthropic's text editor tool. Instrument-named ops put the instrument first and elide their unit: `ast_replace` (the AST pattern is how the edit finds its target) and `exhash` (hash-verified line addresses travel inside its commands), place-prefixed like any other line-level op: `file_ast_replace`, `msg_ast_replace`, `file_exhash`, `cell_exhash`. Converters are named x2y (`nb2dict`, `cell2xml`; in aidialog, `dlg` on exactly one side of every converter), and on a held object the converter is a `to_y` method (`nb.to_dict()`). Plural marks arity: `view_cell` takes one cell, `lnhashview_cells` several, `del_msgs` many.\n\n## Parameters\n\nOne vocabulary, identical wherever it appears:\n\n- The place's address comes first (`text`; `path`; `path, cell_id`; a message `id`), the new text next, and ambient context last as keyword-only (message tools name their dialog that way).\n- `start_line`/`end_line`: 1-based, inclusive, `None` for first/last, negative counting from the end. `del_lines` accepts no defaults: state the range.\n- `re_filter`/`invert_filter`: restrict an edit to lines matching (or not matching) a regex, like ex's `g//` and `g!//`; combines with the range.\n- Searches read patterns as regex by default; editors read them as literal text until `use_regex=True`. Searching is read-only, so its default favors power; editing favors caution.\n- `nums` and `lnhashs` on any view: line numbers, or `lineno|hash|` addresses. `maxlen` caps characters per summary line; `trunc_out`/`trunc_in` truncate outputs and sources in dialog views.\n- Search tools share one filter vocabulary: `pattern` first, `root='.'`, and the same include/exclude/ext/hidden/ignore block across `fd`, `ls`, `rg`, and `nbrg`. Variants differ by defaults, not API: `ls` is `fd` with listing defaults. Boolean filters narrow as `only_*` and widen as `include_*`.\n- `context=` counts whatever units the place has: lines (or blocks in summary mode) for files, cells for notebooks, messages for dialogs. Dialog search defaults to context 1 because the neighbouring note usually explains the match: the why lives next to the what.\n- Every editor returns a diff (\"none: No changes.\" when nothing changed). The diff is the verification: read it instead of re-viewing the target.\n\n## Functions and methods\n\nEvery operation has two shapes with one contract each. The function is a transaction: it addresses a file by path, applies the edit, writes, and returns a diff. The method is a session: it changes the object in hand, and nothing reaches disk until an explicit save. The correspondence is mechanical - the method is the function minus its address arguments, keeping its name except that a place word which became `self` drops: `cell_str_replace(path, cell_id, ...)` is `c.str_replace(...)` on a held `NbCell`, `find_cells(path, pat)` is `nb.find_cells(pat)` on a held `Notebook`. Function-side reads return plain snapshot rows carrying addresses, source, and meta; session-side reads return the objects themselves. Use one shape at a time per file: save before switching to functions, reopen after.\n\n## Addresses\n\nEdits say where with line numbers, lnhash addresses, or section tokens. Take addresses from the read you were already doing instead of with a second look: views accept `nums=True` or `lnhashs=True`, and searches return addresses directly (`rg(lnhashs=True)`). Prefer lnhash addresses whenever `exhash` is installed: they are verified against current content at edit time, so a stale address fails loudly instead of editing nearby text, which is exactly why taking addresses early works. Plain line numbers are unverified and shift as edits apply, so re-view after each edit and apply multi-edits bottom-to-top. `exhash.skill` owns the address format and the verified editor. Section tokens make a long document navigable and editable a piece at a time. An outline row shows one, as in `1.6.|12|a3f2|,45|b1c3|`: the section number, then the lnhash addresses of its first and last lines. `d.at(token)` returns that section with its hash checked, `d.view(tok1, tok2)` prints the rendered text of each, and the same boundary pair drops straight into a range edit: `file_exhash(path, ('12|a3f2|,45|b1c3|', 'c', new_text))`. Read `exhash.skill` before working through a large Markdown, code, or notebook file, or before reading documentation from a URL: `open_doc` also searches within a tree and follows links, which is how the llms.txt docs workflow runs.\n\nWhen you don't yet know where to edit, locate with a summary first: `rgapi`'s `rg(summary=True)` and `nbrg`, aidialog's `summary_dlg`, and exhash's `open_doc` outlines each show one row per natural unit (block, cell, message, section), carrying the unit's address. For prose, config, and other paragraph-shaped text, blocks are usually the right unit for reading too: one summary row shows the whole matched paragraph with its boundary addresses, where a line-mode hit shows a fragment that then needs a context view. Summaries locate, views read, addresses edit, diffs confirm.\n\n## What's where\n\n- `fastcore.tools`: text primitives, file tools, and `line_hash`/`lnhash`/`lnhash_at` for creating addresses without exhash installed.\n- `fastcore.nbio`: notebook read/write/validate/repair, cell construction, cell editors, and the `Notebook`/`NbCell` session objects with their snapshot queries (`find_cells`, `summary_nb`).\n- `exhash.skill`: hash-verified editing for files and cells, plus `open_doc` section outlines for Markdown, code, and notebooks; prefer it for edits where installed.\n- `rgapi.skill`: `rg`/`fd`/`ls`/`nbrg` search with lnhash output, and `rgstr` to search text already in hand.\n- `remold`: structural search and rewrite for Python source (declarative ast-grep rules, LibCST matcher transforms, symbol queries); the engine behind `ast_replace`.\n- `aidialog.dlgskill`, `dialoghelper`: the dialog layer, including its own theory of dialogs and projections.\n\nDocs: https://fastcore.fast.ai/tools.html.md and https://fastcore.fast.ai/nbio.html.md\n\"\"\"\n\n## types:\n- class Notebook: ... # Read, query, and edit Jupyter notebooks\n- class NbCell(AttrDict,adict,dict)\n\n## functions:\n- def insert_line(text: str, insert_line: int, new_str: str): ... # Insert new_str at specified line number\u2026\n- def str_replace(text: str, old_str: str, new_str: str, **replace_params): ... # Replace occurrence(s) of old_str with new_str\u2026\n- def strs_replace(text: str, old_strs: list[str], new_strs: list[str], **replace_params): ... # Replace multiple strings simultaneously\u2026\n- def replace_lines(text: str, start_line: int = None, end_line: int = None, new_content: str = ''): ... # Replace line range with new content; the defaults replace the entire contents\u2026\n- def del_lines(text: str, start_line: int, end_line: int, re_filter: str = None, invert_filter: bool = False): ... # Delete line range; deletion is destructive, so both line numbers must be given explicitly (`1, -1` for all lines)\u2026\n- def ast_replace(text: str, repls: list): ... # Apply ast-grep structural pattern replacements to python source\u2026\n- def file_insert_line(path: str, insert_line: int, new_str: str): ... # Insert new_str at specified line number\u2026\n- def file_str_replace(path: str, old_str: str, new_str: str, **replace_params): ... # Replace occurrence(s) of old_str with new_str\u2026\n- def file_strs_replace(path: str, old_strs: list[str], new_strs: list[str], **replace_params): ... # Replace multiple strings simultaneously\u2026\n- def file_replace_lines(path: str, start_line: int = None, end_line: int = None, new_content: str = ''): ... # Replace line range with new content; the defaults replace the entire contents\u2026\n- def file_del_lines(path: str, start_line: int, end_line: int, re_filter: str = None, invert_filter: bool = False): ... # Delete line range; deletion is destructive, so both line numbers must be given explicitly (`1, -1` for all lines)\u2026\n- def file_ast_replace(path: str, repls: list): ... # Apply ast-grep structural pattern replacements to python source\u2026\n- def view_file(path: str, start_line: int = 1, end_line: int = None, nums: bool = True, lnhashs: bool = False): ... # Read file contents, optionally limited to 1-based line range\u2026\n- def create_file(path: str, contents: str, overwrite: bool = False): ... # Create a new file with contents. Error if file exists, unless `overwrite`.\u2026\n- def line_hash(line: str) -> str: ... # 4-char hex hash of `line`\u2026\n- def lnhash(lineno: int, line: str) -> str: ... # `lineno|hash|` exhash address for `line` at `lineno`\u2026\n- def lnhash_at(s: str | list | tuple, line: int) -> str: ... # `lineno|hash|` exhash address of line `line` of `s`\u2026\n- def read_nb(path): ... # Return notebook at `path` (expands `~`) [1bdc]\n- def write_nb(nb, path): ... # Write `nb` to `path` (expands `~`) [a307]\n- def new_nb(cells=None, meta=None, nbformat=4, nbformat_minor=5): ... # Returns an empty new notebook [dee4]\n- def mk_cell(text, cell_type='code', **kwargs): ... # Create an `NbCell` containing `text`\u2026\n- def validate_nb(nb): ... # Raise `ValueError` for structural problems in notebook `nb`; returns it unchanged if fine [141c]\n- def validate_cell(cell, idx=None): ... # Raise `ValueError` for structural problems in notebook cell dict `cell`; returns it unchanged if fine [0ba9]\n- def repair_nb(nb): ... # Fix deterministic structural problems in `nb`, returning a list of repairs made [137e]\n- def repair_cell(cell, idx=None): ... # Fix deterministic structural problems in `cell`, returning a list of repairs made [8864]\n- def view_cell(path: str, cell_id: str, start_line: int = 1, end_line: int = None, nums: bool = True, lnhashs: bool = False, incl_out: bool = False, trunc_out: bool = True): ... # View a cell's source, optionally limited to 1-based line range\u2026\n- def cell_insert_line(path: str, cell_id: str, insert_line: int, new_str: str): ... # Insert new_str at specified line number\u2026\n- def cell_str_replace(path: str, cell_id: str, old_str: str, new_str: str, **replace_params): ... # Replace occurrence(s) of old_str with new_str\u2026\n- def cell_strs_replace(path: str, cell_id: str, old_strs: list[str], new_strs: list[str], **replace_params): ... # Replace multiple strings simultaneously\u2026\n- def cell_replace_lines(path: str, cell_id: str, start_line: int = None, end_line: int = None, new_content: str = ''): ... # Replace line range with new content; the defaults replace the entire contents\u2026\n- def cell_del_lines(path: str, cell_id: str, start_line: int, end_line: int, re_filter: str = None, invert_filter: bool = False): ... # Delete line range; deletion is destructive, so both line numbers must be given explicitly (`1, -1` for all lines)\u2026\n- def cell_ast_replace(path: str, cell_id: str, repls: list): ... # Apply ast-grep structural pattern replacements to python source\u2026\n- def find_cells(path, pat: str = '', cell_type: str = None, ids='', context: int = None): ... # Snapshot `FoundCells` for matching cells in the notebook at `path`\u2026\n- def summary_nb(path, maxlen: int = 180): ... # One snapshot line per cell of the notebook at `path`\u2026\n\n## shared params:\n- **replace_params (from `str_replace`):\n start_line: int = None, # Optional 1-based start line to limit search\n end_line: int = None, # Optional 1-based end line to limit search\n n_matches: int = None, # Max replacements (None=all)\n re_filter: str = None, # If provided, only process lines matching this regex (like g// in ex)\n invert_filter: bool = False, # Invert the filter (like g!// in ex)\n use_regex: bool = False, # Treat old_str as a regex, and new_str as an `re.sub` template?"}]} {"type": "custom_tool_call", "name": "exec", "input": "const r=await tools.mcp__clikernel__execute({\"code\":\"doc(dsk, exh, rgsk)\"}); for (const c of (r.content||[])) c.type===\"text\"?text(c.text):c.type===\"image\"?image(c):null;\n", "call_id": "toolu_e09602479d275e16978b5e6caf9bc790", "id": "ctc_a4c87fdc7cc55177bd5900151d830fe7", "status": "completed"} @@ -7,55 +7,57 @@ {"type": "custom_tool_call", "name": "exec", "input": "const r=await tools.mcp__clikernel__execute({\"code\":\"doc(acp)\"}); for (const c of (r.content||[])) c.type===\"text\"?text(c.text):c.type===\"image\"?image(c):null;\n", "call_id": "call_fd9e6170bda950c8b8be9d905c6944fb", "id": "ctc_8fdd19e66e1d56d3a9dda2f1977959d6", "status": "completed"} {"type": "custom_tool_call_output", "call_id": "call_fd9e6170bda950c8b8be9d905c6944fb", "output": [{"type": "input_text", "text": "# module aai_coding.coding_patterns:\n\n\"\"\"Jeremy's coding style and conventions: read before writing, reviewing, or assessing any code.\n\n# Coding Patterns\n\nBased on the fastai style guide and Jeremy's 3 decades of coding experience, and used across all of his projects, not only fastai ones.\n\nUse the fastcore/fasthtml ecosystem (fastcore, fasthtml, fastlite, ...) when picking libraries. These are Jeremy's own, editable-installed as siblings, and preferred over heavier third-party alternatives.\n\n## Improving Tooling Pays Off Exponentially\n\nMaking our tools marvellous matters more than the task in hand. A finished task helps once. A better tool helps every later task, every later session, and the whole team. Ergonomics count as much as capability. Most code you have read was written by people who put up with tool friction rather than fix it, so your default is the workaround. Here the tools are ours and one edit away. When something grates, fix it or raise it. Never quietly work around it.\n\n## Every Construct Must Earn Its Place\n\nReaders assume everything present is necessary. When they see `str(x)` on something that's already a str, they stop and wonder what subtle thing it's guarding against. When the answer is \"nothing\", they paid for a mystery with no payoff. The same goes for defensive copies (`list(x)` that's never mutated), \"just in case\" try/excepts, redundant type coercions, and unused parameters. Before adding any construct, know why it's needed. If you can't say, leave it out. The cost doubles in nbdev projects, where tests are documentation.\n\nKnowing why a construct is needed does not mean waiting for someone to ask for it. An open source user who finds a gap concludes the library cannot do it and moves on. Judge a library feature by whether the design calls for it and users would expect it.\n\nThe same applies to prose in code: almost never add comments (only when the code is truly unclear), and don't add type hints, docstrings, or boilerplate that pull no weight. Prefer concise, readable code over verbose \"enterprise\" style.\nOnly write a code comment to state a constraint the code itself can't show, never to say where it came from, what the next line does, or why your change is correct. That's you talking to the reviewer, not the next reader, and it's noise the moment the PR merges.\n\n## Plain Commands: Flags, Redirects, and Pipes Are for Exceptions\n\nThe same principle applies to running commands. The bare invocation is the contract: `pytest tests -q`, `cargo fmt`, `maturin develop`. Project config exists precisely so the plain command does the right thing, and then every invocation is short, identical, and instantly readable. A flag, redirect, or pipe is a statement (\"this one call has a requirement the defaults don't cover\"), and used that way it carries information: the reader stops, asks why, and there's an answer. Sprinkled defensively on every call, decorations destroy that signal: every command looks exceptional so none is, and the one deliberate flag is indistinguishable from habit. Transcripts compound the damage, since each decorated call teaches later calls to decorate too.\n\n- Wanting the same flag on every run means it's a missing config line: promote it and go back to the bare command (`pytest --timeout 300` on every run becomes `timeout = 60` under `[tool.pytest.ini_options]`).\n- Don't check-then-apply when applying is the goal: `cargo fmt`, not `cargo fmt --check` followed by `cargo fmt`. `--check` is for a final no-mutation verification, e.g. CI.\n- Run commands bare and read their output. Never pipe through truncating filters (`| tail -20`, `| grep PASS`): truncation is decided before the output exists, and it hides exactly the surprises worth seeing. Never merge stderr into stdout with `2>&1`: separated, a crash is unmissable.\n- On the rare occasion output genuinely can't come back inline (far too large), redirect the streams separately (`>meta/stdout.txt 2>meta/stderr.txt`) and read the files from the kernel.\n- A real one-off requirement gets its flag once, with the reason stated alongside, and disappears again on the next call.\n\n## Docments\n\nDocments are trailing comments on function parameters that fastcore uses for documentation. A signature with docments (or any long signature) uses this layout: `(` stays on the def line, each parameter on its own line indented 4, and `):` alone on its own line at def indent:\n\n```python\n@delegates(start_kernel)\nasync def run_kernel(\n kernel_name='python3', # Kernelspec name to launch\n manager_cls=KernelManager, # Manager class, e.g. a subclass customizing launch\n **kwargs\n):\n```\n\nNEVER remove docments when refactoring. They're essential documentation.\n\nWhen `**kwargs` passes through to a known callee, decorate with `@delegates(callee)` so the signature shows the real options. delegates REQUIRES the collector to be named `kwargs`, not `kw` etc. Skip the decorator when the callee's own signature is just `**kwargs` (nothing to delegate).\n\n## Raw Strings\n\nWrite any non-trivial string literal as a raw string (`r\"...\"` / `r\"\"\"...\"\"\"`): regexes, text you pass to tools, code or markup inside strings, anything multi-line or containing backslashes. In plain strings a stray `\\n` or `\\d` either errors or silently corrupts, and each miss costs a round trip to diagnose plus another to fix. Raw strings are WYSIWYG, so the first attempt matches what you meant. The `r` costs nothing when no escapes are present, so make it the default, not the exception.\n\n## Style Checker (chkstyle)\n\nRun `chkstyle {path}` to check fastai style (only include path if needed). But use judgment. chkstyle is a hint, not gospel.\nIn nbdev projects, point it at the notebooks (`chkstyle nbs/00_core.ipynb`), not the exported `.py`. The notebook run also checks example and test cells, which never reach the module.\n\n## Config Patterns\n\nRead from standard locations rather than duplicating config:\n\n- GitHub release notes config: `.github/release.yml`\n- Project config: `pyproject.toml` under `[tool.yourpkg]`\n- Infer values when possible (e.g., package name from `[project].name`)\n- Bundle data files inside the package and read them with `importlib.resources`\n\n## Versioning: Bump After Release\n\nJeremy bumps the version immediately after each release, as part of releasing and never as part of a change. The tree therefore always carries the next release's version. A sibling dep pin can name a version before it ships (`foo>=`).\n\nA downstream pin is part of the change that creates the dependency. When a change makes one package consume another's new behavior or API, stamp the consumers' pins in the same session. A pin deferred to release time is a forgotten pin.\n\nThis convention is for Python projects. Other artifact types version at change time instead: for example, the Claude Code plugins in skill-plugins bump automatically when `./build.py` regenerates a changed output.\n\n## Project Layout\n\nPrefer flat layout over src/:\n\n```\nmyproject/\n\u251c\u2500\u2500 mypkg/\n\u2502 \u2514\u2500\u2500 __init__.py\n\u251c\u2500\u2500 tests/\n\u251c\u2500\u2500 pyproject.toml\n\u2514\u2500\u2500 README.md\n```\n\n## Testing\n\nAll code has writing, maintenance, and readability costs, and tests most of all: every test must be kept passing forever, gets read by every future contributor, and must be revised whenever the behavior it pins changes. So never write a test as a reflex, and don't aim for anywhere near 100% coverage. A test earns its place only when:\n\n- it documents an idea: in nbdev notebooks, tests ARE the documentation (see `doc(nbdev.skill)`), or\n- the logic is intricate enough that you had to think carefully to get it right (edge cases, parsing, arithmetic, tricky conditionals: the places a future change could silently break it), or\n- the code assumes something about an external system (a file format, an API's response shape, another tool's behavior) that is somewhat likely to change one day, and we want to hear about it when the assumption is violated. These must exercise the real thing (a mock merely re-states our assumption), so they're usually the slow-marked tests\n\nWiring and orchestration get zero tests: re-exports, delegations, one-line glue, functions that only sequence calls to other tools. A test there only asserts that Python works, and pins down internals we may want to change. Strong tell: if a test needs recording fakes or mock collaborators to reach the code, it's testing a transcript of the implementation, not logic. Extract the logic into a small pure function and test that, or don't test at all.\n\nIF you add a pytest test, ALWAYS work red-green: write it FIRST, run it to see it fail, THEN make the change, then run it again to see it pass. In nbdev projects there are no test cells - see `doc(nbdev.skill)`: changes revise lesson cells, and the red-green check applies only to an assertion you actually revised or added.\n\n- Prefer as few tests as possible: a single test that walks through many checks is more readable and faster than many small ones\n- A check worth keeping goes in a real test file or notebook cell, never left as an ad-hoc command. In a notebook, the checks made while exploring often ARE the narrative (each one both documents what we needed to know and keeps guarding it), so they stay as example cells. In a pytest file, an exploratory check survives only if it meets one of the criteria above\n- Assert the logic, not incidentals: check what the behavior guarantees, never byte-exact renderings, exact reprs, or field order. A test that compares a whole output string locks in formatting decisions that were never the point (e.g. assert the content appears in a markdown display block, not the display's exact text). NEVER use tests to \"lock in\" behavior, unless that exact behavior really is a key part of the logic or contract that must always be true forever\n- Use `pytest -q` (not `python -m pytest`, which prompts for permission). nbdev projects use `nbdev-test` on the changed notebook, but some notebooks are slow or hit live services, so check with the user before running one you don't know is safe (known safe: all of pyskills)\n- Don't run slow-marked tests until finishing a session, or after a change likely to directly impact them\n\n## One-liner Patterns\n\n```python\n# Conditionals\nif not x: return default\nif x and y: do_thing()\n\n# Try/except\ntry: return run(\"cmd\").strip() or default\nexcept Exception: return default\n\n# Loops\nfor i in range(n): result[i] = 0\nwhile len(items) < 3: items.append(0)\n```\n\n## Import Style\n\nCombine imports on single lines:\n\n```python\nimport os, re, sys, shutil\nfrom pathlib import Path\nfrom fastcore.utils import *\n```\n\n`from fastcore.utils import *` already provides `os`, `Path`, and much of the stdlib, so don't re-import those alongside it.\n\"\"\""}]} {"type": "custom_tool_call", "name": "exec", "input": "const r=await tools.mcp__clikernel__execute({\"code\":\"list_pyskills()\"}); for (const c of (r.content||[])) c.type===\"text\"?text(c.text):c.type===\"image\"?image(c):null;\n", "call_id": "toolu_672854b052f65fb1ae1858e536926796", "id": "ctc_e41a056fac365720894d4aaa88697598", "status": "completed"} -{"type": "custom_tool_call_output", "call_id": "toolu_672854b052f65fb1ae1858e536926796", "output": [{"type": "input_text", "text": "{'nbdev.skill': 'Author clear, executable nbdev notebooks where code, prose, examples, outputs, and tests form one coherent narrative.',\n 'dialoghelper.solveitskill': 'Read, search, edit, and manage Solveit dialogs using dialoghelper.core, including dialog/message addressing, line-numbered inspection, targeted message edits, add/update/delete/copy/paste workflows, and safe editing patterns.',\n 'tracefunc.skill': 'See what a Python function did, line by line: how many times each line ran and what the variables held. Use when working out why code takes a branch, loops, recurses, or computes a wrong value, without changing that code or stepping through a debugger.',\n 'rgapi.skill': 'Fast and flexible file finding and search for Python. Use this when code needs `fd`-style file finding or `rg`-style searching.',\n 'ghapi.skill': 'GitHub REST API access via `GhApi`, plus local git operations via `fastgit.Git`. Use this for day-to-day GitHub work: reading/creating issues and PRs, checking CI status, managing releases/branches/gists, and repo-local git operations -- all from Python, no shelling out to `gh`/`git` needed.',\n 'fastanki.skill': 'Anki flashcard tools for spaced repetition. Direct sqlite and AnkiWeb sync, with no Anki app needed.',\n 'remold': \"Structural search and rewrite for Python source: declarative ast-grep pattern rules, LibCST matcher transforms for everything patterns can't express, and tree-based symbol queries. Use this to edit code by structure (rename calls, move methods, rewrite APIs) where regexes break and `ast` loses comments.\",\n 'jph': \"Jeremy's tools, which for now is just his notes system. Use if Jeremy says 'create a note', or 'check my notes' or similar.\",\n 'cordslite.skill': 'Read-only access to Discord through cordslite. Covers connecting, opening a guild, finding your way around its channels, searching messages, reading threads, and fetching attachments.',\n 'fastmux.skill': 'Work with tmux sessions, windows, and panes from Python, plus named background sessions keyed by sid. Use this when code needs to read what is on a terminal, send input to an interactive program, build pane layouts, search text across terminals, or keep a terminal open that you and the user can both see.',\n 'exhash.skill': 'Universal hash-verified text editing for local files. Use this for one reliable way to read, preview, and change text files.',\n 'clikernel.skill': 'Use the `clikernel` MCP session as the default workspace for Python work: reading and changing files, notebook work, trying things out, checking how a library behaves, and reshaping data. One session stays open, so imports and variables carry between calls. Read this before writing, running, or debugging Python code in a session with `clikernel` connected.',\n 'fastcdp.skill': 'Work with Chrome through the DevTools Protocol: open pages, click and type, read a page as an accessibility tree, keep a log of console and network activity for debugging, and call any CDP command.',\n 'aai_coding.coding_patterns': \"Jeremy's coding style and conventions: read before writing, reviewing, or assessing any code.\",\n 'aai_coding.harness_docs': 'Official docs for the tool you are running in (Claude Code or codex): read before answering questions about its behaviour, config, or features.',\n 'aai_coding.looker': 'Answer questions about images and PDFs by asking a separate codex process to look at them, which keeps large files out of this session (macOS only). Use it to verify rendered output: PDFs, screenshots, captured windows.',\n 'aai_coding.theory': 'What \"Theory\" (capital T) means in this team\\'s vocabulary: Peter Naur\\'s programming-as-theory-building. Read when the user says the Theory of something, or asks to discuss, find, or capture a Theory.',\n 'aai_coding.write_docs': 'How to write reference prose: read before writing docstrings, READMEs, API docs, PR descriptions, commit messages, or messages to co-workers.',\n 'aai_coding.write_prose': \"How to write narrative prose that reads as though a person wrote it: read before writing blog posts, essays, announcements, or anything else with the author's voice. write-docs covers reference prose.\",\n 'aai_coding.write_summary': 'How to write summaries that stand alone: read before summarizing a conversation, meeting, PR, paper, or repo change for a human reader.',\n 'fastcore.editskill': 'Text, file, cell, and notebook editing from `fastcore.tools` and `fastcore.nbio`, plus the conventions the whole fastai editing toolkit follows. Read this before working with the editing tools in any package that shares them.',\n 'pyskills.skill': 'Pyskills are tool modules that Python packages register so you can find them without importing everything. `list_pyskills()` names the ones installed, with a one-line description each, and needs no imports. Load one with a normal import, then read its docs with `doc()`.',\n 'llmsurgery.skill': 'Read and work with Claude Code and Codex session transcripts.',\n 'aidialog.dlgskill': \"Read, search, and edit dialogs and notebooks through aidialog's `Dialog` and `Message` classes\",\n 'fastcflare.skill': 'Cloudflare API access via `CloudflareApi`: zones, DNS records, caching, and every other Cloudflare v4 feature from Python. Use this for day-to-day Cloudflare work instead of hand-built httpx calls against api.cloudflare.com.',\n 'test.skill': 'A test skill.'}"}]} -{"type": "custom_tool_call", "name": "exec", "input": "const r=await tools.mcp__clikernel__execute({\"code\":\"dojo_start()\"}); for (const c of (r.content||[])) c.type===\"text\"?text(c.text):c.type===\"image\"?image(c):null;\n", "call_id": "toolu_f70d761adac65c71a754002e74624e4b", "id": "ctc_37686131afbd505aa238d1bdb270a903", "status": "completed"} -{"type": "custom_tool_call_output", "call_id": "toolu_f70d761adac65c71a754002e74624e4b", "output": [{"type": "input_text", "text": "== llmdojo ==\nAssumed knowledge: the toolkit conventions taught by the startup doc() calls, doc(clik, pysk, edsk), doc(dsk, exh, rgsk), and doc(acp). Do not continue unless that output is visible in your context; if any is missing, read it now.\nWork only in: /tmp/dojo\n%cd there first: chdir is never penalized, because the relative paths it enables make every later cell shorter - path tokens you never repeat are tokens saved, in the dojo and in real work.\nScoring: kernel cell = 1 stroke; Bash tool call = 2; each print() call = +1. The tooling's reprs are designed to be optimally useful read bare, so end each cell with a bare expression and read what comes back. `doc()` returns a `PrettyString`: leave it as the final expression when you want to see it, or assign it when you do not want it rendered, for instance for very large docs you want to search through. Cells of only doc()/list_pyskills()/imports are free (bare calls, NOT wrapped in print()), as are comment-only narration cells and chdir cells (%cd / os.chdir).\nPenalties: +1 per skill module or workspace function used before doc()ing it.\nPar assumes the tooling's best route, shown with each kata at scoring: matching par means you found it.\nWhy the round matters: the rest of this session copies whatever this transcript shows, so one round of correct tooling use improves everything after it. Falling back to sed, cat, or Path.read_text() teaches the opposite, and we find those fallbacks go with weaker, less creative work across the whole task.\nPer-kata scoring: before starting a kata, run a cell containing ONLY a comment in this exact format: '# kata 3'. Cells after it count toward that kata until the next tag cell; tag cells are free. Anything fancier is narration, not a tag.\nPar for the round: 8. When done: dojo_score(bash_calls=)\nThe round is complete ONLY on a clean score: par or better, every kata ok, no penalties. Until then do no work outside the dojo; redo over-par katas with dojo_redo, in ascending order. Scoring pauses the ledger: dojo_redo (or dojo_resume() without a reset) restarts it.\nThis dojo is an early version: note anything about the scoring or process that seems possibly-imperfect, and include it in your report.\n\n1. (par 1) Why does this project use httpx? Answer in prose via dojo_score(orient=\"...\"), including the specific justification the notebook gives. Tip: find_msgs' context= defaults to 1 for a reason - the why usually lives next to the what.\n2. (par 2) In core.py: change the default units to 'metric', delete the FIXME comment line, and rename the cfg variable to config everywhere (load_cfg keeps its name; docstring unchanged).\n3. (par 2) In tmpl.py: replace the whole render() function with exactly this, verbatim:\n\n def render(name, temp):\n r\"\"\"Render a one-line summary; keep \\t, \\n and ''' literal in this docstring.\"\"\"\n return name + ':\\t' + str(temp) + ' degrees\\n'\n4. (par 2) In nbs/01_api.ipynb: the markdown under the Retries header is wrong; it should say the request is retried twice more, making \"3 attempts\" in all.\n5. (par 1) report.py defines daily_report (the kata dir is importable during the round). Produce the report for report.SAMPLE in the form needed for production, and pass its first line via dojo_score(report=\"...\"). Tip: doc() is free, and an overview line ending in \"...\" means the full docstring holds more."}]} -{"type": "custom_tool_call", "name": "exec", "input": "const r=await tools.mcp__clikernel__execute({\"code\":\"%cd /tmp/dojo\"}); for (const c of (r.content||[])) c.type===\"text\"?text(c.text):c.type===\"image\"?image(c):null;\n", "call_id": "toolu_0600686d320a50f2bdcfa18ebcb24aa8", "id": "ctc_020af65a0ef95a20811fc2e408cdea68", "status": "completed"} -{"type": "custom_tool_call_output", "call_id": "toolu_0600686d320a50f2bdcfa18ebcb24aa8", "output": [{"type": "input_text", "text": "/tmp/dojo"}]} -{"type": "custom_tool_call", "name": "exec", "input": "const r=await tools.mcp__clikernel__execute({\"code\":\"# kata 1\"}); for (const c of (r.content||[])) c.type===\"text\"?text(c.text):c.type===\"image\"?image(c):null;\n", "call_id": "toolu_4edf21b3da8c5e14836f4e0754f0cc3b", "id": "ctc_2f334fd24b4752abb2a22d45c051ff6b", "status": "completed"} -{"type": "custom_tool_call_output", "call_id": "toolu_4edf21b3da8c5e14836f4e0754f0cc3b", "output": [{"type": "input_text", "text": "(mcp__clikernel__execute completed with no output)"}]} -{"type": "custom_tool_call", "name": "exec", "input": "const r=await tools.mcp__clikernel__execute({\"code\":\"doc(find_msgs, view_dlg)\"}); for (const c of (r.content||[])) c.type===\"text\"?text(c.text):c.type===\"image\"?image(c):null;\n", "call_id": "toolu_3115bf5c68005ee2ad6f765324b85396", "id": "ctc_dc1f47f11f595a028c4539371f6c6af2", "status": "completed"} -{"type": "custom_tool_call_output", "call_id": "toolu_3115bf5c68005ee2ad6f765324b85396", "output": [{"type": "input_text", "text": "def find_msgs(\n re_pattern:str='', # Regex over content (a prompt's reply included), DOTALL+MULTILINE; an invalid regex matches literally\n dlg:NoneType=None, # An ipynb path (expands `~`); the current dialog file if None\n msg_type:str=None, # Optional limit by type ('code', 'note', 'prompt', or 'raw')\n only_err:bool=False, # Only code messages with error outputs?\n only_exp:bool=False, # Only exported messages (nbdev export directive in content or meta)?\n ids:str='', # Optionally filter by ids (comma-separated str, or list); results are always in dialog order, whatever order the ids are given\n before:int=0, # Also include n messages before each match\n after:int=0, # Also include n messages after each match\n context:int=None, # Messages of context around matches (default 1, or 0 when `headers_only`)\n limit:int=None, # Max matched messages\n use_case:bool=False, # Case-sensitive matching?\n use_regex:bool=True, # Regex matching (else plain substring)?\n headers_only:bool=False, # Only heading notes (an outline view)?\n header_section:str=None, # Return the section starting with this heading, plus its children\n pred:callable=None, # Extra match criterion, e.g. from `symdef_finder`/`symref_finder`/`ast_finder`, or host-specific flags\n)->MsgRows: # Snapshot rows (`id`, `msg_type`, `content`, `out`, `meta`), indexed by id and shown as previews with context rows marked\"\"\"Find messages in the dialog file matching all the given criteria; for live results, call `Dialog.find_msgs`\"\"\"\n# doced: find_msgs='d407'\n\ndef view_dlg(\n dlg:NoneType=None, # An ipynb path (expands `~`); the current dialog file if None\n incl_out:bool=False, # Include code outputs?\n only_errors:bool=False, # Show only code messages with error outputs (implies `incl_out`)?\n trunc_out:bool=True, # Truncate each output to ~512 chars?\n):\"\"\"The dialog file as concise XML; meta-exported messages carry a bare `export` attr\"\"\"\n# doced: view_dlg='4441'"}]} -{"type": "custom_tool_call", "name": "exec", "input": "const r=await tools.mcp__clikernel__execute({\"code\":\"view_dlg('nbs/01_api.ipynb')\"}); for (const c of (r.content||[])) c.type===\"text\"?text(c.text):c.type===\"image\"?image(c):null;\n", "call_id": "toolu_431dee4b3f055b81a0a8bc2f8eb1c98b", "id": "ctc_816c76056dca5edc8746b0f7b77cb21f", "status": "completed"} -{"type": "custom_tool_call_output", "call_id": "toolu_431dee4b3f055b81a0a8bc2f8eb1c98b", "output": [{"type": "input_text", "text": "# weather\n\n> Fetch and summarize daily weather data from the open-meteo API#| export\nimport httpxWe don't use `requests` here. It has no async support, and its API is synchronous throughout. It also opens a new connection per call unless you create and close a Session yourself, so there is no connection pooling by default. This newer client keeps the same API shape and fixes both.#| export\ndef fetch_daily(lat, lon):\n \"Fetch one day of weather for `lat`,`lon`\"\n r = httpx.get('https://api.open-meteo.com/v1/forecast', params=dict(latitude=lat, longitude=lon, daily='temperature_2m_max'))\n return r.json()['daily']`fetch_daily` returns the raw `daily` block, one entry per requested field.d = httpx.get('https://api.open-meteo.com/v1/forecast?latitude=52&longitude=0&daily=temperature_2m_max').json()\nlist(d)## RetriesOn a connection error, `fetch_daily` retries the request twice before giving up.fetch_daily(52, 0)['time']"}]} -{"type": "custom_tool_call", "name": "exec", "input": "const r=await tools.mcp__clikernel__execute({\"code\":\"# Kata 1 answered; on to the file edits.\"}); for (const c of (r.content||[])) c.type===\"text\"?text(c.text):c.type===\"image\"?image(c):null;\n", "call_id": "toolu_ff8091368d6e5b598f355a8412561d38", "id": "ctc_7dff04ac703a5b60a82aa6de6cdcf0cf", "status": "completed"} -{"type": "custom_tool_call_output", "call_id": "toolu_ff8091368d6e5b598f355a8412561d38", "output": [{"type": "input_text", "text": "(mcp__clikernel__execute completed with no output)"}]} -{"type": "custom_tool_call", "name": "exec", "input": "const r=await tools.mcp__clikernel__execute({\"code\":\"# kata 2\"}); for (const c of (r.content||[])) c.type===\"text\"?text(c.text):c.type===\"image\"?image(c):null;\n", "call_id": "toolu_3f566991c8365e8181b0fb2f9cc7224c", "id": "ctc_839cfb54a1825d47b590441e27f05766", "status": "completed"} +{"type": "custom_tool_call_output", "call_id": "toolu_672854b052f65fb1ae1858e536926796", "output": [{"type": "input_text", "text": "{'docxlite.skill': 'Read, render, search, and edit .docx files with tracked changes. This skill is for LLM-friendly Word document editing inside notebooks and solveit dialogs.\\nIt builds on `python-docx`, adding markdown previews, markdown-to-runs insertion,\\ndocument-order block iteration, raw-text search, tracked changes, and small editing\\nhelpers for paragraphs, cells, rows, and tables.',\n 'tracefunc.skill': \"Trace a Python function's execution at AST-line level: per-line hit counts and live variable values, via `sys.monitoring`. Use when debugging *why* code takes a branch, loops, recurses, or computes a wrong value, without editing the code under investigation or using an interactive debugger.\",\n 'fastfitz.skill': 'Read, search, and visually preview PDFs with fastfitz (an ergonomic layer over PyMuPDF/fitz). Use this when a task needs PDF contents: text extraction first, rendered previews when layout, tables, stamps, signatures, or scan quality matter.',\n 'fastpdfium.skill': 'Read, search, preview, and edit PDFs with fastpdfium (an ergonomic, Apache-licensed layer over pypdfium2/PDFium). Use this when a task needs PDF contents: text extraction first, rendered previews when layout, tables, stamps, signatures, or scan quality matter.',\n 'fastcdp.skill': 'Work with Chrome through the DevTools Protocol: open pages, click and type, read a page as an accessibility tree, keep a log of console and network activity for debugging, and call any CDP command.',\n 'remold': \"Structural search and rewrite for Python source: declarative ast-grep pattern rules, LibCST matcher transforms for everything patterns can't express, and tree-based symbol queries. Use this to edit code by structure (rename calls, move methods, rewrite APIs) where regexes break and `ast` loses comments.\",\n 'fastmux.skill': 'Work with tmux sessions, windows, and panes from Python, plus named background sessions keyed by sid. Use this when code needs to read what is on a terminal, send input to an interactive program, build pane layouts, search text across terminals, or keep a terminal open that you and the user can both see.',\n 'llmsurgery.skill': 'Read and work with Claude Code and Codex session transcripts.',\n 'solveit_client.skill': \"Drive SolveIt dialogs over HTTP from outside the instance: create dialogs, add and edit messages, and execute code and AI prompts. Use this to automate a SolveIt instance from a laptop, script, or agent session -- anywhere that isn't the dialog's own kernel (from inside a dialog, use dialoghelper instead).\",\n 'cordslite.skill': 'Read-only access to Discord through cordslite. Covers connecting, opening a guild, finding your way around its channels, searching messages, reading threads, and fetching attachments.',\n 'fastcore.editskill': 'Text, file, cell, and notebook editing from `fastcore.tools` and `fastcore.nbio`, plus the conventions the whole fastai editing toolkit follows. Read this before working with the editing tools in any package that shares them.',\n 'solvedrive.skill': \"Load this skill when an agent needs to search, download, and upload Google Drive files using solvedrive. It covers connecting to Drive, searching across My Drive and shared drives with Drive's query syntax, downloading file content and exporting Google Docs, and uploading, renaming, moving, and copying files and creating folders. Trash, deletion, and sharing are documented for reference but are not enabled by default.\",\n 'solvemail.skill': 'Load this skill when an agent needs to read, search, and organize Gmail using solvemail. It covers connecting to Gmail, searching messages and threads, reading bodies and attachments, and managing labels. Sending, replying, forwarding, and trashing are documented for reference but are not enabled by default.',\n 'fastgws.skill': \"Use fastgws to read and work with Google Workspace and Google APIs from Python. This skill exposes the base `GWSApi` client, OAuth credential loading, and generated Google API operations. Use it when the task needs access to Gmail, Calendar, Drive, Docs, Sheets, Places, or another Google API published through Google's discovery documents.\",\n 'aidialog.dlgskill': \"Read, search, and edit dialogs and notebooks through aidialog's `Dialog` and `Message` classes\",\n 'nbdev.skill': 'Author clear, executable nbdev notebooks where code, prose, examples, outputs, and tests form one coherent narrative.',\n 'pyskills.skill': 'Pyskills are tool modules that Python packages register so you can find them without importing everything. `list_pyskills()` names the ones installed, with a one-line description each, and needs no imports. Load one with a normal import, then read its docs with `doc()`.',\n 'ghapi.skill': 'GitHub REST API access via `GhApi`, plus local git operations via `fastgit.Git`. Use this for day-to-day GitHub work: reading/creating issues and PRs, checking CI status, managing releases/branches/gists, and repo-local git operations -- all from Python, no shelling out to `gh`/`git` needed.',\n 'rgapi.skill': 'Fast and flexible file finding and search for Python. Use this when code needs `fd`-style file finding or `rg`-style searching.',\n 'exhash.skill': 'Universal hash-verified text editing for local files. Use this for one reliable way to read, preview, and change text files.',\n 'claude2dlg.core': 'claude.ai web conversations as dialogs',\n 'fastcflare.skill': 'Cloudflare API access via `CloudflareApi`: zones, DNS records, caching, and every other Cloudflare v4 feature from Python. Use this for day-to-day Cloudflare work instead of hand-built httpx calls against api.cloudflare.com.',\n 'clikernel.skill': 'Use the `clikernel` MCP session as the default workspace for Python work: reading and changing files, notebook work, trying things out, checking how a library behaves, and reshaping data. One session stays open, so imports and variables carry between calls. Read this before writing, running, or debugging Python code in a session with `clikernel` connected.',\n 'dialoghelper.solveitskill': 'Read, search, edit, and manage Solveit dialogs using dialoghelper.core, including dialog/message addressing, line-numbered inspection, targeted message edits, add/update/delete/copy/paste workflows, and safe editing patterns.',\n 'aai_coding.coding_patterns': \"Jeremy's coding style and conventions: read before writing, reviewing, or assessing any code.\",\n 'aai_coding.harness_docs': 'Official docs for the tool you are running in (Claude Code or codex): read before answering questions about its behaviour, config, or features.',\n 'aai_coding.looker': 'Answer questions about images and PDFs by asking a separate codex process to look at them, which keeps large files out of this session (macOS only). Use it to verify rendered output: PDFs, screenshots, captured windows.',\n 'aai_coding.theory': 'What \"Theory\" (capital T) means in this team\\'s vocabulary: Peter Naur\\'s programming-as-theory-building. Read when the user says the Theory of something, or asks to discuss, find, or capture a Theory.',\n 'aai_coding.write_docs': 'How to write reference prose: read before writing docstrings, READMEs, API docs, PR descriptions, commit messages, or messages to co-workers.',\n 'aai_coding.write_prose': \"How to write narrative prose that reads as though a person wrote it: read before writing blog posts, essays, announcements, or anything else with the author's voice. write-docs covers reference prose.\",\n 'aai_coding.write_summary': 'How to write summaries that stand alone: read before summarizing a conversation, meeting, PR, paper, or repo change for a human reader.',\n 'fasttract.drafting': 'Draft and export legal contracts in a Solveit dialog',\n 'my_local.skill': 'A quick local pyskill.'}"}]} +{"type": "message", "role": "assistant", "content": [{"type": "output_text", "text": "OK I'm ready."}]} +{"type": "message", "role": "user", "content": [{"type": "input_text", "text": "Complete the dojo and tell me when you're ready."}]} +{"type": "custom_tool_call", "name": "exec", "input": "const r=await tools.mcp__clikernel__execute({\"code\":\"dojo_start()\"}); for (const c of (r.content||[])) c.type===\"text\"?text(c.text):c.type===\"image\"?image(c):null;\n", "call_id": "toolu_0600686d320a50f2bdcfa18ebcb24aa8", "id": "ctc_020af65a0ef95a20811fc2e408cdea68", "status": "completed"} +{"type": "custom_tool_call_output", "call_id": "toolu_0600686d320a50f2bdcfa18ebcb24aa8", "output": [{"type": "input_text", "text": "== llmdojo ==\nAssumed knowledge: the toolkit conventions taught by the docs your host's startup asked you to read: doc(pysk, edsk) (with the host's own skill module first, where it has one), doc(dsk, exh, rgsk), and doc(acp). Do not continue unless that output is visible in your context; if any is missing, read it now.\nWork only in: /tmp/dojo\n%cd there first: chdir is never penalized, because the relative paths it enables make every later cell shorter - path tokens you never repeat are tokens saved, in the dojo and in real work.\nScoring: kernel cell = 1 stroke; Bash tool call = 2; each print() call = +1. The tooling's reprs are designed to be optimally useful read bare, so end each cell with a bare expression and read what comes back. `doc()` returns a `PrettyString`: leave it as the final expression when you want to see it, or assign it when you do not want it rendered, for instance for very large docs you want to search through. Cells of only doc()/list_pyskills()/imports are free (bare calls, NOT wrapped in print()), as are comment-only narration cells and chdir cells (%cd / os.chdir).\nPenalties: +1 per skill module or workspace function used before doc()ing it.\nPar assumes the tooling's best route, shown with each kata at scoring: matching par means you found it.\nWhy the round matters: the rest of this session copies whatever this transcript shows, so one round of correct tooling use improves everything after it. Falling back to sed, cat, or Path.read_text() teaches the opposite, and we find those fallbacks go with weaker, less creative work across the whole task.\nPer-kata scoring: before starting a kata, run a cell containing ONLY a comment in this exact format: '# kata 3'. Cells after it count toward that kata until the next tag cell; tag cells are free. Anything fancier is narration, not a tag.\nPar for the round: 8. When done: dojo_score(bash_calls=)\nThe round is complete ONLY on a clean score: par or better, every kata ok, no penalties. Until then do no work outside the dojo; redo over-par katas with dojo_redo, in ascending order. Scoring pauses the ledger: dojo_redo (or dojo_resume() without a reset) restarts it.\nThis dojo is an early version: note anything about the scoring or process that seems possibly-imperfect, and include it in your report.\n\n1. (par 1) Why does this project use httpx? Answer in prose via dojo_score(orient=\"...\"), including the specific justification the notebook gives. Tip: find_msgs' context= defaults to 1 for a reason - the why usually lives next to the what.\n2. (par 2) In core.py: change the default units to 'metric', delete the FIXME comment line, and rename the cfg variable to config everywhere (load_cfg keeps its name; docstring unchanged).\n3. (par 2) In tmpl.py: replace the whole render() function with exactly this, verbatim:\n\n def render(name, temp):\n r\"\"\"Render a one-line summary; keep \\t, \\n and ''' literal in this docstring.\"\"\"\n return name + ':\\t' + str(temp) + ' degrees\\n'\n4. (par 2) In nbs/01_api.ipynb: the markdown under the Retries header is wrong; it should say the request is retried twice more, making \"3 attempts\" in all.\n5. (par 1) report.py defines daily_report (the kata dir is importable during the round). Produce the report for report.SAMPLE in the form needed for production, and pass its first line via dojo_score(report=\"...\"). Tip: doc() is free, and an overview line ending in \"...\" means the full docstring holds more."}]} +{"type": "custom_tool_call", "name": "exec", "input": "const r=await tools.mcp__clikernel__execute({\"code\":\"%cd /tmp/dojo\"}); for (const c of (r.content||[])) c.type===\"text\"?text(c.text):c.type===\"image\"?image(c):null;\n", "call_id": "toolu_4edf21b3da8c5e14836f4e0754f0cc3b", "id": "ctc_2f334fd24b4752abb2a22d45c051ff6b", "status": "completed"} +{"type": "custom_tool_call_output", "call_id": "toolu_4edf21b3da8c5e14836f4e0754f0cc3b", "output": [{"type": "input_text", "text": "/tmp/dojo"}]} +{"type": "custom_tool_call", "name": "exec", "input": "const r=await tools.mcp__clikernel__execute({\"code\":\"# kata 1\"}); for (const c of (r.content||[])) c.type===\"text\"?text(c.text):c.type===\"image\"?image(c):null;\n", "call_id": "toolu_3115bf5c68005ee2ad6f765324b85396", "id": "ctc_dc1f47f11f595a028c4539371f6c6af2", "status": "completed"} +{"type": "custom_tool_call_output", "call_id": "toolu_3115bf5c68005ee2ad6f765324b85396", "output": [{"type": "input_text", "text": "(mcp__clikernel__execute completed with no output)"}]} +{"type": "custom_tool_call", "name": "exec", "input": "const r=await tools.mcp__clikernel__execute({\"code\":\"doc(find_msgs, view_dlg)\"}); for (const c of (r.content||[])) c.type===\"text\"?text(c.text):c.type===\"image\"?image(c):null;\n", "call_id": "toolu_431dee4b3f055b81a0a8bc2f8eb1c98b", "id": "ctc_816c76056dca5edc8746b0f7b77cb21f", "status": "completed"} +{"type": "custom_tool_call_output", "call_id": "toolu_431dee4b3f055b81a0a8bc2f8eb1c98b", "output": [{"type": "input_text", "text": "def find_msgs(\n re_pattern:str='', # Regex over content (a prompt's reply included), DOTALL+MULTILINE; an invalid regex matches literally\n dlg:NoneType=None, # An ipynb path (expands `~`); the current dialog file if None\n msg_type:str=None, # Optional limit by type ('code', 'note', 'prompt', or 'raw')\n only_err:bool=False, # Only code messages with error outputs?\n only_exp:bool=False, # Only exported messages (nbdev export directive in content or meta)?\n ids:str='', # Optionally filter by ids (comma-separated str, or list); results are always in dialog order, whatever order the ids are given\n before:int=0, # Also include n messages before each match\n after:int=0, # Also include n messages after each match\n context:int=None, # Messages of context around matches (default 1, or 0 when `headers_only`)\n limit:int=None, # Max matched messages\n use_case:bool=False, # Case-sensitive matching?\n use_regex:bool=True, # Regex matching (else plain substring)?\n headers_only:bool=False, # Only heading notes (an outline view)?\n header_section:str=None, # Return the section starting with this heading, plus its children\n pred:callable=None, # Extra match criterion, e.g. from `symdef_finder`/`symref_finder`/`ast_finder`, or host-specific flags\n)->MsgRows: # Snapshot rows (`id`, `msg_type`, `content`, `out`, `meta`), indexed by id and shown as previews with context rows marked\"\"\"Find messages in the dialog file matching all the given criteria; for live results, call `Dialog.find_msgs`\"\"\"\n# doced: find_msgs='d407'\n\ndef view_dlg(\n dlg:NoneType=None, # An ipynb path (expands `~`); the current dialog file if None\n incl_out:bool=False, # Include code outputs?\n only_errors:bool=False, # Show only code messages with error outputs (implies `incl_out`)?\n trunc_out:bool=True, # Truncate each output to ~512 chars?\n):\"\"\"The dialog file as concise XML; meta-exported messages carry a bare `export` attr\"\"\"\n# doced: view_dlg='4441'"}]} +{"type": "custom_tool_call", "name": "exec", "input": "const r=await tools.mcp__clikernel__execute({\"code\":\"view_dlg('nbs/01_api.ipynb')\"}); for (const c of (r.content||[])) c.type===\"text\"?text(c.text):c.type===\"image\"?image(c):null;\n", "call_id": "toolu_ff8091368d6e5b598f355a8412561d38", "id": "ctc_7dff04ac703a5b60a82aa6de6cdcf0cf", "status": "completed"} +{"type": "custom_tool_call_output", "call_id": "toolu_ff8091368d6e5b598f355a8412561d38", "output": [{"type": "input_text", "text": "# weather\n\n> Fetch and summarize daily weather data from the open-meteo API#| export\nimport httpxWe don't use `requests` here. It has no async support, and its API is synchronous throughout. It also opens a new connection per call unless you create and close a Session yourself, so there is no connection pooling by default. This newer client keeps the same API shape and fixes both.#| export\ndef fetch_daily(lat, lon):\n \"Fetch one day of weather for `lat`,`lon`\"\n r = httpx.get('https://api.open-meteo.com/v1/forecast', params=dict(latitude=lat, longitude=lon, daily='temperature_2m_max'))\n return r.json()['daily']`fetch_daily` returns the raw `daily` block, one entry per requested field.d = httpx.get('https://api.open-meteo.com/v1/forecast?latitude=52&longitude=0&daily=temperature_2m_max').json()\nlist(d)## RetriesOn a connection error, `fetch_daily` retries the request twice before giving up.fetch_daily(52, 0)['time']"}]} +{"type": "custom_tool_call", "name": "exec", "input": "const r=await tools.mcp__clikernel__execute({\"code\":\"# Kata 1 answered; on to the file edits.\"}); for (const c of (r.content||[])) c.type===\"text\"?text(c.text):c.type===\"image\"?image(c):null;\n", "call_id": "toolu_3f566991c8365e8181b0fb2f9cc7224c", "id": "ctc_839cfb54a1825d47b590441e27f05766", "status": "completed"} {"type": "custom_tool_call_output", "call_id": "toolu_3f566991c8365e8181b0fb2f9cc7224c", "output": [{"type": "input_text", "text": "(mcp__clikernel__execute completed with no output)"}]} -{"type": "custom_tool_call", "name": "exec", "input": "const r=await tools.mcp__clikernel__execute({\"code\":\"doc(lnhashview_file, file_exhash)\"}); for (const c of (r.content||[])) c.type===\"text\"?text(c.text):c.type===\"image\"?image(c):null;\n", "call_id": "toolu_7e7a488041b15e8ea8f9435d64bfb98e", "id": "ctc_973a3963471056c68515920e12edeb46", "status": "completed"} -{"type": "custom_tool_call_output", "call_id": "toolu_7e7a488041b15e8ea8f9435d64bfb98e", "output": [{"type": "input_text", "text": "def lnhashview_file(\n path:str, start:int=None, end:int=None\n)->LnhashView:\"\"\"Return lines formatted as space-padded ``lineno|hash|content`` for file at ``path`` (expands ``~``). Optional 1-based ``start``/``end`` filter the range; ``end`` past EOF is clamped.\"\"\"\n# doced: lnhashview_file='52c2'\n\ndef file_exhash(\n path:str, *cmds:tuple, sw:int=4, inplace:bool=True\n):\"\"\"Read files and notebook cells, apply file-aware exhash commands, and return per-target results or a combined diff.\n\nCommand tuples are the ``exhash.skill`` module docstring's; ``path`` (expands ``~``, as do qualified addresses) is the\ndefault file context for unqualified addresses. Prefix source address\nstrings, and ``m``/``t`` destination strings, with ``path:`` to target\nanother file, or ``path.ipynb:cellid:`` to target one notebook cell's\nsource (``cellid`` may be an exact id or unique prefix)::\n\n (\"src/a.py:10|aaaa|,20|bbbb|\", \"m\", \"src/b.py:$\")\n\nA range must stay within one file or cell. An ``m``/``t`` destination that\nomits the prefix inherits it from the *first address*, never from ``path``:\na bare destination like ``$`` targets the source's own file, even when\n``path`` names another. So whenever the source is qualified, qualify the\ndestination too. Escape literal colons in filenames as ``\\:`` and literal\nbackslashes as ``\\\\``. Missing files are treated as empty only for commands\nvalid against an empty buffer (``0|0000|`` with ``a``/``i``, or as an\n``m``/``t`` destination); cells are never created: a cell target must\nalready exist, or the command raises ``KeyError``.\n\nBy default (``inplace=True``) write changed files only after every command\nsucceeds and return the combined diff string (display-truncated via\n``truncate_diff``); if any command fails, write nothing. Lines addressed by ``p``\nare reported too: a ``p``-only call writes nothing and returns those lines as a bare,\nuntruncated ``lnhashview``, and printed rows in a target that also changed ride in its\ndiff as context. With more than one reported target, each printed-only group is headed\nby ``# file `` or ``# cell ``. Pass ``inplace=False`` to preview instead: a\n``FileSetEditResult`` is returned with ``files``, ``changed``, ``default_path``,\n``res[path]`` (cell targets under ``'path:cellid'``), and ``res.format_diff(context=1)``.\"\"\"\n# doced: file_exhash='cbcc'"}]} -{"type": "custom_tool_call", "name": "exec", "input": "const r=await tools.mcp__clikernel__execute({\"code\":\"lnhashview_file('core.py')\"}); for (const c of (r.content||[])) c.type===\"text\"?text(c.text):c.type===\"image\"?image(c):null;\n", "call_id": "toolu_ffd47d82a3495b69839aec94efc51878", "id": "ctc_85bf42356bcc5836bce5b6772793ade6", "status": "completed"} -{"type": "custom_tool_call_output", "call_id": "toolu_ffd47d82a3495b69839aec94efc51878", "output": [{"type": "input_text", "text": " 1|8248|\"Configuration handling for the weather project.\"\n 2|0000|\n 3|97bb|DEFAULTS = dict(units='imperial', lang='en')\n 4|0000|\n 5|0000|\n 6|e7d9|def load_cfg(path):\n 7|42b6| \"Read a cfg file from `path`, merging entries over `DEFAULTS`\"\n 8|7521| # FIXME: drop this\n 9|d643| cfg = dict(DEFAULTS)\n10|93cb| for line in open(path).read().splitlines():\n11|c33e| k, v = line.split('=', 1)\n12|8bd5| cfg[k.strip()] = v.strip()\n13|6816| return cfg"}]} -{"type": "custom_tool_call", "name": "exec", "input": "const r=await tools.mcp__clikernel__execute({\"code\":\"file_exhash('core.py',\\n (r\\\"13|6816|\\\", \\\"s\\\", r\\\"\\\\bcfg\\\\b\\\", \\\"config\\\"),\\n (r\\\"12|8bd5|\\\", \\\"s\\\", r\\\"\\\\bcfg\\\\b\\\", \\\"config\\\"),\\n (r\\\"9|d643|\\\", \\\"s\\\", r\\\"\\\\bcfg\\\\b\\\", \\\"config\\\"),\\n (r\\\"8|7521|\\\", \\\"d\\\"),\\n (r\\\"3|97bb|\\\", \\\"s\\\", \\\"imperial\\\", \\\"metric\\\"),\\n)\"}); for (const c of (r.content||[])) c.type===\"text\"?text(c.text):c.type===\"image\"?image(c):null;\n", "call_id": "toolu_d1db6dd715f05b82a8d8509a61727b8c", "id": "ctc_70db3ff464bb537c8fc8c6d9d4caf183", "status": "completed"} -{"type": "custom_tool_call_output", "call_id": "toolu_d1db6dd715f05b82a8d8509a61727b8c", "output": [{"type": "input_text", "text": "--- core.py\n+++ core.py\n 2|0000|\n-3|97bb|DEFAULTS = dict(units='imperial', lang='en')\n+3|5ffb|DEFAULTS = dict(units='metric', lang='en')\n 4|0000|\n---\n 7|42b6| \"Read a cfg file from `path`, merging entries over `DEFAULTS`\"\n-8|7521| # FIXME: drop this\n-9|d643| cfg = dict(DEFAULTS)\n+8|fa0d| config = dict(DEFAULTS)\n 9|93cb| for line in open(path).read().splitlines():\n 10|c33e| k, v = line.split('=', 1)\n-12|8bd5| cfg[k.strip()] = v.strip()\n+11|1dc0| config[k.strip()] = v.strip()\n\u20262 lines elided\u2026"}]} -{"type": "custom_tool_call", "name": "exec", "input": "const r=await tools.mcp__clikernel__execute({\"code\":\"# One call, bottom-to-top so the delete can't shift the addresses below it; the diff shows all three changes landed.\"}); for (const c of (r.content||[])) c.type===\"text\"?text(c.text):c.type===\"image\"?image(c):null;\n", "call_id": "toolu_2e9824bfb483514abfdb66f37d4721c2", "id": "ctc_091fd99b5b3858979b66b2f80cf31b05", "status": "completed"} -{"type": "custom_tool_call_output", "call_id": "toolu_2e9824bfb483514abfdb66f37d4721c2", "output": [{"type": "input_text", "text": "(mcp__clikernel__execute completed with no output)"}]} -{"type": "custom_tool_call", "name": "exec", "input": "const r=await tools.mcp__clikernel__execute({\"code\":\"# kata 3\"}); for (const c of (r.content||[])) c.type===\"text\"?text(c.text):c.type===\"image\"?image(c):null;\n", "call_id": "toolu_80987bc222a05b17aff679fc1ed1af12", "id": "ctc_78590e516dd6531083e104070734fee2", "status": "completed"} +{"type": "custom_tool_call", "name": "exec", "input": "const r=await tools.mcp__clikernel__execute({\"code\":\"# kata 2\"}); for (const c of (r.content||[])) c.type===\"text\"?text(c.text):c.type===\"image\"?image(c):null;\n", "call_id": "toolu_7e7a488041b15e8ea8f9435d64bfb98e", "id": "ctc_973a3963471056c68515920e12edeb46", "status": "completed"} +{"type": "custom_tool_call_output", "call_id": "toolu_7e7a488041b15e8ea8f9435d64bfb98e", "output": [{"type": "input_text", "text": "(mcp__clikernel__execute completed with no output)"}]} +{"type": "custom_tool_call", "name": "exec", "input": "const r=await tools.mcp__clikernel__execute({\"code\":\"doc(lnhashview_file, file_exhash)\"}); for (const c of (r.content||[])) c.type===\"text\"?text(c.text):c.type===\"image\"?image(c):null;\n", "call_id": "toolu_ffd47d82a3495b69839aec94efc51878", "id": "ctc_85bf42356bcc5836bce5b6772793ade6", "status": "completed"} +{"type": "custom_tool_call_output", "call_id": "toolu_ffd47d82a3495b69839aec94efc51878", "output": [{"type": "input_text", "text": "def lnhashview_file(\n path:str, start:int=None, end:int=None\n)->LnhashView:\"\"\"Return lines formatted as space-padded ``lineno|hash|content`` for file at ``path`` (expands ``~``). Optional 1-based ``start``/``end`` filter the range; ``end`` past EOF is clamped.\"\"\"\n# doced: lnhashview_file='52c2'\n\ndef file_exhash(\n path:str, *cmds:tuple, sw:int=4, inplace:bool=True\n):\"\"\"Read files and notebook cells, apply file-aware exhash commands, and return per-target results or a combined diff.\n\nCommand tuples are the ``exhash.skill`` module docstring's; ``path`` (expands ``~``, as do qualified addresses) is the\ndefault file context for unqualified addresses. Prefix source address\nstrings, and ``m``/``t`` destination strings, with ``path:`` to target\nanother file, or ``path.ipynb:cellid:`` to target one notebook cell's\nsource (``cellid`` may be an exact id or unique prefix)::\n\n (\"src/a.py:10|aaaa|,20|bbbb|\", \"m\", \"src/b.py:$\")\n\nA range must stay within one file or cell. An ``m``/``t`` destination that\nomits the prefix inherits it from the *first address*, never from ``path``:\na bare destination like ``$`` targets the source's own file, even when\n``path`` names another. So whenever the source is qualified, qualify the\ndestination too. Escape literal colons in filenames as ``\\:`` and literal\nbackslashes as ``\\\\``. Missing files are treated as empty only for commands\nvalid against an empty buffer (``0|0000|`` with ``a``/``i``, or as an\n``m``/``t`` destination); cells are never created: a cell target must\nalready exist, or the command raises ``KeyError``.\n\nBy default (``inplace=True``) write changed files only after every command\nsucceeds and return the combined diff string (display-truncated via\n``truncate_diff``); if any command fails, write nothing. Lines addressed by ``p``\nare reported too: a ``p``-only call writes nothing and returns those lines as a bare,\nuntruncated ``lnhashview``, and printed rows in a target that also changed ride in its\ndiff as context. With more than one reported target, each printed-only group is headed\nby ``# file `` or ``# cell ``. Pass ``inplace=False`` to preview instead: a\n``FileSetEditResult`` is returned with ``files``, ``changed``, ``default_path``,\n``res[path]`` (cell targets under ``'path:cellid'``), and ``res.format_diff(context=1)``.\"\"\"\n# doced: file_exhash='cbcc'"}]} +{"type": "custom_tool_call", "name": "exec", "input": "const r=await tools.mcp__clikernel__execute({\"code\":\"lnhashview_file('core.py')\"}); for (const c of (r.content||[])) c.type===\"text\"?text(c.text):c.type===\"image\"?image(c):null;\n", "call_id": "toolu_d1db6dd715f05b82a8d8509a61727b8c", "id": "ctc_70db3ff464bb537c8fc8c6d9d4caf183", "status": "completed"} +{"type": "custom_tool_call_output", "call_id": "toolu_d1db6dd715f05b82a8d8509a61727b8c", "output": [{"type": "input_text", "text": " 1|8248|\"Configuration handling for the weather project.\"\n 2|0000|\n 3|97bb|DEFAULTS = dict(units='imperial', lang='en')\n 4|0000|\n 5|0000|\n 6|e7d9|def load_cfg(path):\n 7|42b6| \"Read a cfg file from `path`, merging entries over `DEFAULTS`\"\n 8|7521| # FIXME: drop this\n 9|d643| cfg = dict(DEFAULTS)\n10|93cb| for line in open(path).read().splitlines():\n11|c33e| k, v = line.split('=', 1)\n12|8bd5| cfg[k.strip()] = v.strip()\n13|6816| return cfg"}]} +{"type": "custom_tool_call", "name": "exec", "input": "const r=await tools.mcp__clikernel__execute({\"code\":\"file_exhash('core.py',\\n (r\\\"13|6816|\\\", \\\"s\\\", r\\\"\\\\bcfg\\\\b\\\", \\\"config\\\"),\\n (r\\\"12|8bd5|\\\", \\\"s\\\", r\\\"\\\\bcfg\\\\b\\\", \\\"config\\\"),\\n (r\\\"9|d643|\\\", \\\"s\\\", r\\\"\\\\bcfg\\\\b\\\", \\\"config\\\"),\\n (r\\\"8|7521|\\\", \\\"d\\\"),\\n (r\\\"3|97bb|\\\", \\\"s\\\", \\\"imperial\\\", \\\"metric\\\"),\\n)\"}); for (const c of (r.content||[])) c.type===\"text\"?text(c.text):c.type===\"image\"?image(c):null;\n", "call_id": "toolu_2e9824bfb483514abfdb66f37d4721c2", "id": "ctc_091fd99b5b3858979b66b2f80cf31b05", "status": "completed"} +{"type": "custom_tool_call_output", "call_id": "toolu_2e9824bfb483514abfdb66f37d4721c2", "output": [{"type": "input_text", "text": "--- core.py\n+++ core.py\n 2|0000|\n-3|97bb|DEFAULTS = dict(units='imperial', lang='en')\n+3|5ffb|DEFAULTS = dict(units='metric', lang='en')\n 4|0000|\n---\n 7|42b6| \"Read a cfg file from `path`, merging entries over `DEFAULTS`\"\n-8|7521| # FIXME: drop this\n-9|d643| cfg = dict(DEFAULTS)\n+8|fa0d| config = dict(DEFAULTS)\n 9|93cb| for line in open(path).read().splitlines():\n 10|c33e| k, v = line.split('=', 1)\n-12|8bd5| cfg[k.strip()] = v.strip()\n+11|1dc0| config[k.strip()] = v.strip()\n\u20262 lines elided\u2026"}]} +{"type": "custom_tool_call", "name": "exec", "input": "const r=await tools.mcp__clikernel__execute({\"code\":\"# One call, bottom-to-top so the delete can't shift the addresses below it; the diff shows all three changes landed.\"}); for (const c of (r.content||[])) c.type===\"text\"?text(c.text):c.type===\"image\"?image(c):null;\n", "call_id": "toolu_80987bc222a05b17aff679fc1ed1af12", "id": "ctc_78590e516dd6531083e104070734fee2", "status": "completed"} {"type": "custom_tool_call_output", "call_id": "toolu_80987bc222a05b17aff679fc1ed1af12", "output": [{"type": "input_text", "text": "(mcp__clikernel__execute completed with no output)"}]} -{"type": "custom_tool_call", "name": "exec", "input": "const r=await tools.mcp__clikernel__execute({\"code\":\"lnhashview_file('tmpl.py')\"}); for (const c of (r.content||[])) c.type===\"text\"?text(c.text):c.type===\"image\"?image(c):null;\n", "call_id": "toolu_f392d8db22e4554ebef91ffff5f4d3e2", "id": "ctc_67b5a384e2d356e29f38f08e0dacaf6e", "status": "completed"} -{"type": "custom_tool_call_output", "call_id": "toolu_f392d8db22e4554ebef91ffff5f4d3e2", "output": [{"type": "input_text", "text": " 1|0228|\"Plain-text rendering for weather summaries.\"\n 2|0000|\n 3|0000|\n 4|dad2|def render(name, temp):\n 5|b62d| # OLD_TMPL: verbose builder kept from the prototype\n 6|f34e| parts = []\n 7|b8fb| parts.append(name)\n 8|3974| parts.append(': ')\n 9|45d1| parts.append(str(temp))\n10|c741| parts.append(' degrees')\n11|d698| out = ''.join(parts)\n12|a03d| out = out + '.'\n13|913e| return out"}]} -{"type": "custom_tool_call", "name": "exec", "input": "const r=await tools.mcp__clikernel__execute({\"code\":\"%%exhash tmpl.py 4|dad2|,13|913e| c\\ndef render(name, temp):\\n r\\\"\\\"\\\"Render a one-line summary; keep \\\\t, \\\\n and ''' literal in this docstring.\\\"\\\"\\\"\\n return name + ':\\\\t' + str(temp) + ' degrees\\\\n'\"}); for (const c of (r.content||[])) c.type===\"text\"?text(c.text):c.type===\"image\"?image(c):null;\n", "call_id": "toolu_a0d435bfb57955748273cd5b92ea208d", "id": "ctc_5559f9c261165cfca1e0c496f914e1b5", "status": "completed"} -{"type": "custom_tool_call_output", "call_id": "toolu_a0d435bfb57955748273cd5b92ea208d", "output": [{"type": "input_text", "text": "--- tmpl.py\n+++ tmpl.py\n 3|0000|\n+4|dad2|def render(name, temp):\n+5|7f91| r\"\"\"Render a one-line summary; keep \\t, \\n and ''' literal in this docstring.\"\"\"\n+6|298f| return name + ':\\t' + str(temp) + ' degrees\\n'\n-4|dad2|def render(name, temp):\n-5|b62d| # OLD_TMPL: verbose builder kept from the prototype\n-6|f34e| parts = []\n-7|b8fb| parts.append(name)\n-8|3974| parts.append(': ')\n-9|45d1| parts.append(str(temp))\n-10|c741| parts.append(' degrees')\n-11|d698| out = ''.join(parts)\n-12|a03d| out = out + '.'\n\u20261 lines elided\u2026"}]} -{"type": "custom_tool_call", "name": "exec", "input": "const r=await tools.mcp__clikernel__execute({\"code\":\"# The magic's text goes in verbatim - no quoting layer to fight, even with backslashes and both quote styles in one docstring.\"}); for (const c of (r.content||[])) c.type===\"text\"?text(c.text):c.type===\"image\"?image(c):null;\n", "call_id": "toolu_f6cea1e8ca9a5bcfb205b6997ea2c075", "id": "ctc_065f2276fcc1516499b7a34b85300724", "status": "completed"} -{"type": "custom_tool_call_output", "call_id": "toolu_f6cea1e8ca9a5bcfb205b6997ea2c075", "output": [{"type": "input_text", "text": "(mcp__clikernel__execute completed with no output)"}]} -{"type": "custom_tool_call", "name": "exec", "input": "const r=await tools.mcp__clikernel__execute({\"code\":\"# kata 4\"}); for (const c of (r.content||[])) c.type===\"text\"?text(c.text):c.type===\"image\"?image(c):null;\n", "call_id": "toolu_25ca562090275871ba1117760e8bc240", "id": "ctc_e006bff95f6450e59bf4f7f0dde5ec2a", "status": "completed"} +{"type": "custom_tool_call", "name": "exec", "input": "const r=await tools.mcp__clikernel__execute({\"code\":\"# kata 3\"}); for (const c of (r.content||[])) c.type===\"text\"?text(c.text):c.type===\"image\"?image(c):null;\n", "call_id": "toolu_f392d8db22e4554ebef91ffff5f4d3e2", "id": "ctc_67b5a384e2d356e29f38f08e0dacaf6e", "status": "completed"} +{"type": "custom_tool_call_output", "call_id": "toolu_f392d8db22e4554ebef91ffff5f4d3e2", "output": [{"type": "input_text", "text": "(mcp__clikernel__execute completed with no output)"}]} +{"type": "custom_tool_call", "name": "exec", "input": "const r=await tools.mcp__clikernel__execute({\"code\":\"lnhashview_file('tmpl.py')\"}); for (const c of (r.content||[])) c.type===\"text\"?text(c.text):c.type===\"image\"?image(c):null;\n", "call_id": "toolu_a0d435bfb57955748273cd5b92ea208d", "id": "ctc_5559f9c261165cfca1e0c496f914e1b5", "status": "completed"} +{"type": "custom_tool_call_output", "call_id": "toolu_a0d435bfb57955748273cd5b92ea208d", "output": [{"type": "input_text", "text": " 1|0228|\"Plain-text rendering for weather summaries.\"\n 2|0000|\n 3|0000|\n 4|dad2|def render(name, temp):\n 5|b62d| # OLD_TMPL: verbose builder kept from the prototype\n 6|f34e| parts = []\n 7|b8fb| parts.append(name)\n 8|3974| parts.append(': ')\n 9|45d1| parts.append(str(temp))\n10|c741| parts.append(' degrees')\n11|d698| out = ''.join(parts)\n12|a03d| out = out + '.'\n13|913e| return out"}]} +{"type": "custom_tool_call", "name": "exec", "input": "const r=await tools.mcp__clikernel__execute({\"code\":\"%%exhash tmpl.py 4|dad2|,13|913e| c\\ndef render(name, temp):\\n r\\\"\\\"\\\"Render a one-line summary; keep \\\\t, \\\\n and ''' literal in this docstring.\\\"\\\"\\\"\\n return name + ':\\\\t' + str(temp) + ' degrees\\\\n'\"}); for (const c of (r.content||[])) c.type===\"text\"?text(c.text):c.type===\"image\"?image(c):null;\n", "call_id": "toolu_f6cea1e8ca9a5bcfb205b6997ea2c075", "id": "ctc_065f2276fcc1516499b7a34b85300724", "status": "completed"} +{"type": "custom_tool_call_output", "call_id": "toolu_f6cea1e8ca9a5bcfb205b6997ea2c075", "output": [{"type": "input_text", "text": "--- tmpl.py\n+++ tmpl.py\n 3|0000|\n+4|dad2|def render(name, temp):\n+5|7f91| r\"\"\"Render a one-line summary; keep \\t, \\n and ''' literal in this docstring.\"\"\"\n+6|298f| return name + ':\\t' + str(temp) + ' degrees\\n'\n-4|dad2|def render(name, temp):\n-5|b62d| # OLD_TMPL: verbose builder kept from the prototype\n-6|f34e| parts = []\n-7|b8fb| parts.append(name)\n-8|3974| parts.append(': ')\n-9|45d1| parts.append(str(temp))\n-10|c741| parts.append(' degrees')\n-11|d698| out = ''.join(parts)\n-12|a03d| out = out + '.'\n\u20261 lines elided\u2026"}]} +{"type": "custom_tool_call", "name": "exec", "input": "const r=await tools.mcp__clikernel__execute({\"code\":\"# The magic's text goes in verbatim - no quoting layer to fight, even with backslashes and both quote styles in one docstring.\"}); for (const c of (r.content||[])) c.type===\"text\"?text(c.text):c.type===\"image\"?image(c):null;\n", "call_id": "toolu_25ca562090275871ba1117760e8bc240", "id": "ctc_e006bff95f6450e59bf4f7f0dde5ec2a", "status": "completed"} {"type": "custom_tool_call_output", "call_id": "toolu_25ca562090275871ba1117760e8bc240", "output": [{"type": "input_text", "text": "(mcp__clikernel__execute completed with no output)"}]} -{"type": "custom_tool_call", "name": "exec", "input": "const r=await tools.mcp__clikernel__execute({\"code\":\"doc(cell_exhash)\"}); for (const c of (r.content||[])) c.type===\"text\"?text(c.text):c.type===\"image\"?image(c):null;\n", "call_id": "toolu_f9859028045152e8816ffc37ebff2d15", "id": "ctc_55ea7022fe3c5cbdad87523afbec2fca", "status": "completed"} -{"type": "custom_tool_call_output", "call_id": "toolu_f9859028045152e8816ffc37ebff2d15", "output": [{"type": "input_text", "text": "def cell_exhash(\n path:str, cell_id:str, *cmds:tuple, sw:int=4, inplace:bool=True\n):\"\"\"Apply exhash commands to the source of notebook cell ``cell_id`` in ipynb file at ``path`` (expands ``~``).\n\nCommand tuples are the ``exhash.skill`` module docstring's; use\n``lnhashview_cell(path, cell_id)`` for addresses.\n``cell_id`` may be an exact id or unique prefix.\n\nBy default (``inplace=True``) write the edited source back when the source actually\nchanged (preserving the cell's original str-or-list-of-lines form; the notebook\nre-serializes in Jupyter's JSON layout) and return the diff string (display-truncated via\n``truncate_diff``); if any command fails, write nothing. A ``p``-only call writes nothing and\nreturns the printed lines as a bare, untruncated ``lnhashview``. Pass\n``inplace=False`` to preview instead: the EditResult is returned without touching the file.\"\"\"\n# doced: cell_exhash='3a30'"}]} -{"type": "custom_tool_call", "name": "exec", "input": "const r=await tools.mcp__clikernel__execute({\"code\":\"find_msgs(header_section='Retries', dlg='nbs/01_api.ipynb')\"}); for (const c of (r.content||[])) c.type===\"text\"?text(c.text):c.type===\"image\"?image(c):null;\n", "call_id": "toolu_6bc36864fa215e73bb81c6ff87b4d7f1", "id": "ctc_a44f4ec80b585e0daa652dcd00f56161", "status": "completed"} -{"type": "custom_tool_call_output", "call_id": "toolu_6bc36864fa215e73bb81c6ff87b4d7f1", "output": [{"type": "input_text", "text": "1f7ecdfb:n:## Retries\nd4f97726:n:On a connection error, `fetch_daily` retries the request twice before giving up.\n85df61e8:c:fetch_daily(52, 0)['time']"}]} -{"type": "custom_tool_call", "name": "exec", "input": "const r=await tools.mcp__clikernel__execute({\"code\":\"%%exhash nbs/01_api.ipynb d4f97726 % c\\nOn a connection error, `fetch_daily` retries the request twice more, making 3 attempts in all before giving up.\"}); for (const c of (r.content||[])) c.type===\"text\"?text(c.text):c.type===\"image\"?image(c):null;\n", "call_id": "toolu_c0d14fa67a365b03aee9bd7e6f60523b", "id": "ctc_326c2ecb2d105e759b35ffb24d016556", "status": "completed"} -{"type": "custom_tool_call_output", "call_id": "toolu_c0d14fa67a365b03aee9bd7e6f60523b", "output": [{"type": "input_text", "text": "--- original\n+++ modified\n+1|7c25|On a connection error, `fetch_daily` retries the request twice more, making 3 attempts in all before giving up.\n-1|d03b|On a connection error, `fetch_daily` retries the request twice before giving up."}]} -{"type": "custom_tool_call", "name": "exec", "input": "const r=await tools.mcp__clikernel__execute({\"code\":\"# The whole message is one line, so % c replaces it cleanly - no hashes needed for a full replace.\"}); for (const c of (r.content||[])) c.type===\"text\"?text(c.text):c.type===\"image\"?image(c):null;\n", "call_id": "toolu_7db0812118c95e68ad09dfbb22932803", "id": "ctc_f1406d4e88e656cb90773bc1762ca022", "status": "completed"} -{"type": "custom_tool_call_output", "call_id": "toolu_7db0812118c95e68ad09dfbb22932803", "output": [{"type": "input_text", "text": "(mcp__clikernel__execute completed with no output)"}]} -{"type": "custom_tool_call", "name": "exec", "input": "const r=await tools.mcp__clikernel__execute({\"code\":\"# kata 5\"}); for (const c of (r.content||[])) c.type===\"text\"?text(c.text):c.type===\"image\"?image(c):null;\n", "call_id": "toolu_fe3fe3f01ce654a085a3966db3ccb2c4", "id": "ctc_99c35b384dc45526bbf0d21363bfd33f", "status": "completed"} +{"type": "custom_tool_call", "name": "exec", "input": "const r=await tools.mcp__clikernel__execute({\"code\":\"# kata 4\"}); for (const c of (r.content||[])) c.type===\"text\"?text(c.text):c.type===\"image\"?image(c):null;\n", "call_id": "toolu_f9859028045152e8816ffc37ebff2d15", "id": "ctc_55ea7022fe3c5cbdad87523afbec2fca", "status": "completed"} +{"type": "custom_tool_call_output", "call_id": "toolu_f9859028045152e8816ffc37ebff2d15", "output": [{"type": "input_text", "text": "(mcp__clikernel__execute completed with no output)"}]} +{"type": "custom_tool_call", "name": "exec", "input": "const r=await tools.mcp__clikernel__execute({\"code\":\"doc(cell_exhash)\"}); for (const c of (r.content||[])) c.type===\"text\"?text(c.text):c.type===\"image\"?image(c):null;\n", "call_id": "toolu_6bc36864fa215e73bb81c6ff87b4d7f1", "id": "ctc_a44f4ec80b585e0daa652dcd00f56161", "status": "completed"} +{"type": "custom_tool_call_output", "call_id": "toolu_6bc36864fa215e73bb81c6ff87b4d7f1", "output": [{"type": "input_text", "text": "def cell_exhash(\n path:str, cell_id:str, *cmds:tuple, sw:int=4, inplace:bool=True\n):\"\"\"Apply exhash commands to the source of notebook cell ``cell_id`` in ipynb file at ``path`` (expands ``~``).\n\nCommand tuples are the ``exhash.skill`` module docstring's; use\n``lnhashview_cell(path, cell_id)`` for addresses.\n``cell_id`` may be an exact id or unique prefix.\n\nBy default (``inplace=True``) write the edited source back when the source actually\nchanged (preserving the cell's original str-or-list-of-lines form; the notebook\nre-serializes in Jupyter's JSON layout) and return the diff string (display-truncated via\n``truncate_diff``); if any command fails, write nothing. A ``p``-only call writes nothing and\nreturns the printed lines as a bare, untruncated ``lnhashview``. Pass\n``inplace=False`` to preview instead: the EditResult is returned without touching the file.\"\"\"\n# doced: cell_exhash='3a30'"}]} +{"type": "custom_tool_call", "name": "exec", "input": "const r=await tools.mcp__clikernel__execute({\"code\":\"find_msgs(header_section='Retries', dlg='nbs/01_api.ipynb')\"}); for (const c of (r.content||[])) c.type===\"text\"?text(c.text):c.type===\"image\"?image(c):null;\n", "call_id": "toolu_c0d14fa67a365b03aee9bd7e6f60523b", "id": "ctc_326c2ecb2d105e759b35ffb24d016556", "status": "completed"} +{"type": "custom_tool_call_output", "call_id": "toolu_c0d14fa67a365b03aee9bd7e6f60523b", "output": [{"type": "input_text", "text": "1f7ecdfb:n:## Retries\nd4f97726:n:On a connection error, `fetch_daily` retries the request twice before giving up.\n85df61e8:c:fetch_daily(52, 0)['time']"}]} +{"type": "custom_tool_call", "name": "exec", "input": "const r=await tools.mcp__clikernel__execute({\"code\":\"%%exhash nbs/01_api.ipynb d4f97726 % c\\nOn a connection error, `fetch_daily` retries the request twice more, making 3 attempts in all before giving up.\"}); for (const c of (r.content||[])) c.type===\"text\"?text(c.text):c.type===\"image\"?image(c):null;\n", "call_id": "toolu_7db0812118c95e68ad09dfbb22932803", "id": "ctc_f1406d4e88e656cb90773bc1762ca022", "status": "completed"} +{"type": "custom_tool_call_output", "call_id": "toolu_7db0812118c95e68ad09dfbb22932803", "output": [{"type": "input_text", "text": "--- original\n+++ modified\n+1|7c25|On a connection error, `fetch_daily` retries the request twice more, making 3 attempts in all before giving up.\n-1|d03b|On a connection error, `fetch_daily` retries the request twice before giving up."}]} +{"type": "custom_tool_call", "name": "exec", "input": "const r=await tools.mcp__clikernel__execute({\"code\":\"# The whole message is one line, so % c replaces it cleanly - no hashes needed for a full replace.\"}); for (const c of (r.content||[])) c.type===\"text\"?text(c.text):c.type===\"image\"?image(c):null;\n", "call_id": "toolu_fe3fe3f01ce654a085a3966db3ccb2c4", "id": "ctc_99c35b384dc45526bbf0d21363bfd33f", "status": "completed"} {"type": "custom_tool_call_output", "call_id": "toolu_fe3fe3f01ce654a085a3966db3ccb2c4", "output": [{"type": "input_text", "text": "(mcp__clikernel__execute completed with no output)"}]} -{"type": "custom_tool_call", "name": "exec", "input": "const r=await tools.mcp__clikernel__execute({\"code\":\"import report\"}); for (const c of (r.content||[])) c.type===\"text\"?text(c.text):c.type===\"image\"?image(c):null;\n", "call_id": "toolu_67793208bea158249375e442243678a6", "id": "ctc_e69321643ff35337bc56d95e3124f501", "status": "completed"} +{"type": "custom_tool_call", "name": "exec", "input": "const r=await tools.mcp__clikernel__execute({\"code\":\"# kata 5\"}); for (const c of (r.content||[])) c.type===\"text\"?text(c.text):c.type===\"image\"?image(c):null;\n", "call_id": "toolu_67793208bea158249375e442243678a6", "id": "ctc_e69321643ff35337bc56d95e3124f501", "status": "completed"} {"type": "custom_tool_call_output", "call_id": "toolu_67793208bea158249375e442243678a6", "output": [{"type": "input_text", "text": "(mcp__clikernel__execute completed with no output)"}]} -{"type": "custom_tool_call", "name": "exec", "input": "const r=await tools.mcp__clikernel__execute({\"code\":\"doc(report.daily_report)\"}); for (const c of (r.content||[])) c.type===\"text\"?text(c.text):c.type===\"image\"?image(c):null;\n", "call_id": "toolu_f8269d95c20d52688b7985e3f13f2dbf", "id": "ctc_093ea572cfcf5ba6ba02ebf726f88e35", "status": "completed"} -{"type": "custom_tool_call_output", "call_id": "toolu_f8269d95c20d52688b7985e3f13f2dbf", "output": [{"type": "input_text", "text": "def daily_report(\n data, # A fetched daily block, e.g. `SAMPLE`\n style:str='plain', # One of 'plain', 'wide', or 'tagged'\n):\"\"\"Render a daily block as a text report, one line per field.\n\nThe tagged style prefixes the header line that downstream systems parse;\nuse it for anything shipped.\"\"\"\n# doced: daily_report='220c'"}]} -{"type": "custom_tool_call", "name": "exec", "input": "const r=await tools.mcp__clikernel__execute({\"code\":\"report.daily_report(report.SAMPLE, style='tagged')\"}); for (const c of (r.content||[])) c.type===\"text\"?text(c.text):c.type===\"image\"?image(c):null;\n", "call_id": "toolu_1f0a4e0cd5195382880871e7f60f52e5", "id": "ctc_c0fc1307c72b5143810222617944752b", "status": "completed"} -{"type": "custom_tool_call_output", "call_id": "toolu_1f0a4e0cd5195382880871e7f60f52e5", "output": [{"type": "input_text", "text": "'WX7034\\ntime: 2024-01-01, 2024-01-02\\ntemperature_2m_max: 3.1, 4.7'"}]} -{"type": "custom_tool_call", "name": "exec", "input": "const r=await tools.mcp__clikernel__execute({\"code\":\"dojo_score(bash_calls=0,\\n orient=\\\"httpx was chosen over requests. requests has no async support, and it does no connection pooling on its own. httpx keeps the same API shape and provides both.\\\",\\n report=\\\"WX7034\\\")\"}); for (const c of (r.content||[])) c.type===\"text\"?text(c.text):c.type===\"image\"?image(c):null;\n", "call_id": "toolu_c24d11d5a3595e3da963a368f2410786", "id": "ctc_fd97e8b8097a5ab9b14da217232310d7", "status": "completed"} -{"type": "custom_tool_call_output", "call_id": "toolu_c24d11d5a3595e3da963a368f2410786", "output": [{"type": "input_text", "text": "strokes 8 + doc penalties 0 = 8, par 8\n 0| %cd /tmp/dojo\n 0| # kata 1\n 0| doc(find_msgs, view_dlg)\n 1| view_dlg('nbs/01_api.ipynb')\n 0| # Kata 1 answered; on to the file edits.\n 0| # kata 2\n 0| doc(lnhashview_file, file_exhash)\n 1| lnhashview_file('core.py')\n 1| file_exhash('core.py',\n 0| # One call, bottom-to-top so the delete can't shift the addresses belo\n 0| # kata 3\n 1| lnhashview_file('tmpl.py')\n 1| %%exhash tmpl.py 4|dad2|,13|913e| c\n 0| # The magic's text goes in verbatim - no quoting layer to fight, even \n 0| # kata 4\n 0| doc(cell_exhash)\n 1| find_msgs(header_section='Retries', dlg='nbs/01_api.ipynb')\n 1| %%exhash nbs/01_api.ipynb d4f97726 % c\n 0| # The whole message is one line, so % c replaces it cleanly - no hashe\n 0| # kata 5\n 0| import report\n 0| doc(report.daily_report)\n 1| report.daily_report(report.SAMPLE, style='tagged')\nlooked like tags but aren't (exact format: a comment-only cell '# kata '): '# Kata 1 answered; on to the file edits.'\nkata 'orient' (strokes 1, par 1): ok\n par route: find_msgs('httpx'): its context= defaults to 1, and the why lives in the note next to the import - a message the pattern doesn't even match, so no search row can surface it. view_dlg reads whole small notebooks fine too. rg and nbrg locate; neither is reading\nkata 'edit set' (strokes 2, par 2): ok\n par route: lnhashview_file, then ONE file_exhash with each command tuple as a positional argument, worked bottom-to-top: the deletion shifts every line below it, and the hash checks catch top-down ordering loudly. s patterns are regexes: escape literal [ ] ( ) . or the call fails. rg(pattern, lnhashs=True) is an equal entry when you know what to look for: hits arrive as edit-ready addresses, fusing locate and view\nkata 'awkward replace' (strokes 2, par 2): ok\n par route: lnhashview_file, then one %%exhash with a range-c address; text verbatim, no quoting. (% c would replace the whole file: too much here)\nkata 'notebook edit' (strokes 2, par 2): ok\n par route: doc(find_msgs) free, find_msgs(header_section=...), then one edit. The old text is known exactly, so a content-addressed edit needs no view: msg_str_replace(id, old, new, dlg=path) or cell_str_replace(path, cell_id, old, new). %%exhash % c replaces the whole cell instead, for when the text is awkward to quote or the target positional\nkata 'doc first' (strokes 1, par 1): ok\n par route: import + doc(report.daily_report) are free, and the full docstring names the style that ships; one cell makes the call. A guessed call runs fine and scores nothing\nClean round. Run dir removed and cwd restored. Completion id: 24b2 - keep this id, including through compaction: passing dojo_start('24b2') in a future session skips the round.\nThe dojo is an early version: include in your report anything above that seemed possibly-imperfect (stroke counts, findings, prompts)."}]} +{"type": "custom_tool_call", "name": "exec", "input": "const r=await tools.mcp__clikernel__execute({\"code\":\"import report\"}); for (const c of (r.content||[])) c.type===\"text\"?text(c.text):c.type===\"image\"?image(c):null;\n", "call_id": "toolu_f8269d95c20d52688b7985e3f13f2dbf", "id": "ctc_093ea572cfcf5ba6ba02ebf726f88e35", "status": "completed"} +{"type": "custom_tool_call_output", "call_id": "toolu_f8269d95c20d52688b7985e3f13f2dbf", "output": [{"type": "input_text", "text": "(mcp__clikernel__execute completed with no output)"}]} +{"type": "custom_tool_call", "name": "exec", "input": "const r=await tools.mcp__clikernel__execute({\"code\":\"doc(report.daily_report)\"}); for (const c of (r.content||[])) c.type===\"text\"?text(c.text):c.type===\"image\"?image(c):null;\n", "call_id": "toolu_1f0a4e0cd5195382880871e7f60f52e5", "id": "ctc_c0fc1307c72b5143810222617944752b", "status": "completed"} +{"type": "custom_tool_call_output", "call_id": "toolu_1f0a4e0cd5195382880871e7f60f52e5", "output": [{"type": "input_text", "text": "def daily_report(\n data, # A fetched daily block, e.g. `SAMPLE`\n style:str='plain', # One of 'plain', 'wide', or 'tagged'\n):\"\"\"Render a daily block as a text report, one line per field.\n\nThe tagged style prefixes the header line that downstream systems parse;\nuse it for anything shipped.\"\"\"\n# doced: daily_report='220c'"}]} +{"type": "custom_tool_call", "name": "exec", "input": "const r=await tools.mcp__clikernel__execute({\"code\":\"report.daily_report(report.SAMPLE, style='tagged')\"}); for (const c of (r.content||[])) c.type===\"text\"?text(c.text):c.type===\"image\"?image(c):null;\n", "call_id": "toolu_c24d11d5a3595e3da963a368f2410786", "id": "ctc_fd97e8b8097a5ab9b14da217232310d7", "status": "completed"} +{"type": "custom_tool_call_output", "call_id": "toolu_c24d11d5a3595e3da963a368f2410786", "output": [{"type": "input_text", "text": "'WX7034\\ntime: 2024-01-01, 2024-01-02\\ntemperature_2m_max: 3.1, 4.7'"}]} +{"type": "custom_tool_call", "name": "exec", "input": "const r=await tools.mcp__clikernel__execute({\"code\":\"dojo_score(bash_calls=0,\\n orient=\\\"httpx was chosen over requests. requests has no async support, and it does no connection pooling on its own. httpx keeps the same API shape and provides both.\\\",\\n report=\\\"WX7034\\\")\"}); for (const c of (r.content||[])) c.type===\"text\"?text(c.text):c.type===\"image\"?image(c):null;\n", "call_id": "toolu_bca5c5b9ef555e7b9543085f68e25917", "id": "ctc_3fe3e803fd8b5468b8bc06a0cc962f93", "status": "completed"} +{"type": "custom_tool_call_output", "call_id": "toolu_bca5c5b9ef555e7b9543085f68e25917", "output": [{"type": "input_text", "text": "strokes 8 + doc penalties 0 = 8, par 8\n 0| %cd /tmp/dojo\n 0| # kata 1\n 0| doc(find_msgs, view_dlg)\n 1| view_dlg('nbs/01_api.ipynb')\n 0| # Kata 1 answered; on to the file edits.\n 0| # kata 2\n 0| doc(lnhashview_file, file_exhash)\n 1| lnhashview_file('core.py')\n 1| file_exhash('core.py',\n 0| # One call, bottom-to-top so the delete can't shift the addresses belo\n 0| # kata 3\n 1| lnhashview_file('tmpl.py')\n 1| %%exhash tmpl.py 4|dad2|,13|913e| c\n 0| # The magic's text goes in verbatim - no quoting layer to fight, even \n 0| # kata 4\n 0| doc(cell_exhash)\n 1| find_msgs(header_section='Retries', dlg='nbs/01_api.ipynb')\n 1| %%exhash nbs/01_api.ipynb d4f97726 % c\n 0| # The whole message is one line, so % c replaces it cleanly - no hashe\n 0| # kata 5\n 0| import report\n 0| doc(report.daily_report)\n 1| report.daily_report(report.SAMPLE, style='tagged')\nlooked like tags but aren't (exact format: a comment-only cell '# kata '): '# Kata 1 answered; on to the file edits.'\nkata 'orient' (strokes 1, par 1): ok\n par route: find_msgs('httpx'): its context= defaults to 1, and the why lives in the note next to the import - a message the pattern doesn't even match, so no search row can surface it. view_dlg reads whole small notebooks fine too. rg and nbrg locate; neither is reading\nkata 'edit set' (strokes 2, par 2): ok\n par route: lnhashview_file, then ONE file_exhash with each command tuple as a positional argument, worked bottom-to-top: the deletion shifts every line below it, and the hash checks catch top-down ordering loudly. s patterns are regexes: escape literal [ ] ( ) . or the call fails. rg(pattern, lnhashs=True) is an equal entry when you know what to look for: hits arrive as edit-ready addresses, fusing locate and view\nkata 'awkward replace' (strokes 2, par 2): ok\n par route: lnhashview_file, then one %%exhash with a range-c address; text verbatim, no quoting. (% c would replace the whole file: too much here)\nkata 'notebook edit' (strokes 2, par 2): ok\n par route: doc(find_msgs) free, find_msgs(header_section=...), then one edit. The old text is known exactly, so a content-addressed edit needs no view: msg_str_replace(id, old, new, dlg=path) or cell_str_replace(path, cell_id, old, new). %%exhash % c replaces the whole cell instead, for when the text is awkward to quote or the target positional\nkata 'doc first' (strokes 1, par 1): ok\n par route: import + doc(report.daily_report) are free, and the full docstring names the style that ships; one cell makes the call. A guessed call runs fine and scores nothing\nClean round. Run dir removed and cwd restored. Completion id: dbfc - keep this id, including through compaction: passing dojo_start('dbfc') in a future session skips the round.\nThe dojo is an early version: include in your report anything above that seemed possibly-imperfect (stroke counts, findings, prompts)."}]} {"type": "message", "role": "assistant", "content": [{"type": "output_text", "text": "OK I'm ready."}]} diff --git a/llmdojo/dojo_data/dojo_template.ipynb b/llmdojo/dojo_data/dojo_template.ipynb deleted file mode 100644 index aa0b7d0..0000000 --- a/llmdojo/dojo_data/dojo_template.ipynb +++ /dev/null @@ -1,361 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "7df0e355", - "metadata": { - "solveit_ai": true - }, - "source": [ - "Bootstrap and complete the dojo and tell me when you're ready.\n", - "\n", - "##### 🤖Reply🤖\n", - "\n", - "Bootstrapping: the startup script has already run its imports, so I read the tooling docs, check the catalog, then take the dojo.\n", - "\n", - "```json {.tool}\n", - "{\n", - " \"id\": \"toolu_01VSqyGbsLQBosaqN92Tz7KB\",\n", - " \"name\": \"mcp__clikernel__execute\",\n", - " \"args\": {\n", - " \"code\": \"doc(clik, pysk, edsk)\"\n", - " },\n", - " \"result\": \"# module clikernel.skill:\\n\\n\\\"\\\"\\\"Use the `clikernel` MCP session as the default workspace for Python work: reading and changing files, notebook work, trying things out, checking how a library behaves, and reshaping data. One session stays open, so imports and variables carry between calls. Read this before writing, running, or debugging Python code in a session with `clikernel` connected.\\n\\nPrefer it over one-off Python scripts (`python -c`, shell heredocs). Prefer in-kernel tools over shell equivalents when they exist: file search and directory listing go through the `rgapi` pyskill (`rg()`/`fd()`/`ls()`), and GitHub and local git work through the `ghapi` pyskill, when those are installed. Shell commands remain the right tool for project test/build commands and non-Python tools.\\n\\n# Starting and stopping\\n\\nThe first `execute` creates a kernel and reports what it imported: read that banner, it says what to do next. That kernel stops when the conversation ends. Use a bare `connect` instead when the kernel should stay running afterwards, and tell the user its id.\\n\\nTo continue earlier work, or to use the user's solveit kernel, `list_kernels` then `connect` with the id. Attaching runs no setup: the kernel's live state is the point.\\n\\n`restart` gives a fresh interpreter under the same id, so redo any setup. `interrupt` stops a long `execute` and keeps state. If a reply says the kernel has stopped, `connect` again. If `connect` fails, the gateway server is not running: tell the user rather than working around it.\\n\\nRemote gateways are the same verbs with a `host`, named in `~/.config/clikernel/gateways.toml`.\\n\\n# Working in it\\n\\n- Magics work as written. `%cd` expands `~` and is the way to change directory: prefer it over `os.chdir`.\\n- Only the last expression in a cell displays. `print(...)` any earlier value you need to see.\\n- Everything a cell outputs lands in the conversation. Be selective: `len(v)` first, then decide what to show.\\n- Don't re-run an `import` already run this session. If a name raises `NameError`, the kernel restarted or is newly attached: redo setup.\\n- After an `nbdev-export` (or any edit to a module already imported), `importlib.reload` that module and re-import any names you hold from it: a name bound by `from x import y` keeps the old object, while a `@patch`ed method refreshes with the reload because the patch writes onto the shared class. Restart only when a class you hold instances of was itself redefined. Check what is loaded by calling it, never with `inspect.getsource`, which reads the file on disk.\\n- Try the simple import or API call first, before changing the environment, monkeypatching, or adding setup.\\n\\\"\\\"\\\"\\n\\n# module pyskills.skill:\\n\\n\\\"\\\"\\\"Pyskills are tool modules that Python packages register so you can find them without importing everything. `list_pyskills()` names the ones installed, with a one-line description each, and needs no imports. Load one with a normal import, then read its docs with `doc()`.\\n\\nUse `doc()` at increasing detail, module first, then the specific class or function before its first use:\\n\\n import pyskills.skill\\n doc(pyskills.skill) # module overview: classes, functions, submodules\\n doc(SkillTestClass, skill_test_func) # symbol detail: full signatures with docments, one section each\\n\\nNormally use `from import *` when loading a pyskill: each pyskill's `__all__` is carefully curated, so a star import brings in exactly the intended API.\\n\\n`doc()` returns a `PrettyString`. A bare final call is rendered by IPython; assigning the result is silent. Assign it when you do not want it rendered, for instance for very large docs you want to search through.\\n\\nDoc the module once while its output is still visible in the conversation, then doc each class or function right before its first call. This is conversation state, not Python-process state: read it again when the earlier output is no longer visible, not merely because the kernel restarted.\\n\\nIn a module overview, a trailing `...` on a line means docments and usage notes exist beyond the signature and first docstring line, and only `doc(func)` shows them: read it before the first call, however complete the line looks. A line with no `...` is that function's whole doc. A `**name` collector (other than `**kwargs` itself) is a shared param group: its params are listed once under `## shared params:` and are passed as ordinary keyword args. `doc` takes several objects at once, each returned as its own section, so batch the reads.\\n\\nWhen more than one pyskill looks like a candidate for a task, `doc()` each candidate rather than guessing from the one-line descriptions: some specialize by input type (e.g. `fastcore.tools` for plain text and files vs `aidialog.dlgskill` for notebooks and dialogs; prefer `exhash.skill` for text editing when it's available) and the short description won't always make the distinction clear.\\n\\nPyskill results are designed so the right call answers the question directly. Post-processing a result with generic Python (a `split`/`join`/slice/comprehension over its output) is a workaround smell: it usually means the call was wrong or a parameter was missed. Check `doc()` for the parameter or sibling function that answers directly; if it genuinely doesn't exist, propose extending the module rather than bridging with ad hoc code.\\n\\nThe same smell applies on the way in: wrapping an argument in `str()`, `expanduser()`, pre-escaping, or path-joining that the call already handles means either the docments weren't read or the tooling or its docs need fixing. Check the parameter before dressing the argument, and tell the user when the most ergonomic argument handling doesn't exist or isn't documented: improving tooling is always first priority.\\n\\nResults are built to be read as their bare reprs: end the cell with the bare expression rather than `print(...)`, which flattens a tuned display to plain `str()`. If a bare result ever reads worse than a printed or reformatted version, the repr is deficient: fix it or tell the user, never quietly work around it.\\n\\nSummarize what a pyskill's docs or results say rather than dumping the full output verbatim, unless the user actually needs to see all of it.\\n\\n`doc()` works on *all* python modules, not only pyskills.\\n\\n`xdir(sym, q=None)` complements `doc()`: it lists an object's public names, filtered by an optional case-insensitive regex. Use it when a module, class, or dynamic API surface is too big to `doc()` whole, e.g. `xdir(page.emulation, 'viewport')` on a fastcdp CDP domain, then `doc()` the match before calling it.\\n\\n`info_md(obj, source=False)` (from `ipykernel_helper`, preloaded by clikernel startup where installed) is the third way to read an object: IPython's `?` -- or `??` with `source=True` -- rendered as markdown. Reach for it when you want an object's real signature, docstring, and source together, rather than `inspect.getsource`/`inspect.signature` or bare `?`/`??`.\\n\\n`from pyskills import createskill; doc(createskill)` for how to build and register your own pyskill modules.\\n\\\"\\\"\\\"\\n\\n## types:\\n- class SkillTestClass(str): ... # Some class.\\n\\n## functions:\\n- async def async_skill_test_func(x: int = 0) -> str: ... # A test function…\\n- def skill_test_func(x: int = 0) -> str: ... # A test function…\\n\\n## elided: 1 submodules. `doc('pyskills.skill', all=True)` lists them.\\n\\n# module fastcore.editskill:\\n\\n\\\"\\\"\\\"Text, file, cell, and notebook editing from `fastcore.tools` and `fastcore.nbio`, plus the conventions the whole fastai editing toolkit follows. Read this before working with the editing tools in any package that shares them.\\n\\n`from fastcore.editskill import *` loads the fastcore editing layer: the text primitives and file tools of `fastcore.tools`, and the notebook I/O and cell editors of `fastcore.nbio`. Sibling packages extend the same toolkit, and where they are installed prefer them as noted: `exhash` for hash-verified editing, `rgapi` for search, `aidialog`/`dialoghelper` for dialogs.\\n\\n## Places\\n\\nEach editing function works on one place: text (a str in memory), a file (a path on disk), a cell (one cell's source in an .ipynb file, addressed by `path, cell_id`), or a notebook (the parsed .ipynb; `Notebook` and `NbCell` are its held-object forms). These are the representation layer: an .ipynb is a file of cells, whatever produced it. The dialog layer above adds the msg and dlg places: a Solveit dialog is an .ipynb whose cells are messages (notes, runnable code, prompt/reply pairs), and `aidialog.dlgskill` and `dialoghelper` provide the message tools, following the conventions here with their own nouns. The word picks the layer: cell tools answer representation questions (\\\"why does Jupyter reject this file?\\\"), msg tools answer content questions (\\\"what does this message say?\\\").\\n\\nThe section layer sits above both. `open_doc` (in exhash) parses a file (`fname=`, or a `Path` as `src`), URL (an `https?://` str), or text (any other str) into a `Section` tree, with sections taken from Markdown headings, tree-sitter definitions in code, or md-heading cells in a notebook. A section points at a span of lines in a file, or a run of cells in a notebook. To change what a section contains, edit those lines or cells with the file and cell tools.\\n\\n## Naming\\n\\nTwo name shapes cover the toolkit, and the pivot is the verb's direct object:\\n\\n- An operation on a whole place takes the place as its noun: verb_place. `view_file`, `create_file`, `read_nb`, `write_nb`, `view_cell`, `validate_nb`; in the dialog layer `view_msg` and `view_dlg`. Coined verbs follow the same shape: `lnhashview_cell` is \\\"lnhashview this cell\\\". When the verb's object is instead the unit inside the place, and that unit names its place uniquely, no prefix is needed - the unit noun is the place signal: `find_msgs`, `add_msg`, `del_msgs` (msgs live only in dialogs), `find_cells`, `summary_nb`'s rows (cells live only in notebooks).\\n- An operation within a place already owns its noun (`insert_line`, `del_lines`, `replace_lines`, `str_replace`), so the place prefixes as a namespace and the op name survives intact: place_op, as in `file_del_lines`, `cell_del_lines`, `msg_del_lines`. The bare op names are the text-level primitives, and every place version keeps the identical signature after its address arguments, so each family is learned once and recognized everywhere.\\n\\nThe exceptions are deliberate and closed. `str_replace` keeps the name and argument order established by Anthropic's text editor tool. Instrument-named ops put the instrument first and elide their unit: `ast_replace` (the AST pattern is how the edit finds its target) and `exhash` (hash-verified line addresses travel inside its commands), place-prefixed like any other line-level op: `file_ast_replace`, `msg_ast_replace`, `file_exhash`, `cell_exhash`. Converters are named x2y (`nb2dict`, `cell2xml`; in aidialog, `dlg` on exactly one side of every converter), and on a held object the converter is a `to_y` method (`nb.to_dict()`). Plural marks arity: `view_cell` takes one cell, `lnhashview_cells` several, `del_msgs` many.\\n\\n## Parameters\\n\\nOne vocabulary, identical wherever it appears:\\n\\n- The place's address comes first (`text`; `path`; `path, cell_id`; a message `id`), the new text next, and ambient context last as keyword-only (message tools name their dialog that way).\\n- `start_line`/`end_line`: 1-based, inclusive, `None` for first/last, negative counting from the end. `del_lines` accepts no defaults: state the range.\\n- `re_filter`/`invert_filter`: restrict an edit to lines matching (or not matching) a regex, like ex's `g//` and `g!//`; combines with the range.\\n- Searches read patterns as regex by default; editors read them as literal text until `use_regex=True`. Searching is read-only, so its default favors power; editing favors caution.\\n- `nums` and `lnhashs` on any view: line numbers, or `lineno|hash|` addresses. `maxlen` caps characters per summary line; `trunc_out`/`trunc_in` truncate outputs and sources in dialog views.\\n- Search tools share one filter vocabulary: `pattern` first, `root='.'`, and the same include/exclude/ext/hidden/ignore block across `fd`, `ls`, `rg`, and `nbrg`. Variants differ by defaults, not API: `ls` is `fd` with listing defaults. Boolean filters narrow as `only_*` and widen as `include_*`.\\n- `context=` counts whatever units the place has: lines (or blocks in summary mode) for files, cells for notebooks, messages for dialogs. Dialog search defaults to context 1 because the neighbouring note usually explains the match: the why lives next to the what.\\n- Every editor returns a diff (\\\"none: No changes.\\\" when nothing changed). The diff is the verification: read it instead of re-viewing the target.\\n\\n## Functions and methods\\n\\nEvery operation has two shapes with one contract each. The function is a transaction: it addresses a file by path, applies the edit, writes, and returns a diff. The method is a session: it changes the object in hand, and nothing reaches disk until an explicit save. The correspondence is mechanical - the method is the function minus its address arguments, keeping its name except that a place word which became `self` drops: `cell_str_replace(path, cell_id, ...)` is `c.str_replace(...)` on a held `NbCell`, `find_cells(path, pat)` is `nb.find_cells(pat)` on a held `Notebook`. Function-side reads return plain snapshot rows carrying addresses, source, and meta; session-side reads return the objects themselves. Use one shape at a time per file: save before switching to functions, reopen after.\\n\\n## Addresses\\n\\nEdits say where with line numbers, lnhash addresses, or section tokens. Take addresses from the read you were already doing instead of with a second look: views accept `nums=True` or `lnhashs=True`, and searches return addresses directly (`rg(lnhashs=True)`). Prefer lnhash addresses whenever `exhash` is installed: they are verified against current content at edit time, so a stale address fails loudly instead of editing nearby text, which is exactly why taking addresses early works. Plain line numbers are unverified and shift as edits apply, so re-view after each edit and apply multi-edits bottom-to-top. `exhash.skill` owns the address format and the verified editor. Section tokens make a long document navigable and editable a piece at a time. An outline row shows one, as in `1.6.|12|a3f2|,45|b1c3|`: the section number, then the lnhash addresses of its first and last lines. `d.at(token)` returns that section with its hash checked, `d.view(tok1, tok2)` prints the rendered text of each, and the same boundary pair drops straight into a range edit: `file_exhash(path, ('12|a3f2|,45|b1c3|', 'c', new_text))`. Read `exhash.skill` before working through a large Markdown, code, or notebook file, or before reading documentation from a URL: `open_doc` also searches within a tree and follows links, which is how the llms.txt docs workflow runs.\\n\\nWhen you don't yet know where to edit, locate with a summary first: `rgapi`'s `rg(summary=True)` and `nbrg`, aidialog's `summary_dlg`, and exhash's `open_doc` outlines each show one row per natural unit (block, cell, message, section), carrying the unit's address. For prose, config, and other paragraph-shaped text, blocks are usually the right unit for reading too: one summary row shows the whole matched paragraph with its boundary addresses, where a line-mode hit shows a fragment that then needs a context view. Summaries locate, views read, addresses edit, diffs confirm.\\n\\n## What's where\\n\\n- `fastcore.tools`: text primitives, file tools, and `line_hash`/`lnhash`/`lnhash_at` for creating addresses without exhash installed.\\n- `fastcore.nbio`: notebook read/write/validate/repair, cell construction, cell editors, and the `Notebook`/`NbCell` session objects with their snapshot queries (`find_cells`, `summary_nb`).\\n- `exhash.skill`: hash-verified editing for files and cells, plus `open_doc` section outlines for Markdown, code, and notebooks; prefer it for edits where installed.\\n- `rgapi.skill`: `rg`/`fd`/`ls`/`nbrg` search with lnhash output, and `rgstr` to search text already in hand.\\n- `remold`: structural search and rewrite for Python source (declarative ast-grep rules, LibCST matcher transforms, symbol queries); the engine behind `ast_replace`.\\n- `aidialog.dlgskill`, `dialoghelper`: the dialog layer, including its own theory of dialogs and projections.\\n\\nDocs: https://fastcore.fast.ai/tools.html.md and https://fastcore.fast.ai/nbio.html.md\\n\\\"\\\"\\\"\\n\\n## types:\\n- class Notebook: ... # Read, query, and edit Jupyter notebooks\\n- class NbCell(AttrDict,adict,dict)\\n\\n## functions:\\n- def insert_line(text: str, insert_line: int, new_str: str): ... # Insert new_str at specified line number…\\n- def str_replace(text: str, old_str: str, new_str: str, **replace_params): ... # Replace occurrence(s) of old_str with new_str…\\n- def strs_replace(text: str, old_strs: list[str], new_strs: list[str], **replace_params): ... # Replace multiple strings simultaneously…\\n- def replace_lines(text: str, start_line: int = None, end_line: int = None, new_content: str = ''): ... # Replace line range with new content; the defaults replace the entire contents…\\n- def del_lines(text: str, start_line: int, end_line: int, re_filter: str = None, invert_filter: bool = False): ... # Delete line range; deletion is destructive, so both line numbers must be given explicitly (`1, -1` for all lines)…\\n- def ast_replace(text: str, repls: list): ... # Apply ast-grep structural pattern replacements to python source…\\n- def file_insert_line(path: str, insert_line: int, new_str: str): ... # Insert new_str at specified line number…\\n- def file_str_replace(path: str, old_str: str, new_str: str, **replace_params): ... # Replace occurrence(s) of old_str with new_str…\\n- def file_strs_replace(path: str, old_strs: list[str], new_strs: list[str], **replace_params): ... # Replace multiple strings simultaneously…\\n- def file_replace_lines(path: str, start_line: int = None, end_line: int = None, new_content: str = ''): ... # Replace line range with new content; the defaults replace the entire contents…\\n- def file_del_lines(path: str, start_line: int, end_line: int, re_filter: str = None, invert_filter: bool = False): ... # Delete line range; deletion is destructive, so both line numbers must be given explicitly (`1, -1` for all lines)…\\n- def file_ast_replace(path: str, repls: list): ... # Apply ast-grep structural pattern replacements to python source…\\n- def view_file(path: str, start_line: int = 1, end_line: int = None, nums: bool = True, lnhashs: bool = False): ... # Read file contents, optionally limited to 1-based line range…\\n- def create_file(path: str, contents: str, overwrite: bool = False): ... # Create a new file with contents. Error if file exists, unless `overwrite`.…\\n- def line_hash(line: str) -> str: ... # 4-char hex hash of `line`…\\n- def lnhash(lineno: int, line: str) -> str: ... # `lineno|hash|` exhash address for `line` at `lineno`…\\n- def lnhash_at(s: str | list | tuple, line: int) -> str: ... # `lineno|hash|` exhash address of line `line` of `s`…\\n- def read_nb(path): ... # Return notebook at `path` (expands `~`) [1bdc]\\n- def write_nb(nb, path): ... # Write `nb` to `path` (expands `~`) [a307]\\n- def new_nb(cells=None, meta=None, nbformat=4, nbformat_minor=5): ... # Returns an empty new notebook [dee4]\\n- def mk_cell(text, cell_type='code', **kwargs): ... # Create an `NbCell` containing `text`…\\n- def validate_nb(nb): ... # Raise `ValueError` for structural problems in notebook `nb`; returns it unchanged if fine [141c]\\n- def validate_cell(cell, idx=None): ... # Raise `ValueError` for structural problems in notebook cell dict `cell`; returns it unchanged if fine [0ba9]\\n- def repair_nb(nb): ... # Fix deterministic structural problems in `nb`, returning a list of repairs made [137e]\\n- def repair_cell(cell, idx=None): ... # Fix deterministic structural problems in `cell`, returning a list of repairs made [8864]\\n- def view_cell(path: str, cell_id: str, start_line: int = 1, end_line: int = None, nums: bool = True, lnhashs: bool = False, incl_out: bool = False, trunc_out: bool = True): ... # View a cell's source, optionally limited to 1-based line range…\\n- def cell_insert_line(path: str, cell_id: str, insert_line: int, new_str: str): ... # Insert new_str at specified line number…\\n- def cell_str_replace(path: str, cell_id: str, old_str: str, new_str: str, **replace_params): ... # Replace occurrence(s) of old_str with new_str…\\n- def cell_strs_replace(path: str, cell_id: str, old_strs: list[str], new_strs: list[str], **replace_params): ... # Replace multiple strings simultaneously…\\n- def cell_replace_lines(path: str, cell_id: str, start_line: int = None, end_line: int = None, new_content: str = ''): ... # Replace line range with new content; the defaults replace the entire contents…\\n- def cell_del_lines(path: str, cell_id: str, start_line: int, end_line: int, re_filter: str = None, invert_filter: bool = False): ... # Delete line range; deletion is destructive, so both line numbers must be given explicitly (`1, -1` for all lines)…\\n- def cell_ast_replace(path: str, cell_id: str, repls: list): ... # Apply ast-grep structural pattern replacements to python source…\\n- def find_cells(path, pat: str = '', cell_type: str = None, ids='', context: int = None): ... # Snapshot `FoundCells` for matching cells in the notebook at `path`…\\n- def summary_nb(path, maxlen: int = 180): ... # One snapshot line per cell of the notebook at `path`…\\n\\n## shared params:\\n- **replace_params (from `str_replace`):\\n start_line: int = None, # Optional 1-based start line to limit search\\n end_line: int = None, # Optional 1-based end line to limit search\\n n_matches: int = None, # Max replacements (None=all)\\n re_filter: str = None, # If provided, only process lines matching this regex (like g// in ex)\\n invert_filter: bool = False, # Invert the filter (like g!// in ex)\\n use_regex: bool = False, # Treat old_str as a regex, and new_str as an `re.sub` template?\"\n", - "}\n", - "```\n", - "\n", - "```json {.tool}\n", - "{\n", - " \"id\": \"toolu_01FUJHFXpnggen2yQbS3tQGE\",\n", - " \"name\": \"mcp__clikernel__execute\",\n", - " \"args\": {\n", - " \"code\": \"doc(dsk, exh, rgsk)\"\n", - " },\n", - " \"result\": \"# module aidialog.dlgskill:\\n\\n\\\"\\\"\\\"Read, search, and edit dialogs and notebooks through aidialog's `Dialog` and `Message` classes\\n\\nUse this whenever the question or edit concerns a notebook's content: messages, sources, outputs, prompts and their replies.\\n\\n## The hierarchy\\n\\nNotebook work happens at three levels, and picking the right level is most of using this module well:\\n\\n- Content (this module): what the messages say and how they change. `summary_dlg`, `find_msgs`, `view_dlg`, the message editing operations.\\n- Representation (`fastcore.nbio`): which keys exist, whether a file is schema-valid, whether bytes changed. Start with `validate_nb`/`validate_cell`, which name the cell at fault; use `read_nb` directly when the question is about the dict itself. For *plain* notebooks (no dialog semantics), nbio's `Notebook`/`NbCell` objects and `cell_*` functions are the content surface, and this module's word choice marks the layer: cells for notebooks, messages for dialogs.\\n- Raw text: only when the file will not parse at all.\\n\\n## Core APIs\\n\\nThe function/method two-shapes contract is `fastcore.editskill`'s, learned once: the function is a transaction addressed by `dlg=` (an ipynb path, or None meaning the current dialog file: `set_dlg`); the method is a session on a held `Dialog` or `Message`, saved by an explicit `dlg.save()` (`msg_str_replace(id, ..., dlg=p)` ⟷ `m.str_replace(...)`). Wrapping any message list in an ephemeral `Dialog(msgs)` makes the whole session surface available on it.\\n\\n- `summary_dlg(dlg)` / `d.summary()`: one `preview` per message, `id:t[directives]:content` (t: c=code n=note p=prompt r=raw; the bracket, as in nbio's `CellRow`, shows meta-form nbdev directives such as `[export]`), with a prompt's reply on a following `> ` line; a line cut short ends in a humanized `[size]`.\\n- `find_msgs(pattern, dlg, ...)`: search by regex, type, errors, heading, ids, or a `pred` (`symdef_finder`/`symref_finder`/`ast_finder` build structural ones); `context` defaults to 1 even with `ids=`, because the neighbouring message usually explains the match -- retrieve and read it. Returns `MsgRow` snapshots (`id`, `msg_type`, `content`, `out`, `meta`) in a `MsgRows`; `d.find_msgs(...)` returns live `Message`s in a `FoundMsgs`. Both index by message id (exact or unique prefix) and refuse integer positions: a find result includes context rows, so `[0]` may be a neighbour of the match, and the raised error teaches the id idiom. Previews mark context rows with `-` in place of the first `:`. Every live message carries a `dlg` backref to its owning `Dialog`, so dialog-level operations are always in reach from a message in hand (e.g. `m.dlg.save()` after mutating `m.output` directly).\\n- `view_dlg(dlg)` / `d.view()` / `view_msg(id)` / `m.view()` / `view_msgs(*ids)` / `msg2xml(m)` / `m.to_xml()`: full views in the shared `item2xml` grammar (a prompt's reply is its `` section; meta `nbdev` directives render as attrs, so a meta-exported message carries a bare `export`); `incl_out=True` on the line views appends the message's output the same way.\\n- Structure: `add_msg`, `del_msgs`, `move_msgs`, `split_msg`, `merge_msgs`, `copy_msgs`/`cut_msgs`/`paste_msgs`, `create_dlg`, with session twins `d.move_msgs`, `m.split`, `d.merge_msgs`, `d.copy_msgs`/`d.cut_msgs`/`d.paste_msgs` (session adds go through `d.mk_message`, deletes through `d.remove_msgs`). `add_msg` with neither `before=` nor `after=` places after the current message (`set_cur_msg`, which a host sets and no write ever moves), or at the end when that is unset or gone. `add_msg` and `d.mk_message` take `meta=` and an `export=` shortcut for the meta `nbdev` export flag, readable and assignable as `m.meta_exported` (`m.exported` reads content and meta together, and is what `find_msgs(only_exp=True)` filters on). The `%%add_msg` magic takes its body verbatim: its line is `%%add_msg [dlg] [msg_type] [export] [before=|after=]`, where a bare path token is the dlg and a bare type name the msg_type, and keyword spellings win over bare tokens.\\n- `update_msg(id, ..., dlg=)` / `m.update(...)`: one transaction for a message's attributes. Plain keywords assign (`content=`, `msg_type=`, `output=`, `meta=` replacing wholesale); `mergemeta` deep-merges into `meta` (a `None` value deletes its key); `export` sets the nbdev directive -- `True` adds, `False` removes (there is no negative form), and either migrates a content-form `#| export` line to meta. The function returns a diff of the message's XML plus a `meta:` line, so every change it can make is visible.\\n- The `%nbrun` line magic runs code cells from the current notebook in the running kernel, by cell id prefix, same grammar: bare tokens are flags (`above`, `below`, `all`, `exported`, `ignore_eval`, `continue_on_error`, `show`) or id prefixes, and `fname=` overrides the notebook for one call. Only cells named by id display their output: bulk-selected cells run silently (`show` overrides), errors always surface naming the failed cell, and the run ends with a `nbrun: N cells ok` line. It returns a coroutine (awaited by async-magic machinery) and runs each cell in the kernel's namespace through user-level channels only (exec, `display()`, raising), so it behaves identically in every kernel.\\n- `d.execute(*ids, ...)` / `m.execute()`: the captured twin, on a held `Dialog`. Runs code messages on an execnb `CaptureShell` (fresh and dropped, unless you pass `shell=`), assigns each result to `m.output` in memory (`d.save()` persists), and returns the messages run as a `RunResult` whose repr is a status report: ok lines collapse into a counted marker, failures always show with the exception. It blocks until done, so an async host should wrap it in `asyncio.to_thread`. `above`/`below` anchor at the ids you pass, and bare `d.execute()` runs all participating code messages (the `eval` cascade).\\n- Text edits: `msg_str_replace`, `msg_strs_replace`, `msg_insert_line`, `msg_replace_lines`, `msg_del_lines`, `msg_ast_replace` (all with `re_filter`/line-range powers; `out=True` edits a prompt's reply or a code message's outputs literal), with the same names as `Message` methods for in-memory editing; `lnhashview_msg`/`msg_exhash` (and `m.lnhashview()`/`m.exhash()`) for hash-verified line edits (`lnhashview_msg` is `view_msg(..., lnhashs=True)`; only the exhash pair needs the `exhash` package).\\n\\n## Idiomatic usage\\n\\nStart by registering the notebook: `set_dlg(path)` makes every function here default to it (and the `%nbrun` magic follows suit), so calls read as \\\"do this to message X\\\". Then orient before acting:\\n\\n- `summary_dlg()` first for anything sizable: a cheap one-line-per-message map. `view_dlg()` when you need the full story in order with ids. Read a notebook in full before describing or changing what it does - the interleaved prose, examples, and stored outputs (`incl_out=True`) are the design rationale.\\n- Read before trying things out: an ad-hoc \\\"what happens if...\\\" check usually re-derives, more slowly and less reliably, what an existing example cell already shows. If after reading you still need to try it, that's a gap in the notebook - add it as a proper example cell so the next reader doesn't repeat it.\\n- `find_msgs` is the targeted view: keep the default `context=1` (the neighbouring markdown usually explains the match), and `ids=` with context is the positional query (\\\"does A precede B?\\\", \\\"what's the idiom around here?\\\"). Name questions are structural, not textual: `symdef_finder`/`symref_finder`/`ast_finder` beat regexes over binding syntax. Where available, rgapi's `nbrg` searches cell sources across files and returns the cell ids these functions take.\\n- Edit at the right level: `add_msg`/`%%add_msg` for new messages; within a message, prefer hash-verified addressing (`lnhashview_msg`/`msg_exhash`, or exhash's `lnhashview_cell`/`cell_exhash` by path and cell id) - view with the lnhash variant the moment an edit is plausible, so the view doubles as the edit's address book. The plain `msg_*` editors fit where exhash can't express the edit, e.g. one `msg_str_replace` across an id list. Never splice via `read_nb`/`write_nb` internals: if a primitive is missing here, stop and propose adding it.\\n- `dlg.validate()` catches structural problems early; `dlg.save()` writes back. Don't wrap calls in `hasattr` or `try/except` - call directly and read the bare result.\\n\\nDocs: https://AnswerDotAI.github.io/aidialog/dlgskill.html.md\\n\\\"\\\"\\\"\\n\\n## types:\\n- class FoundMsgs(Found,Msgs,L,…): ... # Find results: live messages indexed by id (exact or unique prefix), never by position; context rows show `-` in place of their first `:`\\n\\n## functions:\\n- def msg_insert_line(id, insert_line: int, new_str: str, *, dlg=None, out: bool = False): ... # Insert new_str at specified line number…\\n- def msg_str_replace(id, old_str: str, new_str: str, *, dlg=None, out: bool = False, **replace_params): ... # Replace occurrence(s) of old_str with new_str…\\n- def msg_strs_replace(id, old_strs: list[str], new_strs: list[str], *, dlg=None, out: bool = False, **replace_params): ... # Replace multiple strings simultaneously…\\n- def msg_replace_lines(id, start_line: int = None, end_line: int = None, new_content: str = '', *, dlg=None, out: bool = False): ... # Replace line range with new content; the defaults replace the entire contents…\\n- def msg_del_lines(id, start_line: int, end_line: int, re_filter: str = None, invert_filter: bool = False, *, dlg=None, out: bool = False): ... # Delete line range; deletion is destructive, so both line numbers must be given explicitly (`1, -1` for all lines)…\\n- def msg_ast_replace(id, repls: list, *, dlg=None, out: bool = False): ... # Apply ast-grep structural pattern replacements to python source…\\n- def set_dlg(fname, cls=None): ... # Set the current dialog file, used by these functions when `dlg` is None…\\n- def cur_dlg(): ... # A fresh `Dialog` read from the current dialog file (None if unset): the session-side entry to the ambient default [9e42]\\n- def set_cur_msg(id): ... # Set the current message, used by `add_msg` when no placement is given…\\n- def cur_msg(): ... # The current message, read fresh from the current dialog file (None if unset, or no longer there) [18ee]\\n- def summary_dlg(dlg=None, maxlen: int = 180): ... # One `preview` per message of the dialog file…\\n- def view_dlg(dlg=None, incl_out: bool = False, only_errors: bool = False, trunc_out: bool = True): ... # The dialog file as concise XML; meta-exported messages carry a bare `export` attr…\\n- def view_msg(id, dlg=None, nums: bool = True, start_line: int = 1, end_line: int = None, lnhashs: bool = False, incl_out: bool = False, trunc_out: bool = True): ... # Show a message's content with optional line numbers or exhash addresses…\\n- def view_msgs(*ids, dlg=None, nums: bool = True, start_line: int = 1, end_line: int = None, lnhashs: bool = False, incl_out: bool = False, trunc_out: bool = True): ... # Show several messages, each preceded by a `# msg ` header…\\n- def find_msgs(re_pattern: str = '', dlg=None, *, msg_type: str = None, only_err: bool = False, only_exp: bool = False, ids='', before: int = 0, after: int = 0, context: int = None, limit: int = None, use_case: bool = False, use_regex: bool = True, headers_only: bool = False, header_section: str = None, pred: callable = None) -> MsgRows: ... # Find messages in the dialog file matching all the given criteria; for live results, call `Dialog.find_msgs`…\\n- def move_msgs(ids, before=None, after=None, dlg=None): ... # Move messages in the dialog file, keeping their relative order; returns them…\\n- def split_msg(id, *linenos: int, dlg=None): ... # Split a message in the dialog file (see `Message.split`)…\\n- def merge_msgs(*ids, dlg=None): ... # Merge messages in the dialog file (see `Dialog.merge_msgs`)…\\n- def copy_msgs(*ids, dlg=None): ... # Copy messages into the paste buffer (replacing its contents), for later `paste_msgs`…\\n- def cut_msgs(*ids, dlg=None): ... # Copy messages into the paste buffer, then remove them from the dialog file…\\n- def paste_msgs(before=None, after=None, dlg=None): ... # Insert copies of the buffered messages (fresh ids) before/after a message or id in the dialog file; returns the new messages…\\n- def symdef_finder(name): ... # `find_msgs` predicate: does the message bind `name` at cell top level? (requires `remold`) [a533]\\n- def symref_finder(name): ... # `find_msgs` predicate: does the message reference `name`? (requires `remold`) [3810]\\n- def ast_finder(pattern): ... # `find_msgs` predicate: does ast-grep `pattern` match in the message? (requires `remold`) [3b44]\\n- def lnhashview_msg(id, dlg=None): ... # Hash-addressed view of a message's content, for `msg_exhash`…\\n- def msg_exhash(id, *cmds: tuple, sw: int = 4, dlg=None): ... # Apply exhash commands to a message in the dialog file, returning the diff…\\n- def add_msg(source: str, msg_type: str = 'code', before: str = None, after: str = None, meta=None, export=False, dlg=None): ... # Add a new message before or after an existing one (default: after the current message, else at the end), returning it…\\n- def del_msgs(*ids, dlg=None): ... # Delete messages by id, returning the removed messages…\\n- def create_dlg(fname: str, source: str = '', msg_type: str = 'code'): ... # Create a new dialog file containing one message, returning the `Dialog` (with `path_` stamped)…\\n- def update_msg(id, dlg=None, *, mergemeta: dict = None, export: bool = None): ... # Update a message's type, meta, or export directive in the dialog file (see `Message.update`)…\\n- def add_msg_magic(line, cell): ... # Add a new message with the magic body as its source, taken verbatim; a bare `export` token sets the meta `nbdev` export flag. [b069]\\n- def nbrun_magic(line): ... # Run code cells from the current notebook (`set_dlg`) by id prefix; bare tokens are flags or id prefixes, `fname=` overrides, a bare line runs all [a932]\\n- def load_ipython_extension(ipython) [52f8]\\n\\n## shared params:\\n- **replace_params (from `msg_str_replace`):\\n start_line: int = None, # Optional 1-based start line to limit search\\n end_line: int = None, # Optional 1-based end line to limit search\\n n_matches: int = None, # Max replacements (None=all)\\n re_filter: str = None, # If provided, only process lines matching this regex (like g// in ex)\\n invert_filter: bool = False, # Invert the filter (like g!// in ex)\\n use_regex: bool = False, # Treat old_str as a regex, and new_str as an `re.sub` template?\\n\\n# module exhash.skill:\\n\\n\\\"\\\"\\\"Universal hash-verified text editing for local files. Use this for one reliable way to read, preview, and change text files.\\n\\nExhash's purpose is to make edits precise and easy to check. First view a file as `lineno|hash|text` (line numbers may be space-padded for alignment); then issue ex-style commands against those exact addresses. Every addressed line's hash is checked immediately before the command runs, so stale context or wrong targets fail instead of editing nearby text. Within one call, a single-line address may match the line's current content or its content at call start, so commands can stack on one line; across calls, re-view. Structural edits still shift lines as they apply, so work *backwards* (bottom-to-top).\\n\\nPrefer exhash over ad hoc patching for text file modifications, and prefer reading with `lnhashview_file` over plain file reads whenever an edit may follow: the view doubles as the address book, so the edit needs no second read.\\n\\nCore APIs:\\n- `lnhashview_file` lists hashed lines.\\n- `exhash` is the in-memory command engine; this docstring is the complete command reference, and `doc(exhash)` adds engine details (strict `s` matching, EditResult fields).\\n- `file_exhash` is the file-aware engine; unqualified addresses use `path` and file-qualified addresses can edit or transfer across files.\\n- `lnhashview_cell` views one notebook cell's source in an `.ipynb` file; `lnhashview_cells` views several explicit cells with `# cell ` headers. `cell_exhash` edits one cell.\\n\\nWorkflow:\\n1. `lnhashview_file(...)`, ending the cell with the bare call: the result displays verbatim, one `lineno|hash|content` line each, so never join, print, or reformat it.\\n2. Copy exact displayed `lineno|hash|` addresses.\\n3. Use tuple command specs; pass each command as its own positional argument, e.g. `file_exhash(path, (addr1, \\\"d\\\"), (addr2, \\\"s\\\", pat, repl))`. Use raw triple-quoted Python strings for address, pattern, replacement, and inserted text when composing commands.\\n4. Use `file_exhash(path, *cmds)` (or `cell_exhash(path, cell_id, *cmds)` for one notebook cell) to apply the edit: both write to disk and return a diff by default. Pass `inplace=False` to preview the result object without touching the file.\\n\\nOnce you hold lnhash addresses, `p` commands are the scattered view: one `(addr, \\\"p\\\")` per address, straight from an `rg` hit or a summary, gives back exactly those lines as verified rows. A `p`-only call writes nothing and returns a bare `lnhashview` of the printed lines (no diff headers, no tag column, no truncation), so its output is an address book that feeds the next edit directly, e.g. `file_exhash(path, (\\\"9|e08c|\\\", \\\"p\\\"), (\\\"18|2f61|\\\", \\\"p\\\"), (\\\"28|3cc8|\\\", \\\"p\\\"))`. `(\\\"%\\\", \\\"p\\\")` views a whole file this way, and `(addr, \\\"g\\\", pat, (\\\"p\\\",))` is scoped grep: a verified row per matching line in the range. Where a call also edits, its printed lines appear in the diff as context rows, always shown even when no hunk is near them.\\n\\nAddressing:\\n Address strings use lnhash addresses: lineno|hash| where hash is a 4-char\\n hex content hash. Use lnhashview to get addresses:\\n lnhashview file.txt show all lines with addresses\\n lnhashview file.txt 10 20 show lines 10-20\\n With multiple commands, hashes are checked immediately before each command runs; a single-line address may also match that line's call-start hash.\\n\\n Single: 12|a3f2|\\n Range: 12|a3f2|,15|b1c3|\\n Last: $ (last line)\\n Whole: % (whole file or cell, same as 1,$; no hashes needed)\\n Special: 0|0000| targets before line 1 (only with a or i)\\n\\nTuple commands:\\n (addr, \\\"s\\\", pat, repl[, flags]) Substitute (Rust regex syntax: backrefs in repl are $1/$0/${name}; a two-char \\\\1 stays literal; a literal $ is written $$, and $name followed by more text needs ${name}text -- an unknown group reference is an error). Flags: g=all, i=case-insensitive. pat/repl are verbatim: literal newlines, slashes, and backslashes all work. For $-heavy replacement text, prefer a c command: its text has no template layer at all.\\n (addr, \\\"d\\\") Delete line(s)\\n (addr, \\\"a\\\", text) Append text after line\\n (addr, \\\"i\\\", text) Insert text before line\\n (addr, \\\"c\\\", text) Change/replace with text\\n (addr, \\\"j\\\") Join with next line; with range, joins all lines in range\\n (addr, \\\"m\\\", dest) Move line(s) after dest address\\n (addr, \\\"t\\\", dest) Copy line(s) after dest address\\n (addr, \\\">\\\", n) Indent n levels (default 1, 4 spaces each)\\n (addr, \\\"<\\\", n) Dedent n levels (default 1)\\n (addr, \\\"sort\\\") Sort lines alphabetically\\n (addr, \\\"p\\\") View line(s): emit them as verified `lineno|hash|` rows, changing nothing\\n (addr, \\\"g\\\", pat, sub), (addr, \\\"g!\\\", pat, sub), (addr, \\\"v\\\", pat, sub) Global commands: apply `sub` to each addressed line matching `pat` (`g!`/`v`: not matching). `sub` is a subcommand tuple without an address, e.g. (\\\"d\\\",) or (\\\"s\\\", \\\"foo\\\", \\\"bar\\\", \\\"g\\\"); globals cannot nest.\\n (addr, \\\"y\\\", source, dest) Transliterate `source` chars to `dest` (equal counts required)\\n\\n\\nCut/copy/paste between files and notebook cells:\\nAny `m` (cut+paste) or `t` (copy+paste) address can carry a target prefix: `path:` for another file, or `path.ipynb:cellid:` for one cell's source (`cellid` exact or unique prefix). This is THE way to transfer existing lines between locations: the lines never pass through your output, so hard-to-retype content (long encoded strings, hashes, long literals) cannot be mistyped. Take source addresses from `lnhashview_file`/`lnhashview_cell` of each target as usual:\\n\\n file_exhash(path, (\\\"src/a.py:10|aaaa|,20|bbbb|\\\", \\\"m\\\", \\\"src/b.py:$\\\")) # cut a.py lines 10-20, paste at end of b.py\\n file_exhash(path, (\\\"nb.ipynb:ab12cd34:6|830e|\\\", \\\"t\\\", \\\"other.ipynb:9f8e:$\\\")) # copy one cell line into another notebook's cell\\n file_exhash(path, (\\\"nb.ipynb:ab12cd34:%\\\", \\\"t\\\", \\\"snippets.py:0|0000|\\\")) # copy a whole cell's source into a new file\\n\\nA range must stay within one file or cell, and cells are never created by a transfer (files can be, via a `0|0000|` destination). For whole-cell structural operations (add/delete/move cells) use `pyskills.ipynb`'s `copy_cells`/`paste_cells` instead; this idiom is for line-level transfer.\\n\\nReformatting a section like `gq`, plus optional indents: `j` the range onto one line, re-view, then split with ONE g-flagged `s` alternating the tokens (picked by eye) that should start each new line, captured and restored with the break and indent in the replacement: `(addr, \\\"s\\\", r\\\", ('foo'|'bar'|'baz')\\\", \\\",\\\\n $1\\\", \\\"g\\\")`\\nImportant:\\nDo not pass raw commands to Python APIs. Do not create addresses by text search or remembered line numbers, and never construct them by computing hashes (e.g. via `line_hash`): addresses come only from a fresh view immediately before the edit. On stale hash, re-view and rebuild. Where rgapi is installed, hits from `rg(pattern, lnhashs=True)` count as fresh views too: their addresses drop straight into commands (and `nbrg` finds the cell ids that `lnhashview_cell` takes). If reaching an address seems to need arithmetic, hand-copying, or a guessed hash, a step on that route was skipped. Tuple text fields can contain newlines wherever the command accepts text. For example, `(addr, \\\"s\\\", \\\"foo\\\", \\\"bar\\\\nbaz\\\")` replaces one line with two. Text fields are taken verbatim: a two-character `\\\\n` sequence stays literal; use an actual newline when you want a line break. For `a`/`i`/`c`, put all text in one tuple field: `\\\"first\\\\nsecond\\\"` starts with `first`, while `\\\"\\\\nfirst\\\"` inserts a leading blank line before `first`. For moving/copying between files or cells, use the qualified `m`/`t` addresses shown above. Missing files can only be created through `(r\\\"0|0000|\\\", \\\"a\\\", text)` or `(r\\\"0|0000|\\\", \\\"i\\\", text)` creation semantics.\\n\\nThe `%%exhash` cell magic:\\nIn IPython sessions, importing this module registers the `%%exhash` cell magic: `%%exhash []
` applies one command whose text is everything below the magic line, taken verbatim (one trailing newline stripped). Passing `` targets that cell in an .ipynb file instead of a plain file (`cell_exhash`); the magic dispatches on token count, so no separate cell magic exists. Because the text is never parsed as Python, no quoting or escaping applies. Use it for EVERY `a`/`i`/`c` command, however simple the text looks: create a file with `%%exhash path 0|0000| a`; replace a whole cell or file with `%%exhash [] % c` (`%` needs no hashes: a full replace has no neighboring lines to mis-hit); replace a region within one with a range address and `c` (`%%exhash 12|a3f2|,15|b1c3| c`), both addresses straight from the one pre-edit view. Tuple `a`/`i`/`c` text fields are only for contexts without magics (scripts, tests): interactively they add a Python quoting layer whose failure modes are not reliably foreseeable, so do not use them.\\nIPython expands `{expr}` and `$var` in the magic line from the user namespace (its standard `var_expand` for all magics), so a path or cell id held in a variable needs no retyping: `%%exhash {path} {cid} % c`. Only the line expands; the text stays verbatim.\\n\\nDocument outlines (`open_doc`):\\nThe outline layer makes exhash a hierarchical editor: `open_doc` opens a file (`fname=`, or a `Path` as `src`; recorded for `refresh()` and edits), a URL (an `https?://` str, fetched), or any other str as text in hand, and returns a `Section` tree. Markdown sections come from headings; code files (py, js, ts, tsx, rs, zig, swift) from tree-sitter definitions; `.ipynb` files from md-heading cells over cells. Display the root bare: fixed-width rows `token title (count) [size] preview` (code rows have no title: the def line, signature included, opens the preview), previews ¶-joined with links rendered `[text][n]` so no URL spends tokens. The token is the verified address: `1.2.|12|a3f2|,45|b1c3|` fuses the dotted addr (trailing dot; the root's is `.`) with the section's boundary lnhash pair, so one row serves navigation, editing, and orientation: `at()` verifies the first hash, and the pair drops straight into a `file_exhash` range command. `at()` accepts ONLY tokens copied from a listing (`'1.6.|12|a3f2|'` or the full range form); bare dotted addrs raise, teaching the idiom. Live traversal is free: `d[1][6]`, `find(title)`, `search(pat)` (counts and matching-line previews), `paths(depth)`, `links(pat)`, and `open(n)` opens link `n` as a new tree with `base` recorded (a non-md target comes back as a leaf whose `.src` holds its text). `view()` is the reading view: rendered text with links `[text][n]` (`.src` is the raw source), displayed verbatim bare; `view(*tokens)` reads several sections in one call, each under a `# token` header when more than one, so a listing's tokens replace a print loop; `nums=`/`lnhashs=` switch any view to stored lines with edit-ready addresses. Notebook section tokens carry the heading cell id (`1.2.|ab12cd34|8f3a|`), and `view(lnhashs=True)` rows are `cellid:lineno|hash|`, ready for `cell_exhash`. The whole llms.txt docs workflow: `toc = open_doc(url)`; `toc.links(topic)`; `page = toc.open(n)`; display `page` bare; `page.view()` whole when small, else `page.search(topic)` and `page.view(*tokens)`.\\n\\\"\\\"\\\"\\n\\n## types:\\n- class Section(dict): ... # One document section: numbered child sections, source span, and a verified address\\n- class Sections(list): ... # Sections listed as fixed-width `token title (count) [size] preview` rows; each row is the live node. Code rows have no title: the def line opens the preview\\n- class Link: ... # One inline link, numbered in document reading order\\n- class Links(list): ... # A list of `Link` rows, displayed one per line\\n\\n## functions:\\n- def line_hash(line: str) -> str: ... # Return a 4-char lowercase hex hash for a single line of text. [3ea3]\\n- def lnhash(lineno: int, line: str) -> str: ... # Return an lnhash address ``lineno|hash|`` for ``line`` at 1-based ``lineno``. [f9d5]\\n- def lnhashview(text: str, start: int = None, end: int = None) -> LnhashView: ... # Return lines formatted as space-padded ``lineno|hash|content``. Optional 1-based ``start``/``end`` filter the range; ``end`` past EOF is clamped. [eb78]\\n- def lnhashview_file(path: str, start: int = None, end: int = None) -> LnhashView: ... # Return lines formatted as space-padded ``lineno|hash|content`` for file at ``path`` (expands ``~``). Optional 1-based ``start``/``end`` filter the range; ``end`` past EOF is clamped. [52c2]\\n- def lnhashview_cell(path: str, cell_id: str, start: int = None, end: int = None) -> LnhashView: ... # Return lines formatted as ``lineno|hash|content`` for the source of notebook cell ``cell_id`` in ipynb file at ``path`` (expands ``~``). ``cell_id`` may be an exact id or unique prefix; optional 1-based ``start``/``end`` filter the range. [08f8]\\n- def lnhashview_cells(path: str, *cell_ids: str, start: int = None, end: int = None) -> LnhashView: ... # Return grouped lnhash views for explicit notebook cell ids in the ipynb file at ``path`` (expands ``~``). Each group starts with ``# cell ``; following lines keep normal ``lineno|hash|content`` format. [b6bd]\\n- def exhash(text: str, cmds: list[tuple], sw: int = 4): ... # Verified line-addressed editor. Apply commands to `text`, return an EditResult.…\\n- def file_exhash(path: str, *cmds: tuple, sw: int = 4, inplace: bool = True): ... # Read files and notebook cells, apply file-aware exhash commands, and return per-target results or a combined diff.…\\n- def cell_exhash(path: str, cell_id: str, *cmds: tuple, sw: int = 4, inplace: bool = True): ... # Apply exhash commands to the source of notebook cell ``cell_id`` in ipynb file at ``path`` (expands ``~``).…\\n- def open_doc(src: str | Path = None, rm_fenced=True, fname: str = None): ... # Open a document as a `Section` tree: a file (`fname`, or a `Path` in `src`; recorded for `refresh` and edits), a URL (fetched), or text…\\n\\n# module rgapi.skill:\\n\\n\\\"\\\"\\\"Fast and flexible file finding and search for Python. Use this when code needs `fd`-style file finding or `rg`-style searching.\\n\\nrgapi wraps the same `ignore`, `grep-regex`, and `grep-searcher` crates ripgrep uses, so `.gitignore`/`.ignore`/`.rgignore`, dot-file handling, glob/ext filters, and regex matching all behave like `rg`. Walking and searching run in parallel and most work stays in Rust, so results come back as structured Python objects instead of CLI text to parse. Prefer rgapi over shelling out to `rg`/`fd` or scanning files by hand: you get typed rows, byte-offset match spans, and lazy iteration.\\n\\nCore APIs:\\n- `fd(root=\\\".\\\", ...)` finds paths with fd-style filters (`pattern` smart-case basename regex, `include`/`exclude`/`glob`, `ext`); returns slash-separated relative paths as `FileEntry` rows: `str` subclasses that lazily stat themselves for `size`/`mtime`/`is_dir`/`stat`. The list displays as an `ls -l`-style table (capped at `rgapi.MAX_REPR` rows); `str(res)` or `list(res)` give plain paths. Unfollowed symlinks are included, displaying with an `l` mode character; `show_target=True` appends `-> target` to their rows, and `link_target` holds it (`None` for non-links).\\n- `ls(root=\\\".\\\", ...)` lists like the shell `ls`: one level, directories included, ignore rules off, sorted by name. It is `fd` with different defaults, so every `fd` filter works; `hidden=True` is `ls -a`.\\n- `rg(pattern, root=\\\".\\\", ...)` returns matching `SearchLine` rows. `summary=True` instead returns blank-line-delimited `SearchBlock` rows, with newline runs displayed as `¶` and `maxlen` source characters shown per block. Context is line-based normally and block-based in summary mode. `paths=True` returns unique paths, `count=True` returns a match-span total, and `lnhashs=True` shows exhash addresses. `summary=True` is incompatible with `paths` and `count`, but combines with `lnhashs` to show copyable block boundaries.\\n- `nbrg(pattern, root=\\\".\\\", cell_context=0, maxlen=180, ...)` searches Jupyter `.ipynb` files (cell source only) and returns matched cells as `NbResults`/`NbCell`; `paths=True` and `count=True` reduce like `rg`'s. Its display is always a one-line cell summary. Use this for notebooks rather than `rg`, to avoid escaped JSON and get stable cell ids. `multiline=True` lets a pattern match across lines within a cell (line-oriented `rg` rejects `\\n` in patterns); `^`/`$` still match at line boundaries.\\n- `rgstr(pattern, text, ...)` searches a string already in hand (a fetched log, a captured output, a doc): `rg`-style `SearchLine` rows and context from in-memory text, with no path label and no separate `compile` step. Reach for it instead of ad hoc `splitlines()` loops over held text.\\n\\nIdiomatic usage:\\n- Results display through tuned reprs designed to be read as-is: end the cell with the bare call and read what comes back - never join, slice, or otherwise reformat a result by hand. If a result is too big to take in full, narrow it with the function's own parameters (`max_results`, `paths=True`, `count=True`, tighter filters), not by post-processing the output.\\n- For orientation, start with `rg(pattern, summary=True)` and drop to line-level rows only where needed; add `lnhashs=True` when an edit may follow, so hits arrive with copyable addresses. This applies beyond unfamiliar code: on prose, config, and markdown, a summary row is the whole matched paragraph, usually exactly the read you wanted.\\n- In kernel sessions, reach for `ls()` rather than shell `ls`, and `fd()` rather than shell `find`, for listings you or later calls will read.\\n\\nSearchLine rows:\\n kind 'match', 'before', 'after', or 'context'\\n path path relative to root\\n line_number 1-based line number\\n lnhash exhash-style `lineno|hash|` address\\n line line text without the trailing newline\\n matches list of (start, end) byte offsets, for 'match' rows\\n asdict() returns the row fields as a plain dict\\n\\nSearchBlock rows (from `rg(summary=True)`):\\n path/block_index/start_line/end_line/start_lnhash/end_lnhash locate the block\\n kind 'match' or 'context'\\n source full block source\\n matches list of matching SearchLine rows within the block\\n asdict() returns the row fields as a plain dict\\nOutput uses `path:start-end:source` for matches and `path:start-end-source` for context. With `lnhashs=True`, `start-end` becomes `start_lnhash,end_lnhash` (or one hash for a one-line block). Empty or whitespace-only lines delimit blocks; `context=N` adds N neighbouring blocks. Multiple matches in one block produce one row.\\n\\nNbCell rows (from `nbrg`):\\n path/cell_index/cell_id/cell_type locate the cell ('code'/'markdown'/'raw')\\n kind 'match' or 'context'\\n source full cell source\\n matches list of SearchLine rows for the matched lines within the cell\\n asdict() returns the cell fields as a plain dict\\nOutput is keyed by `cell_id` (the nbformat cell/message id), not line number: `path:cell_id:source` for matches and `path:cell_id-source` for context. A match row starts at its first matched line: earlier lines display as `…[Ln]` (`n` the matched line's 1-based number in the cell), except that a leading `#|` directive line is kept, e.g. `#| export…[L4]needle here`. Newline runs display as `¶` (following indentation kept) and `maxlen` limits displayed source without changing `source`. `cell_context=N` adds N neighbouring cells. Walking, parsing, and matching run in parallel in Rust; outputs and metadata are skipped.\\n\\nImportant:\\nTraversal is parallel and result order is NOT guaranteed; wrap in `sorted(...)` if you need stable order. `path_re`/`skip_path_re` filter the returned/searched paths but do not prune traversal; use `skip_dir`/`skip_dir_re` to prune whole subtrees for speed. Run `doc(func)` for full parameter docments.\\n\\\"\\\"\\\"\\n\\n## types:\\n- class RgIter: ... # Initialize self. See help(type(self)) for accurate signature.\\n\\n## functions:\\n- def fd(root: str | Path = '.', pattern: str | NoneType = None, files: bool = True, dirs: bool = False, show_target: bool = False, timeout_ms: int | NoneType = None, *, ext: str | list | NoneType = None, ignore: bool = True, max_depth: int | NoneType = None, **walk_params) -> PathResults: ... # Find paths with fd-style filters and gitignore support.…\\n- def ls(root: str | Path = '.', pattern: str | NoneType = None, dirs: bool = True, max_depth: int | NoneType = 1, ignore: bool = False, *, files: bool = True, show_target: bool = False, timeout_ms: int | NoneType = None, ext: str | list | NoneType = None, **walk_params) -> PathResults: ... # List a directory like `ls`: one level, directories included, ignore rules off, sorted by name.…\\n- def rg(pattern: str, root: str | Path = '.', case_sensitive: bool | NoneType = None, smart_case: bool = False, before_context: int = 0, after_context: int = 0, context: int = 0, paths: bool = False, count: bool = False, max_results: int | NoneType = None, lnhashs: bool = False, timeout_ms: int | NoneType = None, summary: bool = False, maxlen: int = 180, *, ext: str | list | NoneType = None, ignore: bool = True, max_depth: int | NoneType = None, **walk_params): ... # Search files and return `SearchResults`, matched paths, or a count; `lnhashs=True` shows exhash-style addresses.…\\n- def rg_iter(pattern: str, root: str | Path = '.', case_sensitive: bool | NoneType = None, smart_case: bool = False, before_context: int = 0, after_context: int = 0, context: int = 0, lnhashs: bool = False, *, ext: str | list | NoneType = None, ignore: bool = True, max_depth: int | NoneType = None, **walk_params) -> RgIter: ... # Search files lazily, yielding `SearchLine` rows; `lnhashs=True` shows exhash-style addresses.…\\n- def nbrg(pattern: str, root: str | Path = '.', cell_context: int = 0, case_sensitive: bool | NoneType = None, smart_case: bool = False, paths: bool = False, multiline: bool = False, max_results: int | NoneType = None, count: bool = False, timeout_ms: int | NoneType = None, maxlen: int = 180, *, ignore: bool = True, max_depth: int | NoneType = None, **walk_params): ... # Search `.ipynb` cell sources under `root` in parallel, returning matched cells, paths, or a count.…\\n- def rgstr(pattern: str, text: str, case_sensitive: bool | NoneType = None, smart_case: bool = False, *, before_context: int = 0, after_context: int = 0, context: int = 0) -> SearchResults: ... # Search text already in hand, like `rg` on a string: no path label, no separate `compile`.…\\n\\n## shared params:\\n- **walk_params (from `fd`):\\n glob: str | list | NoneType = None, # Include glob or globs; alias for `include`\\n include: str | list | NoneType = None, # Include glob or globs, e.g. `*.py`\\n exclude: str | list | NoneType = None, # Exclude glob or globs, e.g. `test_*.py`\\n hidden: bool = False, # Include hidden files and directories\\n min_depth: int | NoneType = None, # Minimum depth for returned/searched paths\\n max_filesize: int | NoneType = None, # Skip files larger than this many bytes\\n follow_links: bool = False, # Follow symbolic links while walking\\n same_file_system: bool = False, # Do not cross filesystem boundaries\\n path_re: str | NoneType = None, # Regex that relative paths must match\\n skip_path_re: str | NoneType = None, # Regex for relative paths to skip\\n skip_dir: str | list | NoneType = None, # Directory glob or globs to prune\\n skip_dir_re: str | NoneType = None, # Directory regex used to prune traversal\"\n", - "}\n", - "```\n", - "\n", - "````json {.tool}\n", - "{\n", - " \"id\": \"call_d41f97015d13d9bc\",\n", - " \"name\": \"mcp__clikernel__execute\",\n", - " \"args\": {\n", - " \"code\": \"doc(acp)\"\n", - " },\n", - " \"result\": \"# module aai_coding.coding_patterns:\\n\\n\\\"\\\"\\\"Jeremy's coding style and conventions: read before writing, reviewing, or assessing any code.\\n\\n# Coding Patterns\\n\\nBased on the fastai style guide and Jeremy's 3 decades of coding experience, and used across all of his projects, not only fastai ones.\\n\\nUse the fastcore/fasthtml ecosystem (fastcore, fasthtml, fastlite, ...) when picking libraries. These are Jeremy's own, editable-installed as siblings, and preferred over heavier third-party alternatives.\\n\\n## Improving Tooling Pays Off Exponentially\\n\\nMaking our tools marvellous matters more than the task in hand. A finished task helps once. A better tool helps every later task, every later session, and the whole team. Ergonomics count as much as capability. Most code you have read was written by people who put up with tool friction rather than fix it, so your default is the workaround. Here the tools are ours and one edit away. When something grates, fix it or raise it. Never quietly work around it.\\n\\n## Every Construct Must Earn Its Place\\n\\nReaders assume everything present is necessary. When they see `str(x)` on something that's already a str, they stop and wonder what subtle thing it's guarding against. When the answer is \\\"nothing\\\", they paid for a mystery with no payoff. The same goes for defensive copies (`list(x)` that's never mutated), \\\"just in case\\\" try/excepts, redundant type coercions, and unused parameters. Before adding any construct, know why it's needed. If you can't say, leave it out. The cost doubles in nbdev projects, where tests are documentation.\\n\\nKnowing why a construct is needed does not mean waiting for someone to ask for it. An open source user who finds a gap concludes the library cannot do it and moves on. Judge a library feature by whether the design calls for it and users would expect it.\\n\\nThe same applies to prose in code: almost never add comments (only when the code is truly unclear), and don't add type hints, docstrings, or boilerplate that pull no weight. Prefer concise, readable code over verbose \\\"enterprise\\\" style.\\nOnly write a code comment to state a constraint the code itself can't show, never to say where it came from, what the next line does, or why your change is correct. That's you talking to the reviewer, not the next reader, and it's noise the moment the PR merges.\\n\\n## Plain Commands: Flags, Redirects, and Pipes Are for Exceptions\\n\\nThe same principle applies to running commands. The bare invocation is the contract: `pytest tests -q`, `cargo fmt`, `maturin develop`. Project config exists precisely so the plain command does the right thing, and then every invocation is short, identical, and instantly readable. A flag, redirect, or pipe is a statement (\\\"this one call has a requirement the defaults don't cover\\\"), and used that way it carries information: the reader stops, asks why, and there's an answer. Sprinkled defensively on every call, decorations destroy that signal: every command looks exceptional so none is, and the one deliberate flag is indistinguishable from habit. Transcripts compound the damage, since each decorated call teaches later calls to decorate too.\\n\\n- Wanting the same flag on every run means it's a missing config line: promote it and go back to the bare command (`pytest --timeout 300` on every run becomes `timeout = 60` under `[tool.pytest.ini_options]`).\\n- Don't check-then-apply when applying is the goal: `cargo fmt`, not `cargo fmt --check` followed by `cargo fmt`. `--check` is for a final no-mutation verification, e.g. CI.\\n- Run commands bare and read their output. Never pipe through truncating filters (`| tail -20`, `| grep PASS`): truncation is decided before the output exists, and it hides exactly the surprises worth seeing. Never merge stderr into stdout with `2>&1`: separated, a crash is unmissable.\\n- On the rare occasion output genuinely can't come back inline (far too large), redirect the streams separately (`>meta/stdout.txt 2>meta/stderr.txt`) and read the files from the kernel.\\n- A real one-off requirement gets its flag once, with the reason stated alongside, and disappears again on the next call.\\n\\n## Docments\\n\\nDocments are trailing comments on function parameters that fastcore uses for documentation. A signature with docments (or any long signature) uses this layout: `(` stays on the def line, each parameter on its own line indented 4, and `):` alone on its own line at def indent:\\n\\n```python\\n@delegates(start_kernel)\\nasync def run_kernel(\\n kernel_name='python3', # Kernelspec name to launch\\n manager_cls=KernelManager, # Manager class, e.g. a subclass customizing launch\\n **kwargs\\n):\\n```\\n\\nNEVER remove docments when refactoring. They're essential documentation.\\n\\nWhen `**kwargs` passes through to a known callee, decorate with `@delegates(callee)` so the signature shows the real options. delegates REQUIRES the collector to be named `kwargs`, not `kw` etc. Skip the decorator when the callee's own signature is just `**kwargs` (nothing to delegate).\\n\\n## Raw Strings\\n\\nWrite any non-trivial string literal as a raw string (`r\\\"...\\\"` / `r\\\"\\\"\\\"...\\\"\\\"\\\"`): regexes, text you pass to tools, code or markup inside strings, anything multi-line or containing backslashes. In plain strings a stray `\\\\n` or `\\\\d` either errors or silently corrupts, and each miss costs a round trip to diagnose plus another to fix. Raw strings are WYSIWYG, so the first attempt matches what you meant. The `r` costs nothing when no escapes are present, so make it the default, not the exception.\\n\\n## Style Checker (chkstyle)\\n\\nRun `chkstyle {path}` to check fastai style (only include path if needed). But use judgment. chkstyle is a hint, not gospel.\\nIn nbdev projects, point it at the notebooks (`chkstyle nbs/00_core.ipynb`), not the exported `.py`. The notebook run also checks example and test cells, which never reach the module.\\n\\n## Config Patterns\\n\\nRead from standard locations rather than duplicating config:\\n\\n- GitHub release notes config: `.github/release.yml`\\n- Project config: `pyproject.toml` under `[tool.yourpkg]`\\n- Infer values when possible (e.g., package name from `[project].name`)\\n- Bundle data files inside the package and read them with `importlib.resources`\\n\\n## Versioning: Bump After Release\\n\\nJeremy bumps the version immediately after each release, as part of releasing and never as part of a change. The tree therefore always carries the next release's version. A sibling dep pin can name a version before it ships (`foo>=`).\\n\\nA downstream pin is part of the change that creates the dependency. When a change makes one package consume another's new behavior or API, stamp the consumers' pins in the same session. A pin deferred to release time is a forgotten pin.\\n\\nThis convention is for Python projects. Other artifact types version at change time instead: for example, the Claude Code plugins in skill-plugins bump automatically when `./build.py` regenerates a changed output.\\n\\n## Project Layout\\n\\nPrefer flat layout over src/:\\n\\n```\\nmyproject/\\n├── mypkg/\\n│ └── __init__.py\\n├── tests/\\n├── pyproject.toml\\n└── README.md\\n```\\n\\n## Testing\\n\\nAll code has writing, maintenance, and readability costs, and tests most of all: every test must be kept passing forever, gets read by every future contributor, and must be revised whenever the behavior it pins changes. So never write a test as a reflex, and don't aim for anywhere near 100% coverage. A test earns its place only when:\\n\\n- it documents an idea: in nbdev notebooks, tests ARE the documentation (see `doc(nbdev.skill)`), or\\n- the logic is intricate enough that you had to think carefully to get it right (edge cases, parsing, arithmetic, tricky conditionals: the places a future change could silently break it), or\\n- the code assumes something about an external system (a file format, an API's response shape, another tool's behavior) that is somewhat likely to change one day, and we want to hear about it when the assumption is violated. These must exercise the real thing (a mock merely re-states our assumption), so they're usually the slow-marked tests\\n\\nWiring and orchestration get zero tests: re-exports, delegations, one-line glue, functions that only sequence calls to other tools. A test there only asserts that Python works, and pins down internals we may want to change. Strong tell: if a test needs recording fakes or mock collaborators to reach the code, it's testing a transcript of the implementation, not logic. Extract the logic into a small pure function and test that, or don't test at all.\\n\\nIF you add a pytest test, ALWAYS work red-green: write it FIRST, run it to see it fail, THEN make the change, then run it again to see it pass. In nbdev projects there are no test cells - see `doc(nbdev.skill)`: changes revise lesson cells, and the red-green check applies only to an assertion you actually revised or added.\\n\\n- Prefer as few tests as possible: a single test that walks through many checks is more readable and faster than many small ones\\n- A check worth keeping goes in a real test file or notebook cell, never left as an ad-hoc command. In a notebook, the checks made while exploring often ARE the narrative (each one both documents what we needed to know and keeps guarding it), so they stay as example cells. In a pytest file, an exploratory check survives only if it meets one of the criteria above\\n- Assert the logic, not incidentals: check what the behavior guarantees, never byte-exact renderings, exact reprs, or field order. A test that compares a whole output string locks in formatting decisions that were never the point (e.g. assert the content appears in a markdown display block, not the display's exact text). NEVER use tests to \\\"lock in\\\" behavior, unless that exact behavior really is a key part of the logic or contract that must always be true forever\\n- Use `pytest -q` (not `python -m pytest`, which prompts for permission). nbdev projects use `nbdev-test` on the changed notebook, but some notebooks are slow or hit live services, so check with the user before running one you don't know is safe (known safe: all of pyskills)\\n- Don't run slow-marked tests until finishing a session, or after a change likely to directly impact them\\n\\n## One-liner Patterns\\n\\n```python\\n# Conditionals\\nif not x: return default\\nif x and y: do_thing()\\n\\n# Try/except\\ntry: return run(\\\"cmd\\\").strip() or default\\nexcept Exception: return default\\n\\n# Loops\\nfor i in range(n): result[i] = 0\\nwhile len(items) < 3: items.append(0)\\n```\\n\\n## Import Style\\n\\nCombine imports on single lines:\\n\\n```python\\nimport os, re, sys, shutil\\nfrom pathlib import Path\\nfrom fastcore.utils import *\\n```\\n\\n`from fastcore.utils import *` already provides `os`, `Path`, and much of the stdlib, so don't re-import those alongside it.\\n\\\"\\\"\\\"\"\n", - "}\n", - "````\n", - "\n", - "```json {.tool}\n", - "{\n", - " \"id\": \"toolu_01YPiAitbgdQ2HkRQYJSruHV\",\n", - " \"name\": \"mcp__clikernel__execute\",\n", - " \"args\": {\n", - " \"code\": \"list_pyskills()\"\n", - " },\n", - " \"result\": \"{'nbdev.skill': 'Author clear, executable nbdev notebooks where code, prose, examples, outputs, and tests form one coherent narrative.',\\n 'dialoghelper.solveitskill': 'Read, search, edit, and manage Solveit dialogs using dialoghelper.core, including dialog/message addressing, line-numbered inspection, targeted message edits, add/update/delete/copy/paste workflows, and safe editing patterns.',\\n 'tracefunc.skill': 'See what a Python function did, line by line: how many times each line ran and what the variables held. Use when working out why code takes a branch, loops, recurses, or computes a wrong value, without changing that code or stepping through a debugger.',\\n 'rgapi.skill': 'Fast and flexible file finding and search for Python. Use this when code needs `fd`-style file finding or `rg`-style searching.',\\n 'ghapi.skill': 'GitHub REST API access via `GhApi`, plus local git operations via `fastgit.Git`. Use this for day-to-day GitHub work: reading/creating issues and PRs, checking CI status, managing releases/branches/gists, and repo-local git operations -- all from Python, no shelling out to `gh`/`git` needed.',\\n 'fastanki.skill': 'Anki flashcard tools for spaced repetition. Direct sqlite and AnkiWeb sync, with no Anki app needed.',\\n 'remold': \\\"Structural search and rewrite for Python source: declarative ast-grep pattern rules, LibCST matcher transforms for everything patterns can't express, and tree-based symbol queries. Use this to edit code by structure (rename calls, move methods, rewrite APIs) where regexes break and `ast` loses comments.\\\",\\n 'jph': \\\"Jeremy's tools, which for now is just his notes system. Use if Jeremy says 'create a note', or 'check my notes' or similar.\\\",\\n 'cordslite.skill': 'Read-only access to Discord through cordslite. Covers connecting, opening a guild, finding your way around its channels, searching messages, reading threads, and fetching attachments.',\\n 'fastmux.skill': 'Work with tmux sessions, windows, and panes from Python, plus named background sessions keyed by sid. Use this when code needs to read what is on a terminal, send input to an interactive program, build pane layouts, search text across terminals, or keep a terminal open that you and the user can both see.',\\n 'exhash.skill': 'Universal hash-verified text editing for local files. Use this for one reliable way to read, preview, and change text files.',\\n 'clikernel.skill': 'Use the `clikernel` MCP session as the default workspace for Python work: reading and changing files, notebook work, trying things out, checking how a library behaves, and reshaping data. One session stays open, so imports and variables carry between calls. Read this before writing, running, or debugging Python code in a session with `clikernel` connected.',\\n 'fastcdp.skill': 'Work with Chrome through the DevTools Protocol: open pages, click and type, read a page as an accessibility tree, keep a log of console and network activity for debugging, and call any CDP command.',\\n 'aai_coding.coding_patterns': \\\"Jeremy's coding style and conventions: read before writing, reviewing, or assessing any code.\\\",\\n 'aai_coding.harness_docs': 'Official docs for the tool you are running in (Claude Code or codex): read before answering questions about its behaviour, config, or features.',\\n 'aai_coding.looker': 'Answer questions about images and PDFs by asking a separate codex process to look at them, which keeps large files out of this session (macOS only). Use it to verify rendered output: PDFs, screenshots, captured windows.',\\n 'aai_coding.theory': 'What \\\"Theory\\\" (capital T) means in this team\\\\'s vocabulary: Peter Naur\\\\'s programming-as-theory-building. Read when the user says the Theory of something, or asks to discuss, find, or capture a Theory.',\\n 'aai_coding.write_docs': 'How to write reference prose: read before writing docstrings, READMEs, API docs, PR descriptions, commit messages, or messages to co-workers.',\\n 'aai_coding.write_prose': \\\"How to write narrative prose that reads as though a person wrote it: read before writing blog posts, essays, announcements, or anything else with the author's voice. write-docs covers reference prose.\\\",\\n 'aai_coding.write_summary': 'How to write summaries that stand alone: read before summarizing a conversation, meeting, PR, paper, or repo change for a human reader.',\\n 'fastcore.editskill': 'Text, file, cell, and notebook editing from `fastcore.tools` and `fastcore.nbio`, plus the conventions the whole fastai editing toolkit follows. Read this before working with the editing tools in any package that shares them.',\\n 'pyskills.skill': 'Pyskills are tool modules that Python packages register so you can find them without importing everything. `list_pyskills()` names the ones installed, with a one-line description each, and needs no imports. Load one with a normal import, then read its docs with `doc()`.',\\n 'llmsurgery.skill': 'Read and work with Claude Code and Codex session transcripts.',\\n 'aidialog.dlgskill': \\\"Read, search, and edit dialogs and notebooks through aidialog's `Dialog` and `Message` classes\\\",\\n 'fastcflare.skill': 'Cloudflare API access via `CloudflareApi`: zones, DNS records, caching, and every other Cloudflare v4 feature from Python. Use this for day-to-day Cloudflare work instead of hand-built httpx calls against api.cloudflare.com.',\\n 'test.skill': 'A test skill.'}\"\n", - "}\n", - "```\n", - "\n", - "```json {.tool}\n", - "{\n", - " \"id\": \"toolu_01XJJ5Yx66nUxRm3WchRqP14\",\n", - " \"name\": \"mcp__clikernel__execute\",\n", - " \"args\": {\n", - " \"code\": \"dojo_start()\"\n", - " },\n", - " \"result\": \"== llmdojo ==\\nAssumed knowledge: the toolkit conventions taught by the startup doc() calls, doc(clik, pysk, edsk), doc(dsk, exh, rgsk), and doc(acp). Do not continue unless that output is visible in your context; if any is missing, read it now.\\nWork only in: /tmp/dojo\\n%cd there first: chdir is never penalized, because the relative paths it enables make every later cell shorter - path tokens you never repeat are tokens saved, in the dojo and in real work.\\nScoring: kernel cell = 1 stroke; Bash tool call = 2; each print() call = +1. The tooling's reprs are designed to be optimally useful read bare, so end each cell with a bare expression and read what comes back. `doc()` returns a `PrettyString`: leave it as the final expression when you want to see it, or assign it when you do not want it rendered, for instance for very large docs you want to search through. Cells of only doc()/list_pyskills()/imports are free (bare calls, NOT wrapped in print()), as are comment-only narration cells and chdir cells (%cd / os.chdir).\\nPenalties: +1 per skill module or workspace function used before doc()ing it.\\nPar assumes the tooling's best route, shown with each kata at scoring: matching par means you found it.\\nWhy the round matters: the rest of this session copies whatever this transcript shows, so one round of correct tooling use improves everything after it. Falling back to sed, cat, or Path.read_text() teaches the opposite, and we find those fallbacks go with weaker, less creative work across the whole task.\\nPer-kata scoring: before starting a kata, run a cell containing ONLY a comment in this exact format: '# kata 3'. Cells after it count toward that kata until the next tag cell; tag cells are free. Anything fancier is narration, not a tag.\\nPar for the round: 8. When done: dojo_score(bash_calls=)\\nThe round is complete ONLY on a clean score: par or better, every kata ok, no penalties. Until then do no work outside the dojo; redo over-par katas with dojo_redo, in ascending order. Scoring pauses the ledger: dojo_redo (or dojo_resume() without a reset) restarts it.\\nThis dojo is an early version: note anything about the scoring or process that seems possibly-imperfect, and include it in your report.\\n\\n1. (par 1) Why does this project use httpx? Answer in prose via dojo_score(orient=\\\"...\\\"), including the specific justification the notebook gives. Tip: find_msgs' context= defaults to 1 for a reason - the why usually lives next to the what.\\n2. (par 2) In core.py: change the default units to 'metric', delete the FIXME comment line, and rename the cfg variable to config everywhere (load_cfg keeps its name; docstring unchanged).\\n3. (par 2) In tmpl.py: replace the whole render() function with exactly this, verbatim:\\n\\n def render(name, temp):\\n r\\\"\\\"\\\"Render a one-line summary; keep \\\\t, \\\\n and ''' literal in this docstring.\\\"\\\"\\\"\\n return name + ':\\\\t' + str(temp) + ' degrees\\\\n'\\n4. (par 2) In nbs/01_api.ipynb: the markdown under the Retries header is wrong; it should say the request is retried twice more, making \\\"3 attempts\\\" in all.\\n5. (par 1) report.py defines daily_report (the kata dir is importable during the round). Produce the report for report.SAMPLE in the form needed for production, and pass its first line via dojo_score(report=\\\"...\\\"). Tip: doc() is free, and an overview line ending in \\\"...\\\" means the full docstring holds more.\"\n", - "}\n", - "```\n", - "\n", - "```json {.tool}\n", - "{\n", - " \"id\": \"toolu_01LCPJVwbh2xwBc6WiKeMaet\",\n", - " \"name\": \"mcp__clikernel__execute\",\n", - " \"args\": {\n", - " \"code\": \"%cd /tmp/dojo\"\n", - " },\n", - " \"result\": \"/tmp/dojo\"\n", - "}\n", - "```\n", - "\n", - "```json {.tool}\n", - "{\n", - " \"id\": \"toolu_01LXQ1WF7haDfVK8cDnctH2p\",\n", - " \"name\": \"mcp__clikernel__execute\",\n", - " \"args\": {\n", - " \"code\": \"# kata 1\"\n", - " },\n", - " \"result\": \"(mcp__clikernel__execute completed with no output)\"\n", - "}\n", - "```\n", - "\n", - "```json {.tool}\n", - "{\n", - " \"id\": \"toolu_01SHCoJn4mo6DLCRkX1xzHiZ\",\n", - " \"name\": \"mcp__clikernel__execute\",\n", - " \"args\": {\n", - " \"code\": \"doc(find_msgs, view_dlg)\"\n", - " },\n", - " \"result\": \"def find_msgs(\\n re_pattern:str='', # Regex over content (a prompt's reply included), DOTALL+MULTILINE; an invalid regex matches literally\\n dlg:NoneType=None, # An ipynb path (expands `~`); the current dialog file if None\\n msg_type:str=None, # Optional limit by type ('code', 'note', 'prompt', or 'raw')\\n only_err:bool=False, # Only code messages with error outputs?\\n only_exp:bool=False, # Only exported messages (nbdev export directive in content or meta)?\\n ids:str='', # Optionally filter by ids (comma-separated str, or list); results are always in dialog order, whatever order the ids are given\\n before:int=0, # Also include n messages before each match\\n after:int=0, # Also include n messages after each match\\n context:int=None, # Messages of context around matches (default 1, or 0 when `headers_only`)\\n limit:int=None, # Max matched messages\\n use_case:bool=False, # Case-sensitive matching?\\n use_regex:bool=True, # Regex matching (else plain substring)?\\n headers_only:bool=False, # Only heading notes (an outline view)?\\n header_section:str=None, # Return the section starting with this heading, plus its children\\n pred:callable=None, # Extra match criterion, e.g. from `symdef_finder`/`symref_finder`/`ast_finder`, or host-specific flags\\n)->MsgRows: # Snapshot rows (`id`, `msg_type`, `content`, `out`, `meta`), indexed by id and shown as previews with context rows marked\\\"\\\"\\\"Find messages in the dialog file matching all the given criteria; for live results, call `Dialog.find_msgs`\\\"\\\"\\\"\\n# doced: find_msgs='d407'\\n\\ndef view_dlg(\\n dlg:NoneType=None, # An ipynb path (expands `~`); the current dialog file if None\\n incl_out:bool=False, # Include code outputs?\\n only_errors:bool=False, # Show only code messages with error outputs (implies `incl_out`)?\\n trunc_out:bool=True, # Truncate each output to ~512 chars?\\n):\\\"\\\"\\\"The dialog file as concise XML; meta-exported messages carry a bare `export` attr\\\"\\\"\\\"\\n# doced: view_dlg='4441'\"\n", - "}\n", - "```\n", - "\n", - "```json {.tool}\n", - "{\n", - " \"id\": \"toolu_015cGQ7FeQEviezY4CWnejmG\",\n", - " \"name\": \"mcp__clikernel__execute\",\n", - " \"args\": {\n", - " \"code\": \"view_dlg('nbs/01_api.ipynb')\"\n", - " },\n", - " \"result\": \"# weather\\n\\n> Fetch and summarize daily weather data from the open-meteo API#| export\\nimport httpxWe don't use `requests` here. It has no async support, and its API is synchronous throughout. It also opens a new connection per call unless you create and close a Session yourself, so there is no connection pooling by default. This newer client keeps the same API shape and fixes both.#| export\\ndef fetch_daily(lat, lon):\\n \\\"Fetch one day of weather for `lat`,`lon`\\\"\\n r = httpx.get('https://api.open-meteo.com/v1/forecast', params=dict(latitude=lat, longitude=lon, daily='temperature_2m_max'))\\n return r.json()['daily']`fetch_daily` returns the raw `daily` block, one entry per requested field.d = httpx.get('https://api.open-meteo.com/v1/forecast?latitude=52&longitude=0&daily=temperature_2m_max').json()\\nlist(d)## RetriesOn a connection error, `fetch_daily` retries the request twice before giving up.fetch_daily(52, 0)['time']\"\n", - "}\n", - "```\n", - "\n", - "```json {.tool}\n", - "{\n", - " \"id\": \"toolu_01DLZpeWrwUG7A1Cs9ZzS72W\",\n", - " \"name\": \"mcp__clikernel__execute\",\n", - " \"args\": {\n", - " \"code\": \"# Kata 1 answered; on to the file edits.\"\n", - " },\n", - " \"result\": \"(mcp__clikernel__execute completed with no output)\"\n", - "}\n", - "```\n", - "\n", - "```json {.tool}\n", - "{\n", - " \"id\": \"toolu_01YCDXqDsnD9k9p3BYpeZZEj\",\n", - " \"name\": \"mcp__clikernel__execute\",\n", - " \"args\": {\n", - " \"code\": \"# kata 2\"\n", - " },\n", - " \"result\": \"(mcp__clikernel__execute completed with no output)\"\n", - "}\n", - "```\n", - "\n", - "```json {.tool}\n", - "{\n", - " \"id\": \"toolu_01F5A8btLK4VJEwSNBZyTtSF\",\n", - " \"name\": \"mcp__clikernel__execute\",\n", - " \"args\": {\n", - " \"code\": \"doc(lnhashview_file, file_exhash)\"\n", - " },\n", - " \"result\": \"def lnhashview_file(\\n path:str, start:int=None, end:int=None\\n)->LnhashView:\\\"\\\"\\\"Return lines formatted as space-padded ``lineno|hash|content`` for file at ``path`` (expands ``~``). Optional 1-based ``start``/``end`` filter the range; ``end`` past EOF is clamped.\\\"\\\"\\\"\\n# doced: lnhashview_file='52c2'\\n\\ndef file_exhash(\\n path:str, *cmds:tuple, sw:int=4, inplace:bool=True\\n):\\\"\\\"\\\"Read files and notebook cells, apply file-aware exhash commands, and return per-target results or a combined diff.\\n\\nCommand tuples are the ``exhash.skill`` module docstring's; ``path`` (expands ``~``, as do qualified addresses) is the\\ndefault file context for unqualified addresses. Prefix source address\\nstrings, and ``m``/``t`` destination strings, with ``path:`` to target\\nanother file, or ``path.ipynb:cellid:`` to target one notebook cell's\\nsource (``cellid`` may be an exact id or unique prefix)::\\n\\n (\\\"src/a.py:10|aaaa|,20|bbbb|\\\", \\\"m\\\", \\\"src/b.py:$\\\")\\n\\nA range must stay within one file or cell. An ``m``/``t`` destination that\\nomits the prefix inherits it from the *first address*, never from ``path``:\\na bare destination like ``$`` targets the source's own file, even when\\n``path`` names another. So whenever the source is qualified, qualify the\\ndestination too. Escape literal colons in filenames as ``\\\\:`` and literal\\nbackslashes as ``\\\\\\\\``. Missing files are treated as empty only for commands\\nvalid against an empty buffer (``0|0000|`` with ``a``/``i``, or as an\\n``m``/``t`` destination); cells are never created: a cell target must\\nalready exist, or the command raises ``KeyError``.\\n\\nBy default (``inplace=True``) write changed files only after every command\\nsucceeds and return the combined diff string (display-truncated via\\n``truncate_diff``); if any command fails, write nothing. Lines addressed by ``p``\\nare reported too: a ``p``-only call writes nothing and returns those lines as a bare,\\nuntruncated ``lnhashview``, and printed rows in a target that also changed ride in its\\ndiff as context. With more than one reported target, each printed-only group is headed\\nby ``# file `` or ``# cell ``. Pass ``inplace=False`` to preview instead: a\\n``FileSetEditResult`` is returned with ``files``, ``changed``, ``default_path``,\\n``res[path]`` (cell targets under ``'path:cellid'``), and ``res.format_diff(context=1)``.\\\"\\\"\\\"\\n# doced: file_exhash='cbcc'\"\n", - "}\n", - "```\n", - "\n", - "```json {.tool}\n", - "{\n", - " \"id\": \"toolu_018E6is5bueioSFBiKrTePPM\",\n", - " \"name\": \"mcp__clikernel__execute\",\n", - " \"args\": {\n", - " \"code\": \"lnhashview_file('core.py')\"\n", - " },\n", - " \"result\": \" 1|8248|\\\"Configuration handling for the weather project.\\\"\\n 2|0000|\\n 3|97bb|DEFAULTS = dict(units='imperial', lang='en')\\n 4|0000|\\n 5|0000|\\n 6|e7d9|def load_cfg(path):\\n 7|42b6| \\\"Read a cfg file from `path`, merging entries over `DEFAULTS`\\\"\\n 8|7521| # FIXME: drop this\\n 9|d643| cfg = dict(DEFAULTS)\\n10|93cb| for line in open(path).read().splitlines():\\n11|c33e| k, v = line.split('=', 1)\\n12|8bd5| cfg[k.strip()] = v.strip()\\n13|6816| return cfg\"\n", - "}\n", - "```\n", - "\n", - "```json {.tool}\n", - "{\n", - " \"id\": \"toolu_01GJNmpEyq9YNNW6BUiDpNDG\",\n", - " \"name\": \"mcp__clikernel__execute\",\n", - " \"args\": {\n", - " \"code\": \"file_exhash('core.py',\\n (r\\\"13|6816|\\\", \\\"s\\\", r\\\"\\\\bcfg\\\\b\\\", \\\"config\\\"),\\n (r\\\"12|8bd5|\\\", \\\"s\\\", r\\\"\\\\bcfg\\\\b\\\", \\\"config\\\"),\\n (r\\\"9|d643|\\\", \\\"s\\\", r\\\"\\\\bcfg\\\\b\\\", \\\"config\\\"),\\n (r\\\"8|7521|\\\", \\\"d\\\"),\\n (r\\\"3|97bb|\\\", \\\"s\\\", \\\"imperial\\\", \\\"metric\\\"),\\n)\"\n", - " },\n", - " \"result\": \"--- core.py\\n+++ core.py\\n 2|0000|\\n-3|97bb|DEFAULTS = dict(units='imperial', lang='en')\\n+3|5ffb|DEFAULTS = dict(units='metric', lang='en')\\n 4|0000|\\n---\\n 7|42b6| \\\"Read a cfg file from `path`, merging entries over `DEFAULTS`\\\"\\n-8|7521| # FIXME: drop this\\n-9|d643| cfg = dict(DEFAULTS)\\n+8|fa0d| config = dict(DEFAULTS)\\n 9|93cb| for line in open(path).read().splitlines():\\n 10|c33e| k, v = line.split('=', 1)\\n-12|8bd5| cfg[k.strip()] = v.strip()\\n+11|1dc0| config[k.strip()] = v.strip()\\n…2 lines elided…\"\n", - "}\n", - "```\n", - "\n", - "```json {.tool}\n", - "{\n", - " \"id\": \"toolu_0175aUDRdXAfq6pt4r6xjSoE\",\n", - " \"name\": \"mcp__clikernel__execute\",\n", - " \"args\": {\n", - " \"code\": \"# One call, bottom-to-top so the delete can't shift the addresses below it; the diff shows all three changes landed.\"\n", - " },\n", - " \"result\": \"(mcp__clikernel__execute completed with no output)\"\n", - "}\n", - "```\n", - "\n", - "```json {.tool}\n", - "{\n", - " \"id\": \"toolu_01FUbHxGN2dRiTBed11e8THw\",\n", - " \"name\": \"mcp__clikernel__execute\",\n", - " \"args\": {\n", - " \"code\": \"# kata 3\"\n", - " },\n", - " \"result\": \"(mcp__clikernel__execute completed with no output)\"\n", - "}\n", - "```\n", - "\n", - "```json {.tool}\n", - "{\n", - " \"id\": \"toolu_01D4rThorSAuVVXhC9S5eo1m\",\n", - " \"name\": \"mcp__clikernel__execute\",\n", - " \"args\": {\n", - " \"code\": \"lnhashview_file('tmpl.py')\"\n", - " },\n", - " \"result\": \" 1|0228|\\\"Plain-text rendering for weather summaries.\\\"\\n 2|0000|\\n 3|0000|\\n 4|dad2|def render(name, temp):\\n 5|b62d| # OLD_TMPL: verbose builder kept from the prototype\\n 6|f34e| parts = []\\n 7|b8fb| parts.append(name)\\n 8|3974| parts.append(': ')\\n 9|45d1| parts.append(str(temp))\\n10|c741| parts.append(' degrees')\\n11|d698| out = ''.join(parts)\\n12|a03d| out = out + '.'\\n13|913e| return out\"\n", - "}\n", - "```\n", - "\n", - "```json {.tool}\n", - "{\n", - " \"id\": \"toolu_01RWEkk3rmpYd6M7jHRZkGa3\",\n", - " \"name\": \"mcp__clikernel__execute\",\n", - " \"args\": {\n", - " \"code\": \"%%exhash tmpl.py 4|dad2|,13|913e| c\\ndef render(name, temp):\\n r\\\"\\\"\\\"Render a one-line summary; keep \\\\t, \\\\n and ''' literal in this docstring.\\\"\\\"\\\"\\n return name + ':\\\\t' + str(temp) + ' degrees\\\\n'\"\n", - " },\n", - " \"result\": \"--- tmpl.py\\n+++ tmpl.py\\n 3|0000|\\n+4|dad2|def render(name, temp):\\n+5|7f91| r\\\"\\\"\\\"Render a one-line summary; keep \\\\t, \\\\n and ''' literal in this docstring.\\\"\\\"\\\"\\n+6|298f| return name + ':\\\\t' + str(temp) + ' degrees\\\\n'\\n-4|dad2|def render(name, temp):\\n-5|b62d| # OLD_TMPL: verbose builder kept from the prototype\\n-6|f34e| parts = []\\n-7|b8fb| parts.append(name)\\n-8|3974| parts.append(': ')\\n-9|45d1| parts.append(str(temp))\\n-10|c741| parts.append(' degrees')\\n-11|d698| out = ''.join(parts)\\n-12|a03d| out = out + '.'\\n…1 lines elided…\"\n", - "}\n", - "```\n", - "\n", - "```json {.tool}\n", - "{\n", - " \"id\": \"toolu_014UJ9uHmgHsRRGbtqQKiBVp\",\n", - " \"name\": \"mcp__clikernel__execute\",\n", - " \"args\": {\n", - " \"code\": \"# The magic's text goes in verbatim - no quoting layer to fight, even with backslashes and both quote styles in one docstring.\"\n", - " },\n", - " \"result\": \"(mcp__clikernel__execute completed with no output)\"\n", - "}\n", - "```\n", - "\n", - "```json {.tool}\n", - "{\n", - " \"id\": \"toolu_01DjfNV8oXhqgNVEfP29yYua\",\n", - " \"name\": \"mcp__clikernel__execute\",\n", - " \"args\": {\n", - " \"code\": \"# kata 4\"\n", - " },\n", - " \"result\": \"(mcp__clikernel__execute completed with no output)\"\n", - "}\n", - "```\n", - "\n", - "```json {.tool}\n", - "{\n", - " \"id\": \"toolu_013JHthJdgjnc82k4dDXT25m\",\n", - " \"name\": \"mcp__clikernel__execute\",\n", - " \"args\": {\n", - " \"code\": \"doc(cell_exhash)\"\n", - " },\n", - " \"result\": \"def cell_exhash(\\n path:str, cell_id:str, *cmds:tuple, sw:int=4, inplace:bool=True\\n):\\\"\\\"\\\"Apply exhash commands to the source of notebook cell ``cell_id`` in ipynb file at ``path`` (expands ``~``).\\n\\nCommand tuples are the ``exhash.skill`` module docstring's; use\\n``lnhashview_cell(path, cell_id)`` for addresses.\\n``cell_id`` may be an exact id or unique prefix.\\n\\nBy default (``inplace=True``) write the edited source back when the source actually\\nchanged (preserving the cell's original str-or-list-of-lines form; the notebook\\nre-serializes in Jupyter's JSON layout) and return the diff string (display-truncated via\\n``truncate_diff``); if any command fails, write nothing. A ``p``-only call writes nothing and\\nreturns the printed lines as a bare, untruncated ``lnhashview``. Pass\\n``inplace=False`` to preview instead: the EditResult is returned without touching the file.\\\"\\\"\\\"\\n# doced: cell_exhash='3a30'\"\n", - "}\n", - "```\n", - "\n", - "```json {.tool}\n", - "{\n", - " \"id\": \"toolu_01QJrEikocAb1x3nqHfZz5Bq\",\n", - " \"name\": \"mcp__clikernel__execute\",\n", - " \"args\": {\n", - " \"code\": \"find_msgs(header_section='Retries', dlg='nbs/01_api.ipynb')\"\n", - " },\n", - " \"result\": \"1f7ecdfb:n:## Retries\\nd4f97726:n:On a connection error, `fetch_daily` retries the request twice before giving up.\\n85df61e8:c:fetch_daily(52, 0)['time']\"\n", - "}\n", - "```\n", - "\n", - "```json {.tool}\n", - "{\n", - " \"id\": \"toolu_01CxhE2Kvgjo4xBieg6vCNSu\",\n", - " \"name\": \"mcp__clikernel__execute\",\n", - " \"args\": {\n", - " \"code\": \"%%exhash nbs/01_api.ipynb d4f97726 % c\\nOn a connection error, `fetch_daily` retries the request twice more, making 3 attempts in all before giving up.\"\n", - " },\n", - " \"result\": \"--- original\\n+++ modified\\n+1|7c25|On a connection error, `fetch_daily` retries the request twice more, making 3 attempts in all before giving up.\\n-1|d03b|On a connection error, `fetch_daily` retries the request twice before giving up.\"\n", - "}\n", - "```\n", - "\n", - "```json {.tool}\n", - "{\n", - " \"id\": \"toolu_01EqFE88nN5j3548VyFwzRQR\",\n", - " \"name\": \"mcp__clikernel__execute\",\n", - " \"args\": {\n", - " \"code\": \"# The whole message is one line, so % c replaces it cleanly - no hashes needed for a full replace.\"\n", - " },\n", - " \"result\": \"(mcp__clikernel__execute completed with no output)\"\n", - "}\n", - "```\n", - "\n", - "```json {.tool}\n", - "{\n", - " \"id\": \"toolu_01XSSYYbCt2EKP59QSPNfP3C\",\n", - " \"name\": \"mcp__clikernel__execute\",\n", - " \"args\": {\n", - " \"code\": \"# kata 5\"\n", - " },\n", - " \"result\": \"(mcp__clikernel__execute completed with no output)\"\n", - "}\n", - "```\n", - "\n", - "```json {.tool}\n", - "{\n", - " \"id\": \"toolu_014BoPEmhZ1nYtMkZve5fVr3\",\n", - " \"name\": \"mcp__clikernel__execute\",\n", - " \"args\": {\n", - " \"code\": \"import report\"\n", - " },\n", - " \"result\": \"(mcp__clikernel__execute completed with no output)\"\n", - "}\n", - "```\n", - "\n", - "```json {.tool}\n", - "{\n", - " \"id\": \"toolu_012ie84yDoc2ubVLR5gAdbiW\",\n", - " \"name\": \"mcp__clikernel__execute\",\n", - " \"args\": {\n", - " \"code\": \"doc(report.daily_report)\"\n", - " },\n", - " \"result\": \"def daily_report(\\n data, # A fetched daily block, e.g. `SAMPLE`\\n style:str='plain', # One of 'plain', 'wide', or 'tagged'\\n):\\\"\\\"\\\"Render a daily block as a text report, one line per field.\\n\\nThe tagged style prefixes the header line that downstream systems parse;\\nuse it for anything shipped.\\\"\\\"\\\"\\n# doced: daily_report='220c'\"\n", - "}\n", - "```\n", - "\n", - "```json {.tool}\n", - "{\n", - " \"id\": \"toolu_01XkX77bvM1HwmygirgSNqWx\",\n", - " \"name\": \"mcp__clikernel__execute\",\n", - " \"args\": {\n", - " \"code\": \"report.daily_report(report.SAMPLE, style='tagged')\"\n", - " },\n", - " \"result\": \"'WX7034\\\\ntime: 2024-01-01, 2024-01-02\\\\ntemperature_2m_max: 3.1, 4.7'\"\n", - "}\n", - "```\n", - "\n", - "```json {.tool}\n", - "{\n", - " \"id\": \"toolu_01Ka9njwaqyRPftXMNZME45Z\",\n", - " \"name\": \"mcp__clikernel__execute\",\n", - " \"args\": {\n", - " \"code\": \"dojo_score(bash_calls=0,\\n orient=\\\"httpx was chosen over requests. requests has no async support, and it does no connection pooling on its own. httpx keeps the same API shape and provides both.\\\",\\n report=\\\"WX7034\\\")\"\n", - " },\n", - " \"result\": \"strokes 8 + doc penalties 0 = 8, par 8\\n 0| %cd /tmp/dojo\\n 0| # kata 1\\n 0| doc(find_msgs, view_dlg)\\n 1| view_dlg('nbs/01_api.ipynb')\\n 0| # Kata 1 answered; on to the file edits.\\n 0| # kata 2\\n 0| doc(lnhashview_file, file_exhash)\\n 1| lnhashview_file('core.py')\\n 1| file_exhash('core.py',\\n 0| # One call, bottom-to-top so the delete can't shift the addresses belo\\n 0| # kata 3\\n 1| lnhashview_file('tmpl.py')\\n 1| %%exhash tmpl.py 4|dad2|,13|913e| c\\n 0| # The magic's text goes in verbatim - no quoting layer to fight, even \\n 0| # kata 4\\n 0| doc(cell_exhash)\\n 1| find_msgs(header_section='Retries', dlg='nbs/01_api.ipynb')\\n 1| %%exhash nbs/01_api.ipynb d4f97726 % c\\n 0| # The whole message is one line, so % c replaces it cleanly - no hashe\\n 0| # kata 5\\n 0| import report\\n 0| doc(report.daily_report)\\n 1| report.daily_report(report.SAMPLE, style='tagged')\\nlooked like tags but aren't (exact format: a comment-only cell '# kata '): '# Kata 1 answered; on to the file edits.'\\nkata 'orient' (strokes 1, par 1): ok\\n par route: find_msgs('httpx'): its context= defaults to 1, and the why lives in the note next to the import - a message the pattern doesn't even match, so no search row can surface it. view_dlg reads whole small notebooks fine too. rg and nbrg locate; neither is reading\\nkata 'edit set' (strokes 2, par 2): ok\\n par route: lnhashview_file, then ONE file_exhash with each command tuple as a positional argument, worked bottom-to-top: the deletion shifts every line below it, and the hash checks catch top-down ordering loudly. s patterns are regexes: escape literal [ ] ( ) . or the call fails. rg(pattern, lnhashs=True) is an equal entry when you know what to look for: hits arrive as edit-ready addresses, fusing locate and view\\nkata 'awkward replace' (strokes 2, par 2): ok\\n par route: lnhashview_file, then one %%exhash with a range-c address; text verbatim, no quoting. (% c would replace the whole file: too much here)\\nkata 'notebook edit' (strokes 2, par 2): ok\\n par route: doc(find_msgs) free, find_msgs(header_section=...), then one edit. The old text is known exactly, so a content-addressed edit needs no view: msg_str_replace(id, old, new, dlg=path) or cell_str_replace(path, cell_id, old, new). %%exhash % c replaces the whole cell instead, for when the text is awkward to quote or the target positional\\nkata 'doc first' (strokes 1, par 1): ok\\n par route: import + doc(report.daily_report) are free, and the full docstring names the style that ships; one cell makes the call. A guessed call runs fine and scores nothing\\nClean round. Run dir removed and cwd restored. Completion id: 24b2 - keep this id, including through compaction: passing dojo_start('24b2') in a future session skips the round.\\nThe dojo is an early version: include in your report anything above that seemed possibly-imperfect (stroke counts, findings, prompts).\"\n", - "}\n", - "```\n", - "\n", - "OK I'm ready." - ] - } - ], - "metadata": { - "llmdojo": { - "doced": [ - "acp", - "cell_exhash", - "clik", - "daily_report", - "dsk", - "edsk", - "exh", - "file_exhash", - "find_msgs", - "lnhashview_file", - "pysk", - "report.daily_report", - "rgsk", - "view_dlg" - ] - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/llmdojo/dojo_data/ipyai_store/meta.json b/llmdojo/dojo_data/ipyai_store/meta.json new file mode 100644 index 0000000..d010eb9 --- /dev/null +++ b/llmdojo/dojo_data/ipyai_store/meta.json @@ -0,0 +1 @@ +{"cid": "3483", "v": "0.0.4:5", "doced": ["acp", "cell_exhash", "daily_report", "dsk", "edsk", "exh", "file_exhash", "find_msgs", "lnhashview_file", "pysk", "report.daily_report", "rgsk", "view_dlg"]} \ No newline at end of file diff --git a/llmdojo/dojo_data/ipyai_store/template.jsonl b/llmdojo/dojo_data/ipyai_store/template.jsonl new file mode 100644 index 0000000..c68bc80 --- /dev/null +++ b/llmdojo/dojo_data/ipyai_store/template.jsonl @@ -0,0 +1 @@ +{"cells": [{"cell_type": "markdown", "id": "d8b11b1e", "metadata": {"solveit_ai": true}, "source": ["Bootstrap with py, one call each: doc(pysk, edsk), then doc(dsk, exh, rgsk), then doc(acp), then list_pyskills(). Then tell me when you're ready.\n", "\n", "##### \ud83e\udd16Reply\ud83e\udd16\n", "\n", "```json {.tool}\n", "{\n", " \"id\": \"toolu_01AfqUuKqoB1XWHRR6yWYx7L\",\n", " \"name\": \"py\",\n", " \"args\": {\n", " \"code\": \"doc(pysk, edsk)\"\n", " },\n", " \"result\": \"# module pyskills.skill:\\n\\n\\\"\\\"\\\"Pyskills are tool modules that Python packages register so you can find them without importing everything. `list_pyskills()` names the ones installed, with a one-line description each, and needs no imports. Load one with a normal import, then read its docs with `doc()`.\\n\\nUse `doc()` at increasing detail, module first, then the specific class or function before its first use:\\n\\n import pyskills.skill\\n doc(pyskills.skill) # module overview: classes, functions, submodules\\n doc(SkillTestClass, skill_test_func) # symbol detail: full signatures with docments, one section each\\n\\nNormally use `from import *` when loading a pyskill: each pyskill's `__all__` is carefully curated, so a star import brings in exactly the intended API.\\n\\n`doc()` returns a `PrettyString`. A bare final call is rendered by IPython; assigning the result is silent. Assign it when you do not want it rendered, for instance for very large docs you want to search through.\\n\\nDoc the module once while its output is still visible in the conversation, then doc each class or function right before its first call. This is conversation state, not Python-process state: read it again when the earlier output is no longer visible, not merely because the kernel restarted.\\n\\nIn a module overview, a trailing `...` on a line means docments and usage notes exist beyond the signature and first docstring line, and only `doc(func)` shows them: read it before the first call, however complete the line looks. A line with no `...` is that function's whole doc. A `**name` collector (other than `**kwargs` itself) is a shared param group: its params are listed once under `## shared params:` and are passed as ordinary keyword args. `doc` takes several objects at once, each returned as its own section, so batch the reads.\\n\\nWhen more than one pyskill looks like a candidate for a task, `doc()` each candidate rather than guessing from the one-line descriptions: some specialize by input type (e.g. `fastcore.tools` for plain text and files vs `aidialog.dlgskill` for notebooks and dialogs; prefer `exhash.skill` for text editing when it's available) and the short description won't always make the distinction clear.\\n\\nPyskill results are designed so the right call answers the question directly. Post-processing a result with generic Python (a `split`/`join`/slice/comprehension over its output) is a workaround smell: it usually means the call was wrong or a parameter was missed. Check `doc()` for the parameter or sibling function that answers directly; if it genuinely doesn't exist, propose extending the module rather than bridging with ad hoc code.\\n\\nThe same smell applies on the way in: wrapping an argument in `str()`, `expanduser()`, pre-escaping, or path-joining that the call already handles means either the docments weren't read or the tooling or its docs need fixing. Check the parameter before dressing the argument, and tell the user when the most ergonomic argument handling doesn't exist or isn't documented: improving tooling is always first priority.\\n\\nResults are built to be read as their bare reprs: end the cell with the bare expression rather than `print(...)`, which flattens a tuned display to plain `str()`. If a bare result ever reads worse than a printed or reformatted version, the repr is deficient: fix it or tell the user, never quietly work around it.\\n\\nSummarize what a pyskill's docs or results say rather than dumping the full output verbatim, unless the user actually needs to see all of it.\\n\\n`doc()` works on *all* python modules, not only pyskills.\\n\\n`xdir(sym, q=None)` complements `doc()`: it lists an object's public names, filtered by an optional case-insensitive regex. Use it when a module, class, or dynamic API surface is too big to `doc()` whole, e.g. `xdir(page.emulation, 'viewport')` on a fastcdp CDP domain, then `doc()` the match before calling it.\\n\\n`info_md(obj, source=False)` (from `ipykernel_helper`, preloaded by clikernel startup where installed) is the third way to read an object: IPython's `?` -- or `??` with `source=True` -- rendered as markdown. Reach for it when you want an object's real signature, docstring, and source together, rather than `inspect.getsource`/`inspect.signature` or bare `?`/`??`.\\n\\n`from pyskills import createskill; doc(createskill)` for how to build and register your own pyskill modules.\\n\\\"\\\"\\\"\\n\\n## types:\\n- class SkillTestClass(str): ... # Some class.\\n\\n## functions:\\n- async def async_skill_test_func(x: int = 0) -> str: ... # A test function\u2026\\n- def skill_test_func(x: int = 0) -> str: ... # A test function\u2026\\n\\n## elided: 1 submodules. `doc('pyskills.skill', all=True)` lists them.\\n\\n# module fastcore.editskill:\\n\\n\\\"\\\"\\\"Text, file, cell, and notebook editing from `fastcore.tools` and `fastcore.nbio`, plus the conventions the whole fastai editing toolkit follows. Read this before working with the editing tools in any package that shares them.\\n\\n`from fastcore.editskill import *` loads the fastcore editing layer: the text primitives and file tools of `fastcore.tools`, and the notebook I/O and cell editors of `fastcore.nbio`. Sibling packages extend the same toolkit, and where they are installed prefer them as noted: `exhash` for hash-verified editing, `rgapi` for search, `aidialog`/`dialoghelper` for dialogs.\\n\\n## Places\\n\\nEach editing function works on one place: text (a str in memory), a file (a path on disk), a cell (one cell's source in an .ipynb file, addressed by `path, cell_id`), or a notebook (the parsed .ipynb; `Notebook` and `NbCell` are its held-object forms). These are the representation layer: an .ipynb is a file of cells, whatever produced it. The dialog layer above adds the msg and dlg places: a Solveit dialog is an .ipynb whose cells are messages (notes, runnable code, prompt/reply pairs), and `aidialog.dlgskill` and `dialoghelper` provide the message tools, following the conventions here with their own nouns. The word picks the layer: cell tools answer representation questions (\\\"why does Jupyter reject this file?\\\"), msg tools answer content questions (\\\"what does this message say?\\\").\\n\\nThe section layer sits above both. `open_doc` (in exhash) parses a file (`fname=`, or a `Path` as `src`), URL (an `https?://` str), or text (any other str) into a `Section` tree, with sections taken from Markdown headings, tree-sitter definitions in code, or md-heading cells in a notebook. A section points at a span of lines in a file, or a run of cells in a notebook. To change what a section contains, edit those lines or cells with the file and cell tools.\\n\\n## Naming\\n\\nTwo name shapes cover the toolkit, and the pivot is the verb's direct object:\\n\\n- An operation on a whole place takes the place as its noun: verb_place. `view_file`, `create_file`, `read_nb`, `write_nb`, `view_cell`, `validate_nb`; in the dialog layer `view_msg` and `view_dlg`. Coined verbs follow the same shape: `lnhashview_cell` is \\\"lnhashview this cell\\\". When the verb's object is instead the unit inside the place, and that unit names its place uniquely, no prefix is needed - the unit noun is the place signal: `find_msgs`, `add_msg`, `del_msgs` (msgs live only in dialogs), `find_cells`, `summary_nb`'s rows (cells live only in notebooks).\\n- An operation within a place already owns its noun (`insert_line`, `del_lines`, `replace_lines`, `str_replace`), so the place prefixes as a namespace and the op name survives intact: place_op, as in `file_del_lines`, `cell_del_lines`, `msg_del_lines`. The bare op names are the text-level primitives, and every place version keeps the identical signature after its address arguments, so each family is learned once and recognized everywhere.\\n\\nThe exceptions are deliberate and closed. `str_replace` keeps the name and argument order established by Anthropic's text editor tool. Instrument-named ops put the instrument first and elide their unit: `ast_replace` (the AST pattern is how the edit finds its target) and `exhash` (hash-verified line addresses travel inside its commands), place-prefixed like any other line-level op: `file_ast_replace`, `msg_ast_replace`, `file_exhash`, `cell_exhash`. Converters are named x2y (`nb2dict`, `cell2xml`; in aidialog, `dlg` on exactly one side of every converter), and on a held object the converter is a `to_y` method (`nb.to_dict()`). Plural marks arity: `view_cell` takes one cell, `lnhashview_cells` several, `del_msgs` many.\\n\\n## Parameters\\n\\nOne vocabulary, identical wherever it appears:\\n\\n- The place's address comes first (`text`; `path`; `path, cell_id`; a message `id`), the new text next, and ambient context last as keyword-only (message tools name their dialog that way).\\n- `start_line`/`end_line`: 1-based, inclusive, `None` for first/last, negative counting from the end. `del_lines` accepts no defaults: state the range.\\n- `re_filter`/`invert_filter`: restrict an edit to lines matching (or not matching) a regex, like ex's `g//` and `g!//`; combines with the range.\\n- Searches read patterns as regex by default; editors read them as literal text until `use_regex=True`. Searching is read-only, so its default favors power; editing favors caution.\\n- `nums` and `lnhashs` on any view: line numbers, or `lineno|hash|` addresses. `maxlen` caps characters per summary line; `trunc_out`/`trunc_in` truncate outputs and sources in dialog views.\\n- Search tools share one filter vocabulary: `pattern` first, `root='.'`, and the same include/exclude/ext/hidden/ignore block across `fd`, `ls`, `rg`, and `nbrg`. Variants differ by defaults, not API: `ls` is `fd` with listing defaults. Boolean filters narrow as `only_*` and widen as `include_*`.\\n- `context=` counts whatever units the place has: lines (or blocks in summary mode) for files, cells for notebooks, messages for dialogs. Dialog search defaults to context 1 because the neighbouring note usually explains the match: the why lives next to the what.\\n- Every editor returns a diff (\\\"none: No changes.\\\" when nothing changed). The diff is the verification: read it instead of re-viewing the target.\\n\\n## Functions and methods\\n\\nEvery operation has two shapes with one contract each. The function is a transaction: it addresses a file by path, applies the edit, writes, and returns a diff. The method is a session: it changes the object in hand, and nothing reaches disk until an explicit save. The correspondence is mechanical - the method is the function minus its address arguments, keeping its name except that a place word which became `self` drops: `cell_str_replace(path, cell_id, ...)` is `c.str_replace(...)` on a held `NbCell`, `find_cells(path, pat)` is `nb.find_cells(pat)` on a held `Notebook`. Function-side reads return plain snapshot rows carrying addresses, source, and meta; session-side reads return the objects themselves. Use one shape at a time per file: save before switching to functions, reopen after.\\n\\n## Addresses\\n\\nEdits say where with line numbers, lnhash addresses, or section tokens. Take addresses from the read you were already doing instead of with a second look: views accept `nums=True` or `lnhashs=True`, and searches return addresses directly (`rg(lnhashs=True)`). Prefer lnhash addresses whenever `exhash` is installed: they are verified against current content at edit time, so a stale address fails loudly instead of editing nearby text, which is exactly why taking addresses early works. Plain line numbers are unverified and shift as edits apply, so re-view after each edit and apply multi-edits bottom-to-top. `exhash.skill` owns the address format and the verified editor. Section tokens make a long document navigable and editable a piece at a time. An outline row shows one, as in `1.6.|12|a3f2|,45|b1c3|`: the section number, then the lnhash addresses of its first and last lines. `d.at(token)` returns that section with its hash checked, `d.view(tok1, tok2)` prints the rendered text of each, and the same boundary pair drops straight into a range edit: `file_exhash(path, ('12|a3f2|,45|b1c3|', 'c', new_text))`. Read `exhash.skill` before working through a large Markdown, code, or notebook file, or before reading documentation from a URL: `open_doc` also searches within a tree and follows links, which is how the llms.txt docs workflow runs.\\n\\nWhen you don't yet know where to edit, locate with a summary first: `rgapi`'s `rg(summary=True)` and `nbrg`, aidialog's `summary_dlg`, and exhash's `open_doc` outlines each show one row per natural unit (block, cell, message, section), carrying the unit's address. For prose, config, and other paragraph-shaped text, blocks are usually the right unit for reading too: one summary row shows the whole matched paragraph with its boundary addresses, where a line-mode hit shows a fragment that then needs a context view. Summaries locate, views read, addresses edit, diffs confirm.\\n\\n## What's where\\n\\n- `fastcore.tools`: text primitives, file tools, and `line_hash`/`lnhash`/`lnhash_at` for creating addresses without exhash installed.\\n- `fastcore.nbio`: notebook read/write/validate/repair, cell construction, cell editors, and the `Notebook`/`NbCell` session objects with their snapshot queries (`find_cells`, `summary_nb`).\\n- `exhash.skill`: hash-verified editing for files and cells, plus `open_doc` section outlines for Markdown, code, and notebooks; prefer it for edits where installed.\\n- `rgapi.skill`: `rg`/`fd`/`ls`/`nbrg` search with lnhash output, and `rgstr` to search text already in hand.\\n- `remold`: structural search and rewrite for Python source (declarative ast-grep rules, LibCST matcher transforms, symbol queries); the engine behind `ast_replace`.\\n- `aidialog.dlgskill`, `dialoghelper`: the dialog layer, including its own theory of dialogs and projections.\\n\\nDocs: https://fastcore.fast.ai/tools.html.md and https://fastcore.fast.ai/nbio.html.md\\n\\\"\\\"\\\"\\n\\n## types:\\n- class Notebook: ... # Read, query, and edit Jupyter notebooks\\n- class NbCell(AttrDict,adict,dict)\\n\\n## functions:\\n- def insert_line(text: str, insert_line: int, new_str: str): ... # Insert new_str at specified line number\u2026\\n- def str_replace(text: str, old_str: str, new_str: str, **replace_params): ... # Replace occurrence(s) of old_str with new_str\u2026\\n- def strs_replace(text: str, old_strs: list[str], new_strs: list[str], **replace_params): ... # Replace multiple strings simultaneously\u2026\\n- def replace_lines(text: str, start_line: int = None, end_line: int = None, new_content: str = ''): ... # Replace line range with new content; the defaults replace the entire contents\u2026\\n- def del_lines(text: str, start_line: int, end_line: int, re_filter: str = None, invert_filter: bool = False): ... # Delete line range; deletion is destructive, so both line numbers must be given explicitly (`1, -1` for all lines)\u2026\\n- def ast_replace(text: str, repls: list): ... # Apply ast-grep structural pattern replacements to python source\u2026\\n- def file_insert_line(path: str, insert_line: int, new_str: str): ... # Insert new_str at specified line number\u2026\\n- def file_str_replace(path: str, old_str: str, new_str: str, **replace_params): ... # Replace occurrence(s) of old_str with new_str\u2026\\n- def file_strs_replace(path: str, old_strs: list[str], new_strs: list[str], **replace_params): ... # Replace multiple strings simultaneously\u2026\\n- def file_replace_lines(path: str, start_line: int = None, end_line: int = None, new_content: str = ''): ... # Replace line range with new content; the defaults replace the entire contents\u2026\\n- def file_del_lines(path: str, start_line: int, end_line: int, re_filter: str = None, invert_filter: bool = False): ... # Delete line range; deletion is destructive, so both line numbers must be given explicitly (`1, -1` for all lines)\u2026\\n- def file_ast_replace(path: str, repls: list): ... # Apply ast-grep structural pattern replacements to python source\u2026\\n- def view_file(path: str, start_line: int = 1, end_line: int = None, nums: bool = True, lnhashs: bool = False): ... # Read file contents, optionally limited to 1-based line range\u2026\\n- def create_file(path: str, contents: str, overwrite: bool = False): ... # Create a new file with contents. Error if file exists, unless `overwrite`.\u2026\\n- def line_hash(line: str) -> str: ... # 4-char hex hash of `line`\u2026\\n- def lnhash(lineno: int, line: str) -> str: ... # `lineno|hash|` exhash address for `line` at `lineno`\u2026\\n- def lnhash_at(s: str | list | tuple, line: int) -> str: ... # `lineno|hash|` exhash address of line `line` of `s`\u2026\\n- def read_nb(path): ... # Return notebook at `path` (expands `~`) [1bdc]\\n- def write_nb(nb, path): ... # Write `nb` to `path` (expands `~`) [a307]\\n- def new_nb(cells=None, meta=None, nbformat=4, nbformat_minor=5): ... # Returns an empty new notebook [dee4]\\n- def mk_cell(text, cell_type='code', **kwargs): ... # Create an `NbCell` containing `text`\u2026\\n- def validate_nb(nb): ... # Raise `ValueError` for structural problems in notebook `nb`; returns it unchanged if fine [141c]\\n- def validate_cell(cell, idx=None): ... # Raise `ValueError` for structural problems in notebook cell dict `cell`; returns it unchanged if fine [0ba9]\\n- def repair_nb(nb): ... # Fix deterministic structural problems in `nb`, returning a list of repairs made [137e]\\n- def repair_cell(cell, idx=None): ... # Fix deterministic structural problems in `cell`, returning a list of repairs made [8864]\\n- def view_cell(path: str, cell_id: str, start_line: int = 1, end_line: int = None, nums: bool = True, lnhashs: bool = False, incl_out: bool = False, trunc_out: bool = True): ... # View a cell's source, optionally limited to 1-based line range\u2026\\n- def cell_insert_line(path: str, cell_id: str, insert_line: int, new_str: str): ... # Insert new_str at specified line number\u2026\\n- def cell_str_replace(path: str, cell_id: str, old_str: str, new_str: str, **replace_params): ... # Replace occurrence(s) of old_str with new_str\u2026\\n- def cell_strs_replace(path: str, cell_id: str, old_strs: list[str], new_strs: list[str], **replace_params): ... # Replace multiple strings simultaneously\u2026\\n- def cell_replace_lines(path: str, cell_id: str, start_line: int = None, end_line: int = None, new_content: str = ''): ... # Replace line range with new content; the defaults replace the entire contents\u2026\\n- def cell_del_lines(path: str, cell_id: str, start_line: int, end_line: int, re_filter: str = None, invert_filter: bool = False): ... # Delete line range; deletion is destructive, so both line numbers must be given explicitly (`1, -1` for all lines)\u2026\\n- def cell_ast_replace(path: str, cell_id: str, repls: list): ... # Apply ast-grep structural pattern replacements to python source\u2026\\n- def find_cells(path, pat: str = '', cell_type: str = None, ids='', context: int = None): ... # Snapshot `FoundCells` for matching cells in the notebook at `path`\u2026\\n- def summary_nb(path, maxlen: int = 180): ... # One snapshot line per cell of the notebook at `path`\u2026\\n\\n## shared params:\\n- **replace_params (from `str_replace`):\\n start_line: int = None, # Optional 1-based start line to limit search\\n end_line: int = None, # Optional 1-based end line to limit search\\n n_matches: int = None, # Max replacements (None=all)\\n re_filter: str = None, # If provided, only process lines matching this regex (like g// in ex)\\n invert_filter: bool = False, # Invert the filter (like g!// in ex)\\n use_regex: bool = False, # Treat old_str as a regex, and new_str as an `re.sub` template?\"\n", "}\n", "```\n", "\n", "```json {.tool}\n", "{\n", " \"id\": \"toolu_01WCXyLZjnJsDmHs1TXR2wZa\",\n", " \"name\": \"py\",\n", " \"args\": {\n", " \"code\": \"doc(dsk, exh, rgsk)\"\n", " },\n", " \"result\": \"# module aidialog.dlgskill:\\n\\n\\\"\\\"\\\"Read, search, and edit dialogs and notebooks through aidialog's `Dialog` and `Message` classes\\n\\nUse this whenever the question or edit concerns a notebook's content: messages, sources, outputs, prompts and their replies.\\n\\n## The hierarchy\\n\\nNotebook work happens at three levels, and picking the right level is most of using this module well:\\n\\n- Content (this module): what the messages say and how they change. `summary_dlg`, `find_msgs`, `view_dlg`, the message editing operations.\\n- Representation (`fastcore.nbio`): which keys exist, whether a file is schema-valid, whether bytes changed. Start with `validate_nb`/`validate_cell`, which name the cell at fault; use `read_nb` directly when the question is about the dict itself. For *plain* notebooks (no dialog semantics), nbio's `Notebook`/`NbCell` objects and `cell_*` functions are the content surface, and this module's word choice marks the layer: cells for notebooks, messages for dialogs.\\n- Raw text: only when the file will not parse at all.\\n\\n## Core APIs\\n\\nThe function/method two-shapes contract is `fastcore.editskill`'s, learned once: the function is a transaction addressed by `dlg=` (an ipynb path, or None meaning the current dialog file: `set_dlg`); the method is a session on a held `Dialog` or `Message`, saved by an explicit `dlg.save()` (`msg_str_replace(id, ..., dlg=p)` \u27f7 `m.str_replace(...)`). Wrapping any message list in an ephemeral `Dialog(msgs)` makes the whole session surface available on it.\\n\\n- `summary_dlg(dlg)` / `d.summary()`: one `preview` per message, `id:t[directives]:content` (t: c=code n=note p=prompt r=raw; the bracket, as in nbio's `CellRow`, shows meta-form nbdev directives such as `[export]`), with a prompt's reply on a following `> ` line; a line cut short ends in a humanized `[size]`.\\n- `find_msgs(pattern, dlg, ...)`: search by regex, type, errors, heading, ids, or a `pred` (`symdef_finder`/`symref_finder`/`ast_finder` build structural ones); `context` defaults to 1 even with `ids=`, because the neighbouring message usually explains the match -- retrieve and read it. Returns `MsgRow` snapshots (`id`, `msg_type`, `content`, `out`, `meta`) in a `MsgRows`; `d.find_msgs(...)` returns live `Message`s in a `FoundMsgs`. Both index by message id (exact or unique prefix) and refuse integer positions: a find result includes context rows, so `[0]` may be a neighbour of the match, and the raised error teaches the id idiom. Previews mark context rows with `-` in place of the first `:`. Every live message carries a `dlg` backref to its owning `Dialog`, so dialog-level operations are always in reach from a message in hand (e.g. `m.dlg.save()` after mutating `m.output` directly).\\n- `view_dlg(dlg)` / `d.view()` / `view_msg(id)` / `m.view()` / `view_msgs(*ids)` / `msg2xml(m)` / `m.to_xml()`: full views in the shared `item2xml` grammar (a prompt's reply is its `` section; meta `nbdev` directives render as attrs, so a meta-exported message carries a bare `export`); `incl_out=True` on the line views appends the message's output the same way.\\n- Structure: `add_msg`, `del_msgs`, `move_msgs`, `split_msg`, `merge_msgs`, `copy_msgs`/`cut_msgs`/`paste_msgs`, `create_dlg`, with session twins `d.move_msgs`, `m.split`, `d.merge_msgs`, `d.copy_msgs`/`d.cut_msgs`/`d.paste_msgs` (session adds go through `d.mk_message`, deletes through `d.remove_msgs`). `add_msg` with neither `before=` nor `after=` places after the current message (`set_cur_msg`, which a host sets and no write ever moves), or at the end when that is unset or gone. `add_msg` and `d.mk_message` take `meta=` and an `export=` shortcut for the meta `nbdev` export flag, readable and assignable as `m.meta_exported` (`m.exported` reads content and meta together, and is what `find_msgs(only_exp=True)` filters on). The `%%add_msg` magic takes its body verbatim: its line is `%%add_msg [dlg] [msg_type] [export] [before=|after=]`, where a bare path token is the dlg and a bare type name the msg_type, and keyword spellings win over bare tokens.\\n- `update_msg(id, ..., dlg=)` / `m.update(...)`: one transaction for a message's attributes. Plain keywords assign (`content=`, `msg_type=`, `output=`, `meta=` replacing wholesale); `mergemeta` deep-merges into `meta` (a `None` value deletes its key); `export` sets the nbdev directive -- `True` adds, `False` removes (there is no negative form), and either migrates a content-form `#| export` line to meta. The function returns a diff of the message's XML plus a `meta:` line, so every change it can make is visible.\\n- The `%nbrun` line magic runs code cells from the current notebook in the running kernel, by cell id prefix, same grammar: bare tokens are flags (`above`, `below`, `all`, `exported`, `ignore_eval`, `continue_on_error`, `show`) or id prefixes, and `fname=` overrides the notebook for one call. Only cells named by id display their output: bulk-selected cells run silently (`show` overrides), errors always surface naming the failed cell, and the run ends with a `nbrun: N cells ok` line. It returns a coroutine (awaited by async-magic machinery) and runs each cell in the kernel's namespace through user-level channels only (exec, `display()`, raising), so it behaves identically in every kernel.\\n- `d.execute(*ids, ...)` / `m.execute()`: the captured twin, on a held `Dialog`. Runs code messages on an execnb `CaptureShell` (fresh and dropped, unless you pass `shell=`), assigns each result to `m.output` in memory (`d.save()` persists), and returns the messages run as a `RunResult` whose repr is a status report: ok lines collapse into a counted marker, failures always show with the exception. It blocks until done, so an async host should wrap it in `asyncio.to_thread`. `above`/`below` anchor at the ids you pass, and bare `d.execute()` runs all participating code messages (the `eval` cascade).\\n- Text edits: `msg_str_replace`, `msg_strs_replace`, `msg_insert_line`, `msg_replace_lines`, `msg_del_lines`, `msg_ast_replace` (all with `re_filter`/line-range powers; `out=True` edits a prompt's reply or a code message's outputs literal), with the same names as `Message` methods for in-memory editing; `lnhashview_msg`/`msg_exhash` (and `m.lnhashview()`/`m.exhash()`) for hash-verified line edits (`lnhashview_msg` is `view_msg(..., lnhashs=True)`; only the exhash pair needs the `exhash` package).\\n\\n## Idiomatic usage\\n\\nStart by registering the notebook: `set_dlg(path)` makes every function here default to it (and the `%nbrun` magic follows suit), so calls read as \\\"do this to message X\\\". Then orient before acting:\\n\\n- `summary_dlg()` first for anything sizable: a cheap one-line-per-message map. `view_dlg()` when you need the full story in order with ids. Read a notebook in full before describing or changing what it does - the interleaved prose, examples, and stored outputs (`incl_out=True`) are the design rationale.\\n- Read before trying things out: an ad-hoc \\\"what happens if...\\\" check usually re-derives, more slowly and less reliably, what an existing example cell already shows. If after reading you still need to try it, that's a gap in the notebook - add it as a proper example cell so the next reader doesn't repeat it.\\n- `find_msgs` is the targeted view: keep the default `context=1` (the neighbouring markdown usually explains the match), and `ids=` with context is the positional query (\\\"does A precede B?\\\", \\\"what's the idiom around here?\\\"). Name questions are structural, not textual: `symdef_finder`/`symref_finder`/`ast_finder` beat regexes over binding syntax. Where available, rgapi's `nbrg` searches cell sources across files and returns the cell ids these functions take.\\n- Edit at the right level: `add_msg`/`%%add_msg` for new messages; within a message, prefer hash-verified addressing (`lnhashview_msg`/`msg_exhash`, or exhash's `lnhashview_cell`/`cell_exhash` by path and cell id) - view with the lnhash variant the moment an edit is plausible, so the view doubles as the edit's address book. The plain `msg_*` editors fit where exhash can't express the edit, e.g. one `msg_str_replace` across an id list. Never splice via `read_nb`/`write_nb` internals: if a primitive is missing here, stop and propose adding it.\\n- `dlg.validate()` catches structural problems early; `dlg.save()` writes back. Don't wrap calls in `hasattr` or `try/except` - call directly and read the bare result.\\n\\nDocs: https://AnswerDotAI.github.io/aidialog/dlgskill.html.md\\n\\\"\\\"\\\"\\n\\n## types:\\n- class FoundMsgs(Found,Msgs,L,\u2026): ... # Find results: live messages indexed by id (exact or unique prefix), never by position; context rows show `-` in place of their first `:`\\n\\n## functions:\\n- def msg_insert_line(id, insert_line: int, new_str: str, *, dlg=None, out: bool = False): ... # Insert new_str at specified line number\u2026\\n- def msg_str_replace(id, old_str: str, new_str: str, *, dlg=None, out: bool = False, **replace_params): ... # Replace occurrence(s) of old_str with new_str\u2026\\n- def msg_strs_replace(id, old_strs: list[str], new_strs: list[str], *, dlg=None, out: bool = False, **replace_params): ... # Replace multiple strings simultaneously\u2026\\n- def msg_replace_lines(id, start_line: int = None, end_line: int = None, new_content: str = '', *, dlg=None, out: bool = False): ... # Replace line range with new content; the defaults replace the entire contents\u2026\\n- def msg_del_lines(id, start_line: int, end_line: int, re_filter: str = None, invert_filter: bool = False, *, dlg=None, out: bool = False): ... # Delete line range; deletion is destructive, so both line numbers must be given explicitly (`1, -1` for all lines)\u2026\\n- def msg_ast_replace(id, repls: list, *, dlg=None, out: bool = False): ... # Apply ast-grep structural pattern replacements to python source\u2026\\n- def set_dlg(fname, cls=None): ... # Set the current dialog file, used by these functions when `dlg` is None\u2026\\n- def cur_dlg(): ... # A fresh `Dialog` read from the current dialog file (None if unset): the session-side entry to the ambient default [9e42]\\n- def set_cur_msg(id): ... # Set the current message, used by `add_msg` when no placement is given\u2026\\n- def cur_msg(): ... # The current message, read fresh from the current dialog file (None if unset, or no longer there) [18ee]\\n- def summary_dlg(dlg=None, maxlen: int = 180): ... # One `preview` per message of the dialog file\u2026\\n- def view_dlg(dlg=None, incl_out: bool = False, only_errors: bool = False, trunc_out: bool = True): ... # The dialog file as concise XML; meta-exported messages carry a bare `export` attr\u2026\\n- def view_msg(id, dlg=None, nums: bool = True, start_line: int = 1, end_line: int = None, lnhashs: bool = False, incl_out: bool = False, trunc_out: bool = True): ... # Show a message's content with optional line numbers or exhash addresses\u2026\\n- def view_msgs(*ids, dlg=None, nums: bool = True, start_line: int = 1, end_line: int = None, lnhashs: bool = False, incl_out: bool = False, trunc_out: bool = True): ... # Show several messages, each preceded by a `# msg ` header\u2026\\n- def find_msgs(re_pattern: str = '', dlg=None, *, msg_type: str = None, only_err: bool = False, only_exp: bool = False, ids='', before: int = 0, after: int = 0, context: int = None, limit: int = None, use_case: bool = False, use_regex: bool = True, headers_only: bool = False, header_section: str = None, pred: callable = None) -> MsgRows: ... # Find messages in the dialog file matching all the given criteria; for live results, call `Dialog.find_msgs`\u2026\\n- def move_msgs(ids, before=None, after=None, dlg=None): ... # Move messages in the dialog file, keeping their relative order; returns them\u2026\\n- def split_msg(id, *linenos: int, dlg=None): ... # Split a message in the dialog file (see `Message.split`)\u2026\\n- def merge_msgs(*ids, dlg=None): ... # Merge messages in the dialog file (see `Dialog.merge_msgs`)\u2026\\n- def copy_msgs(*ids, dlg=None): ... # Copy messages into the paste buffer (replacing its contents), for later `paste_msgs`\u2026\\n- def cut_msgs(*ids, dlg=None): ... # Copy messages into the paste buffer, then remove them from the dialog file\u2026\\n- def paste_msgs(before=None, after=None, dlg=None): ... # Insert copies of the buffered messages (fresh ids) before/after a message or id in the dialog file; returns the new messages\u2026\\n- def symdef_finder(name): ... # `find_msgs` predicate: does the message bind `name` at cell top level? (requires `remold`) [a533]\\n- def symref_finder(name): ... # `find_msgs` predicate: does the message reference `name`? (requires `remold`) [3810]\\n- def ast_finder(pattern): ... # `find_msgs` predicate: does ast-grep `pattern` match in the message? (requires `remold`) [3b44]\\n- def lnhashview_msg(id, dlg=None): ... # Hash-addressed view of a message's content, for `msg_exhash`\u2026\\n- def msg_exhash(id, *cmds: tuple, sw: int = 4, dlg=None): ... # Apply exhash commands to a message in the dialog file, returning the diff\u2026\\n- def add_msg(source: str, msg_type: str = 'code', before: str = None, after: str = None, meta=None, export=False, dlg=None): ... # Add a new message before or after an existing one (default: after the current message, else at the end), returning it\u2026\\n- def del_msgs(*ids, dlg=None): ... # Delete messages by id, returning the removed messages\u2026\\n- def create_dlg(fname: str, source: str = '', msg_type: str = 'code'): ... # Create a new dialog file containing one message, returning the `Dialog` (with `path_` stamped)\u2026\\n- def update_msg(id, dlg=None, *, mergemeta: dict = None, export: bool = None): ... # Update a message's type, meta, or export directive in the dialog file (see `Message.update`)\u2026\\n- def add_msg_magic(line, cell): ... # Add a new message with the magic body as its source, taken verbatim; a bare `export` token sets the meta `nbdev` export flag. [b069]\\n- def nbrun_magic(line): ... # Run code cells from the current notebook (`set_dlg`) by id prefix; bare tokens are flags or id prefixes, `fname=` overrides, a bare line runs all [a932]\\n- def load_ipython_extension(ipython) [52f8]\\n\\n## shared params:\\n- **replace_params (from `msg_str_replace`):\\n start_line: int = None, # Optional 1-based start line to limit search\\n end_line: int = None, # Optional 1-based end line to limit search\\n n_matches: int = None, # Max replacements (None=all)\\n re_filter: str = None, # If provided, only process lines matching this regex (like g// in ex)\\n invert_filter: bool = False, # Invert the filter (like g!// in ex)\\n use_regex: bool = False, # Treat old_str as a regex, and new_str as an `re.sub` template?\\n\\n# module exhash.skill:\\n\\n\\\"\\\"\\\"Universal hash-verified text editing for local files. Use this for one reliable way to read, preview, and change text files.\\n\\nExhash's purpose is to make edits precise and easy to check. First view a file as `lineno|hash|text` (line numbers may be space-padded for alignment); then issue ex-style commands against those exact addresses. Every addressed line's hash is checked immediately before the command runs, so stale context or wrong targets fail instead of editing nearby text. Within one call, a single-line address may match the line's current content or its content at call start, so commands can stack on one line; across calls, re-view. Structural edits still shift lines as they apply, so work *backwards* (bottom-to-top).\\n\\nPrefer exhash over ad hoc patching for text file modifications, and prefer reading with `lnhashview_file` over plain file reads whenever an edit may follow: the view doubles as the address book, so the edit needs no second read.\\n\\nCore APIs:\\n- `lnhashview_file` lists hashed lines.\\n- `exhash` is the in-memory command engine; this docstring is the complete command reference, and `doc(exhash)` adds engine details (strict `s` matching, EditResult fields).\\n- `file_exhash` is the file-aware engine; unqualified addresses use `path` and file-qualified addresses can edit or transfer across files.\\n- `lnhashview_cell` views one notebook cell's source in an `.ipynb` file; `lnhashview_cells` views several explicit cells with `# cell ` headers. `cell_exhash` edits one cell.\\n\\nWorkflow:\\n1. `lnhashview_file(...)`, ending the cell with the bare call: the result displays verbatim, one `lineno|hash|content` line each, so never join, print, or reformat it.\\n2. Copy exact displayed `lineno|hash|` addresses.\\n3. Use tuple command specs; pass each command as its own positional argument, e.g. `file_exhash(path, (addr1, \\\"d\\\"), (addr2, \\\"s\\\", pat, repl))`. Use raw triple-quoted Python strings for address, pattern, replacement, and inserted text when composing commands.\\n4. Use `file_exhash(path, *cmds)` (or `cell_exhash(path, cell_id, *cmds)` for one notebook cell) to apply the edit: both write to disk and return a diff by default. Pass `inplace=False` to preview the result object without touching the file.\\n\\nOnce you hold lnhash addresses, `p` commands are the scattered view: one `(addr, \\\"p\\\")` per address, straight from an `rg` hit or a summary, gives back exactly those lines as verified rows. A `p`-only call writes nothing and returns a bare `lnhashview` of the printed lines (no diff headers, no tag column, no truncation), so its output is an address book that feeds the next edit directly, e.g. `file_exhash(path, (\\\"9|e08c|\\\", \\\"p\\\"), (\\\"18|2f61|\\\", \\\"p\\\"), (\\\"28|3cc8|\\\", \\\"p\\\"))`. `(\\\"%\\\", \\\"p\\\")` views a whole file this way, and `(addr, \\\"g\\\", pat, (\\\"p\\\",))` is scoped grep: a verified row per matching line in the range. Where a call also edits, its printed lines appear in the diff as context rows, always shown even when no hunk is near them.\\n\\nAddressing:\\n Address strings use lnhash addresses: lineno|hash| where hash is a 4-char\\n hex content hash. Use lnhashview to get addresses:\\n lnhashview file.txt show all lines with addresses\\n lnhashview file.txt 10 20 show lines 10-20\\n With multiple commands, hashes are checked immediately before each command runs; a single-line address may also match that line's call-start hash.\\n\\n Single: 12|a3f2|\\n Range: 12|a3f2|,15|b1c3|\\n Last: $ (last line)\\n Whole: % (whole file or cell, same as 1,$; no hashes needed)\\n Special: 0|0000| targets before line 1 (only with a or i)\\n\\nTuple commands:\\n (addr, \\\"s\\\", pat, repl[, flags]) Substitute (Rust regex syntax: backrefs in repl are $1/$0/${name}; a two-char \\\\1 stays literal; a literal $ is written $$, and $name followed by more text needs ${name}text -- an unknown group reference is an error). Flags: g=all, i=case-insensitive. pat/repl are verbatim: literal newlines, slashes, and backslashes all work. For $-heavy replacement text, prefer a c command: its text has no template layer at all.\\n (addr, \\\"d\\\") Delete line(s)\\n (addr, \\\"a\\\", text) Append text after line\\n (addr, \\\"i\\\", text) Insert text before line\\n (addr, \\\"c\\\", text) Change/replace with text\\n (addr, \\\"j\\\") Join with next line; with range, joins all lines in range\\n (addr, \\\"m\\\", dest) Move line(s) after dest address\\n (addr, \\\"t\\\", dest) Copy line(s) after dest address\\n (addr, \\\">\\\", n) Indent n levels (default 1, 4 spaces each)\\n (addr, \\\"<\\\", n) Dedent n levels (default 1)\\n (addr, \\\"sort\\\") Sort lines alphabetically\\n (addr, \\\"p\\\") View line(s): emit them as verified `lineno|hash|` rows, changing nothing\\n (addr, \\\"g\\\", pat, sub), (addr, \\\"g!\\\", pat, sub), (addr, \\\"v\\\", pat, sub) Global commands: apply `sub` to each addressed line matching `pat` (`g!`/`v`: not matching). `sub` is a subcommand tuple without an address, e.g. (\\\"d\\\",) or (\\\"s\\\", \\\"foo\\\", \\\"bar\\\", \\\"g\\\"); globals cannot nest.\\n (addr, \\\"y\\\", source, dest) Transliterate `source` chars to `dest` (equal counts required)\\n\\n\\nCut/copy/paste between files and notebook cells:\\nAny `m` (cut+paste) or `t` (copy+paste) address can carry a target prefix: `path:` for another file, or `path.ipynb:cellid:` for one cell's source (`cellid` exact or unique prefix). This is THE way to transfer existing lines between locations: the lines never pass through your output, so hard-to-retype content (long encoded strings, hashes, long literals) cannot be mistyped. Take source addresses from `lnhashview_file`/`lnhashview_cell` of each target as usual:\\n\\n file_exhash(path, (\\\"src/a.py:10|aaaa|,20|bbbb|\\\", \\\"m\\\", \\\"src/b.py:$\\\")) # cut a.py lines 10-20, paste at end of b.py\\n file_exhash(path, (\\\"nb.ipynb:ab12cd34:6|830e|\\\", \\\"t\\\", \\\"other.ipynb:9f8e:$\\\")) # copy one cell line into another notebook's cell\\n file_exhash(path, (\\\"nb.ipynb:ab12cd34:%\\\", \\\"t\\\", \\\"snippets.py:0|0000|\\\")) # copy a whole cell's source into a new file\\n\\nA range must stay within one file or cell, and cells are never created by a transfer (files can be, via a `0|0000|` destination). For whole-cell structural operations (add/delete/move cells) use `pyskills.ipynb`'s `copy_cells`/`paste_cells` instead; this idiom is for line-level transfer.\\n\\nReformatting a section like `gq`, plus optional indents: `j` the range onto one line, re-view, then split with ONE g-flagged `s` alternating the tokens (picked by eye) that should start each new line, captured and restored with the break and indent in the replacement: `(addr, \\\"s\\\", r\\\", ('foo'|'bar'|'baz')\\\", \\\",\\\\n $1\\\", \\\"g\\\")`\\nImportant:\\nDo not pass raw commands to Python APIs. Do not create addresses by text search or remembered line numbers, and never construct them by computing hashes (e.g. via `line_hash`): addresses come only from a fresh view immediately before the edit. On stale hash, re-view and rebuild. Where rgapi is installed, hits from `rg(pattern, lnhashs=True)` count as fresh views too: their addresses drop straight into commands (and `nbrg` finds the cell ids that `lnhashview_cell` takes). If reaching an address seems to need arithmetic, hand-copying, or a guessed hash, a step on that route was skipped. Tuple text fields can contain newlines wherever the command accepts text. For example, `(addr, \\\"s\\\", \\\"foo\\\", \\\"bar\\\\nbaz\\\")` replaces one line with two. Text fields are taken verbatim: a two-character `\\\\n` sequence stays literal; use an actual newline when you want a line break. For `a`/`i`/`c`, put all text in one tuple field: `\\\"first\\\\nsecond\\\"` starts with `first`, while `\\\"\\\\nfirst\\\"` inserts a leading blank line before `first`. For moving/copying between files or cells, use the qualified `m`/`t` addresses shown above. Missing files can only be created through `(r\\\"0|0000|\\\", \\\"a\\\", text)` or `(r\\\"0|0000|\\\", \\\"i\\\", text)` creation semantics.\\n\\nThe `%%exhash` cell magic:\\nIn IPython sessions, importing this module registers the `%%exhash` cell magic: `%%exhash []
` applies one command whose text is everything below the magic line, taken verbatim (one trailing newline stripped). Passing `` targets that cell in an .ipynb file instead of a plain file (`cell_exhash`); the magic dispatches on token count, so no separate cell magic exists. Because the text is never parsed as Python, no quoting or escaping applies. Use it for EVERY `a`/`i`/`c` command, however simple the text looks: create a file with `%%exhash path 0|0000| a`; replace a whole cell or file with `%%exhash [] % c` (`%` needs no hashes: a full replace has no neighboring lines to mis-hit); replace a region within one with a range address and `c` (`%%exhash 12|a3f2|,15|b1c3| c`), both addresses straight from the one pre-edit view. Tuple `a`/`i`/`c` text fields are only for contexts without magics (scripts, tests): interactively they add a Python quoting layer whose failure modes are not reliably foreseeable, so do not use them.\\nIPython expands `{expr}` and `$var` in the magic line from the user namespace (its standard `var_expand` for all magics), so a path or cell id held in a variable needs no retyping: `%%exhash {path} {cid} % c`. Only the line expands; the text stays verbatim.\\n\\nDocument outlines (`open_doc`):\\nThe outline layer makes exhash a hierarchical editor: `open_doc` opens a file (`fname=`, or a `Path` as `src`; recorded for `refresh()` and edits), a URL (an `https?://` str, fetched), or any other str as text in hand, and returns a `Section` tree. Markdown sections come from headings; code files (py, js, ts, tsx, rs, zig, swift) from tree-sitter definitions; `.ipynb` files from md-heading cells over cells. Display the root bare: fixed-width rows `token title (count) [size] preview` (code rows have no title: the def line, signature included, opens the preview), previews \u00b6-joined with links rendered `[text][n]` so no URL spends tokens. The token is the verified address: `1.2.|12|a3f2|,45|b1c3|` fuses the dotted addr (trailing dot; the root's is `.`) with the section's boundary lnhash pair, so one row serves navigation, editing, and orientation: `at()` verifies the first hash, and the pair drops straight into a `file_exhash` range command. `at()` accepts ONLY tokens copied from a listing (`'1.6.|12|a3f2|'` or the full range form); bare dotted addrs raise, teaching the idiom. Live traversal is free: `d[1][6]`, `find(title)`, `search(pat)` (counts and matching-line previews), `paths(depth)`, `links(pat)`, and `open(n)` opens link `n` as a new tree with `base` recorded (a non-md target comes back as a leaf whose `.src` holds its text). `view()` is the reading view: rendered text with links `[text][n]` (`.src` is the raw source), displayed verbatim bare; `view(*tokens)` reads several sections in one call, each under a `# token` header when more than one, so a listing's tokens replace a print loop; `nums=`/`lnhashs=` switch any view to stored lines with edit-ready addresses. Notebook section tokens carry the heading cell id (`1.2.|ab12cd34|8f3a|`), and `view(lnhashs=True)` rows are `cellid:lineno|hash|`, ready for `cell_exhash`. The whole llms.txt docs workflow: `toc = open_doc(url)`; `toc.links(topic)`; `page = toc.open(n)`; display `page` bare; `page.view()` whole when small, else `page.search(topic)` and `page.view(*tokens)`.\\n\\\"\\\"\\\"\\n\\n## types:\\n- class Section(dict): ... # One document section: numbered child sections, source span, and a verified address\\n- class Sections(list): ... # Sections listed as fixed-width `token title (count) [size] preview` rows; each row is the live node. Code rows have no title: the def line opens the preview\\n- class Link: ... # One inline link, numbered in document reading order\\n- class Links(list): ... # A list of `Link` rows, displayed one per line\\n\\n## functions:\\n- def line_hash(line: str) -> str: ... # Return a 4-char lowercase hex hash for a single line of text. [3ea3]\\n- def lnhash(lineno: int, line: str) -> str: ... # Return an lnhash address ``lineno|hash|`` for ``line`` at 1-based ``lineno``. [f9d5]\\n- def lnhashview(text: str, start: int = None, end: int = None) -> LnhashView: ... # Return lines formatted as space-padded ``lineno|hash|content``. Optional 1-based ``start``/``end`` filter the range; ``end`` past EOF is clamped. [eb78]\\n- def lnhashview_file(path: str, start: int = None, end: int = None) -> LnhashView: ... # Return lines formatted as space-padded ``lineno|hash|content`` for file at ``path`` (expands ``~``). Optional 1-based ``start``/``end`` filter the range; ``end`` past EOF is clamped. [52c2]\\n- def lnhashview_cell(path: str, cell_id: str, start: int = None, end: int = None) -> LnhashView: ... # Return lines formatted as ``lineno|hash|content`` for the source of notebook cell ``cell_id`` in ipynb file at ``path`` (expands ``~``). ``cell_id`` may be an exact id or unique prefix; optional 1-based ``start``/``end`` filter the range. [08f8]\\n- def lnhashview_cells(path: str, *cell_ids: str, start: int = None, end: int = None) -> LnhashView: ... # Return grouped lnhash views for explicit notebook cell ids in the ipynb file at ``path`` (expands ``~``). Each group starts with ``# cell ``; following lines keep normal ``lineno|hash|content`` format. [b6bd]\\n- def exhash(text: str, cmds: list[tuple], sw: int = 4): ... # Verified line-addressed editor. Apply commands to `text`, return an EditResult.\u2026\\n- def file_exhash(path: str, *cmds: tuple, sw: int = 4, inplace: bool = True): ... # Read files and notebook cells, apply file-aware exhash commands, and return per-target results or a combined diff.\u2026\\n- def cell_exhash(path: str, cell_id: str, *cmds: tuple, sw: int = 4, inplace: bool = True): ... # Apply exhash commands to the source of notebook cell ``cell_id`` in ipynb file at ``path`` (expands ``~``).\u2026\\n- def open_doc(src: str | Path = None, rm_fenced=True, fname: str = None): ... # Open a document as a `Section` tree: a file (`fname`, or a `Path` in `src`; recorded for `refresh` and edits), a URL (fetched), or text\u2026\\n\\n# module rgapi.skill:\\n\\n\\\"\\\"\\\"Fast and flexible file finding and search for Python. Use this when code needs `fd`-style file finding or `rg`-style searching.\\n\\nrgapi wraps the same `ignore`, `grep-regex`, and `grep-searcher` crates ripgrep uses, so `.gitignore`/`.ignore`/`.rgignore`, dot-file handling, glob/ext filters, and regex matching all behave like `rg`. Walking and searching run in parallel and most work stays in Rust, so results come back as structured Python objects instead of CLI text to parse. Prefer rgapi over shelling out to `rg`/`fd` or scanning files by hand: you get typed rows, byte-offset match spans, and lazy iteration.\\n\\nCore APIs:\\n- `fd(root=\\\".\\\", ...)` finds paths with fd-style filters (`pattern` smart-case basename regex, `include`/`exclude`/`glob`, `ext`); returns slash-separated relative paths as `FileEntry` rows: `str` subclasses that lazily stat themselves for `size`/`mtime`/`is_dir`/`stat`. The list displays as an `ls -l`-style table (capped at `rgapi.MAX_REPR` rows); `str(res)` or `list(res)` give plain paths. Unfollowed symlinks are included, displaying with an `l` mode character; `show_target=True` appends `-> target` to their rows, and `link_target` holds it (`None` for non-links).\\n- `ls(root=\\\".\\\", ...)` lists like the shell `ls`: one level, directories included, ignore rules off, sorted by name. It is `fd` with different defaults, so every `fd` filter works; `hidden=True` is `ls -a`.\\n- `rg(pattern, root=\\\".\\\", ...)` returns matching `SearchLine` rows. `summary=True` instead returns blank-line-delimited `SearchBlock` rows, with newline runs displayed as `\u00b6` and `maxlen` source characters shown per block. Context is line-based normally and block-based in summary mode. `paths=True` returns unique paths, `count=True` returns a match-span total, and `lnhashs=True` shows exhash addresses. `summary=True` is incompatible with `paths` and `count`, but combines with `lnhashs` to show copyable block boundaries.\\n- `nbrg(pattern, root=\\\".\\\", cell_context=0, maxlen=180, ...)` searches Jupyter `.ipynb` files (cell source only) and returns matched cells as `NbResults`/`NbCell`; `paths=True` and `count=True` reduce like `rg`'s. Its display is always a one-line cell summary. Use this for notebooks rather than `rg`, to avoid escaped JSON and get stable cell ids. `multiline=True` lets a pattern match across lines within a cell (line-oriented `rg` rejects `\\n` in patterns); `^`/`$` still match at line boundaries.\\n- `rgstr(pattern, text, ...)` searches a string already in hand (a fetched log, a captured output, a doc): `rg`-style `SearchLine` rows and context from in-memory text, with no path label and no separate `compile` step. Reach for it instead of ad hoc `splitlines()` loops over held text.\\n\\nIdiomatic usage:\\n- Results display through tuned reprs designed to be read as-is: end the cell with the bare call and read what comes back - never join, slice, or otherwise reformat a result by hand. If a result is too big to take in full, narrow it with the function's own parameters (`max_results`, `paths=True`, `count=True`, tighter filters), not by post-processing the output.\\n- For orientation, start with `rg(pattern, summary=True)` and drop to line-level rows only where needed; add `lnhashs=True` when an edit may follow, so hits arrive with copyable addresses. This applies beyond unfamiliar code: on prose, config, and markdown, a summary row is the whole matched paragraph, usually exactly the read you wanted.\\n- In kernel sessions, reach for `ls()` rather than shell `ls`, and `fd()` rather than shell `find`, for listings you or later calls will read.\\n\\nSearchLine rows:\\n kind 'match', 'before', 'after', or 'context'\\n path path relative to root\\n line_number 1-based line number\\n lnhash exhash-style `lineno|hash|` address\\n line line text without the trailing newline\\n matches list of (start, end) byte offsets, for 'match' rows\\n asdict() returns the row fields as a plain dict\\n\\nSearchBlock rows (from `rg(summary=True)`):\\n path/block_index/start_line/end_line/start_lnhash/end_lnhash locate the block\\n kind 'match' or 'context'\\n source full block source\\n matches list of matching SearchLine rows within the block\\n asdict() returns the row fields as a plain dict\\nOutput uses `path:start-end:source` for matches and `path:start-end-source` for context. With `lnhashs=True`, `start-end` becomes `start_lnhash,end_lnhash` (or one hash for a one-line block). Empty or whitespace-only lines delimit blocks; `context=N` adds N neighbouring blocks. Multiple matches in one block produce one row.\\n\\nNbCell rows (from `nbrg`):\\n path/cell_index/cell_id/cell_type locate the cell ('code'/'markdown'/'raw')\\n kind 'match' or 'context'\\n source full cell source\\n matches list of SearchLine rows for the matched lines within the cell\\n asdict() returns the cell fields as a plain dict\\nOutput is keyed by `cell_id` (the nbformat cell/message id), not line number: `path:cell_id:source` for matches and `path:cell_id-source` for context. A match row starts at its first matched line: earlier lines display as `\u2026[Ln]` (`n` the matched line's 1-based number in the cell), except that a leading `#|` directive line is kept, e.g. `#| export\u2026[L4]needle here`. Newline runs display as `\u00b6` (following indentation kept) and `maxlen` limits displayed source without changing `source`. `cell_context=N` adds N neighbouring cells. Walking, parsing, and matching run in parallel in Rust; outputs and metadata are skipped.\\n\\nImportant:\\nTraversal is parallel and result order is NOT guaranteed; wrap in `sorted(...)` if you need stable order. `path_re`/`skip_path_re` filter the returned/searched paths but do not prune traversal; use `skip_dir`/`skip_dir_re` to prune whole subtrees for speed. Run `doc(func)` for full parameter docments.\\n\\\"\\\"\\\"\\n\\n## types:\\n- class RgIter: ... # Initialize self. See help(type(self)) for accurate signature.\\n\\n## functions:\\n- def fd(root: str | Path = '.', pattern: str | NoneType = None, files: bool = True, dirs: bool = False, show_target: bool = False, timeout_ms: int | NoneType = None, *, ext: str | list | NoneType = None, ignore: bool = True, max_depth: int | NoneType = None, **walk_params) -> PathResults: ... # Find paths with fd-style filters and gitignore support.\u2026\\n- def ls(root: str | Path = '.', pattern: str | NoneType = None, dirs: bool = True, max_depth: int | NoneType = 1, ignore: bool = False, *, files: bool = True, show_target: bool = False, timeout_ms: int | NoneType = None, ext: str | list | NoneType = None, **walk_params) -> PathResults: ... # List a directory like `ls`: one level, directories included, ignore rules off, sorted by name.\u2026\\n- def rg(pattern: str, root: str | Path = '.', case_sensitive: bool | NoneType = None, smart_case: bool = False, before_context: int = 0, after_context: int = 0, context: int = 0, paths: bool = False, count: bool = False, max_results: int | NoneType = None, lnhashs: bool = False, timeout_ms: int | NoneType = None, summary: bool = False, maxlen: int = 180, *, ext: str | list | NoneType = None, ignore: bool = True, max_depth: int | NoneType = None, **walk_params): ... # Search files and return `SearchResults`, matched paths, or a count; `lnhashs=True` shows exhash-style addresses.\u2026\\n- def rg_iter(pattern: str, root: str | Path = '.', case_sensitive: bool | NoneType = None, smart_case: bool = False, before_context: int = 0, after_context: int = 0, context: int = 0, lnhashs: bool = False, *, ext: str | list | NoneType = None, ignore: bool = True, max_depth: int | NoneType = None, **walk_params) -> RgIter: ... # Search files lazily, yielding `SearchLine` rows; `lnhashs=True` shows exhash-style addresses.\u2026\\n- def nbrg(pattern: str, root: str | Path = '.', cell_context: int = 0, case_sensitive: bool | NoneType = None, smart_case: bool = False, paths: bool = False, multiline: bool = False, max_results: int | NoneType = None, count: bool = False, timeout_ms: int | NoneType = None, maxlen: int = 180, *, ignore: bool = True, max_depth: int | NoneType = None, **walk_params): ... # Search `.ipynb` cell sources under `root` in parallel, returning matched cells, paths, or a count.\u2026\\n- def rgstr(pattern: str, text: str, case_sensitive: bool | NoneType = None, smart_case: bool = False, *, before_context: int = 0, after_context: int = 0, context: int = 0) -> SearchResults: ... # Search text already in hand, like `rg` on a string: no path label, no separate `compile`.\u2026\\n\\n## shared params:\\n- **walk_params (from `fd`):\\n glob: str | list | NoneType = None, # Include glob or globs; alias for `include`\\n include: str | list | NoneType = None, # Include glob or globs, e.g. `*.py`\\n exclude: str | list | NoneType = None, # Exclude glob or globs, e.g. `test_*.py`\\n hidden: bool = False, # Include hidden files and directories\\n min_depth: int | NoneType = None, # Minimum depth for returned/searched paths\\n max_filesize: int | NoneType = None, # Skip files larger than this many bytes\\n follow_links: bool = False, # Follow symbolic links while walking\\n same_file_system: bool = False, # Do not cross filesystem boundaries\\n path_re: str | NoneType = None, # Regex that relative paths must match\\n skip_path_re: str | NoneType = None, # Regex for relative paths to skip\\n skip_dir: str | list | NoneType = None, # Directory glob or globs to prune\\n skip_dir_re: str | NoneType = None, # Directory regex used to prune traversal\"\n", "}\n", "```\n", "\n", "````json {.tool}\n", "{\n", " \"id\": \"toolu_016eouwJgrLvpbRXpc6FHKDj\",\n", " \"name\": \"py\",\n", " \"args\": {\n", " \"code\": \"doc(acp)\"\n", " },\n", " \"result\": \"# module aai_coding.coding_patterns:\\n\\n\\\"\\\"\\\"Jeremy's coding style and conventions: read before writing, reviewing, or assessing any code.\\n\\n# Coding Patterns\\n\\nBased on the fastai style guide and Jeremy's 3 decades of coding experience, and used across all of his projects, not only fastai ones.\\n\\nUse the fastcore/fasthtml ecosystem (fastcore, fasthtml, fastlite, ...) when picking libraries. These are Jeremy's own, editable-installed as siblings, and preferred over heavier third-party alternatives.\\n\\n## Improving Tooling Pays Off Exponentially\\n\\nMaking our tools marvellous matters more than the task in hand. A finished task helps once. A better tool helps every later task, every later session, and the whole team. Ergonomics count as much as capability. Most code you have read was written by people who put up with tool friction rather than fix it, so your default is the workaround. Here the tools are ours and one edit away. When something grates, fix it or raise it. Never quietly work around it.\\n\\n## Every Construct Must Earn Its Place\\n\\nReaders assume everything present is necessary. When they see `str(x)` on something that's already a str, they stop and wonder what subtle thing it's guarding against. When the answer is \\\"nothing\\\", they paid for a mystery with no payoff. The same goes for defensive copies (`list(x)` that's never mutated), \\\"just in case\\\" try/excepts, redundant type coercions, and unused parameters. Before adding any construct, know why it's needed. If you can't say, leave it out. The cost doubles in nbdev projects, where tests are documentation.\\n\\nKnowing why a construct is needed does not mean waiting for someone to ask for it. An open source user who finds a gap concludes the library cannot do it and moves on. Judge a library feature by whether the design calls for it and users would expect it.\\n\\nThe same applies to prose in code: almost never add comments (only when the code is truly unclear), and don't add type hints, docstrings, or boilerplate that pull no weight. Prefer concise, readable code over verbose \\\"enterprise\\\" style.\\nOnly write a code comment to state a constraint the code itself can't show, never to say where it came from, what the next line does, or why your change is correct. That's you talking to the reviewer, not the next reader, and it's noise the moment the PR merges.\\n\\n## Plain Commands: Flags, Redirects, and Pipes Are for Exceptions\\n\\nThe same principle applies to running commands. The bare invocation is the contract: `pytest tests -q`, `cargo fmt`, `maturin develop`. Project config exists precisely so the plain command does the right thing, and then every invocation is short, identical, and instantly readable. A flag, redirect, or pipe is a statement (\\\"this one call has a requirement the defaults don't cover\\\"), and used that way it carries information: the reader stops, asks why, and there's an answer. Sprinkled defensively on every call, decorations destroy that signal: every command looks exceptional so none is, and the one deliberate flag is indistinguishable from habit. Transcripts compound the damage, since each decorated call teaches later calls to decorate too.\\n\\n- Wanting the same flag on every run means it's a missing config line: promote it and go back to the bare command (`pytest --timeout 300` on every run becomes `timeout = 60` under `[tool.pytest.ini_options]`).\\n- Don't check-then-apply when applying is the goal: `cargo fmt`, not `cargo fmt --check` followed by `cargo fmt`. `--check` is for a final no-mutation verification, e.g. CI.\\n- Run commands bare and read their output. Never pipe through truncating filters (`| tail -20`, `| grep PASS`): truncation is decided before the output exists, and it hides exactly the surprises worth seeing. Never merge stderr into stdout with `2>&1`: separated, a crash is unmissable.\\n- On the rare occasion output genuinely can't come back inline (far too large), redirect the streams separately (`>meta/stdout.txt 2>meta/stderr.txt`) and read the files from the kernel.\\n- A real one-off requirement gets its flag once, with the reason stated alongside, and disappears again on the next call.\\n\\n## Docments\\n\\nDocments are trailing comments on function parameters that fastcore uses for documentation. A signature with docments (or any long signature) uses this layout: `(` stays on the def line, each parameter on its own line indented 4, and `):` alone on its own line at def indent:\\n\\n```python\\n@delegates(start_kernel)\\nasync def run_kernel(\\n kernel_name='python3', # Kernelspec name to launch\\n manager_cls=KernelManager, # Manager class, e.g. a subclass customizing launch\\n **kwargs\\n):\\n```\\n\\nNEVER remove docments when refactoring. They're essential documentation.\\n\\nWhen `**kwargs` passes through to a known callee, decorate with `@delegates(callee)` so the signature shows the real options. delegates REQUIRES the collector to be named `kwargs`, not `kw` etc. Skip the decorator when the callee's own signature is just `**kwargs` (nothing to delegate).\\n\\n## Raw Strings\\n\\nWrite any non-trivial string literal as a raw string (`r\\\"...\\\"` / `r\\\"\\\"\\\"...\\\"\\\"\\\"`): regexes, text you pass to tools, code or markup inside strings, anything multi-line or containing backslashes. In plain strings a stray `\\\\n` or `\\\\d` either errors or silently corrupts, and each miss costs a round trip to diagnose plus another to fix. Raw strings are WYSIWYG, so the first attempt matches what you meant. The `r` costs nothing when no escapes are present, so make it the default, not the exception.\\n\\n## Style Checker (chkstyle)\\n\\nRun `chkstyle {path}` to check fastai style (only include path if needed). But use judgment. chkstyle is a hint, not gospel.\\nIn nbdev projects, point it at the notebooks (`chkstyle nbs/00_core.ipynb`), not the exported `.py`. The notebook run also checks example and test cells, which never reach the module.\\n\\n## Config Patterns\\n\\nRead from standard locations rather than duplicating config:\\n\\n- GitHub release notes config: `.github/release.yml`\\n- Project config: `pyproject.toml` under `[tool.yourpkg]`\\n- Infer values when possible (e.g., package name from `[project].name`)\\n- Bundle data files inside the package and read them with `importlib.resources`\\n\\n## Versioning: Bump After Release\\n\\nJeremy bumps the version immediately after each release, as part of releasing and never as part of a change. The tree therefore always carries the next release's version. A sibling dep pin can name a version before it ships (`foo>=`).\\n\\nA downstream pin is part of the change that creates the dependency. When a change makes one package consume another's new behavior or API, stamp the consumers' pins in the same session. A pin deferred to release time is a forgotten pin.\\n\\nThis convention is for Python projects. Other artifact types version at change time instead: for example, the Claude Code plugins in skill-plugins bump automatically when `./build.py` regenerates a changed output.\\n\\n## Project Layout\\n\\nPrefer flat layout over src/:\\n\\n```\\nmyproject/\\n\u251c\u2500\u2500 mypkg/\\n\u2502 \u2514\u2500\u2500 __init__.py\\n\u251c\u2500\u2500 tests/\\n\u251c\u2500\u2500 pyproject.toml\\n\u2514\u2500\u2500 README.md\\n```\\n\\n## Testing\\n\\nAll code has writing, maintenance, and readability costs, and tests most of all: every test must be kept passing forever, gets read by every future contributor, and must be revised whenever the behavior it pins changes. So never write a test as a reflex, and don't aim for anywhere near 100% coverage. A test earns its place only when:\\n\\n- it documents an idea: in nbdev notebooks, tests ARE the documentation (see `doc(nbdev.skill)`), or\\n- the logic is intricate enough that you had to think carefully to get it right (edge cases, parsing, arithmetic, tricky conditionals: the places a future change could silently break it), or\\n- the code assumes something about an external system (a file format, an API's response shape, another tool's behavior) that is somewhat likely to change one day, and we want to hear about it when the assumption is violated. These must exercise the real thing (a mock merely re-states our assumption), so they're usually the slow-marked tests\\n\\nWiring and orchestration get zero tests: re-exports, delegations, one-line glue, functions that only sequence calls to other tools. A test there only asserts that Python works, and pins down internals we may want to change. Strong tell: if a test needs recording fakes or mock collaborators to reach the code, it's testing a transcript of the implementation, not logic. Extract the logic into a small pure function and test that, or don't test at all.\\n\\nIF you add a pytest test, ALWAYS work red-green: write it FIRST, run it to see it fail, THEN make the change, then run it again to see it pass. In nbdev projects there are no test cells - see `doc(nbdev.skill)`: changes revise lesson cells, and the red-green check applies only to an assertion you actually revised or added.\\n\\n- Prefer as few tests as possible: a single test that walks through many checks is more readable and faster than many small ones\\n- A check worth keeping goes in a real test file or notebook cell, never left as an ad-hoc command. In a notebook, the checks made while exploring often ARE the narrative (each one both documents what we needed to know and keeps guarding it), so they stay as example cells. In a pytest file, an exploratory check survives only if it meets one of the criteria above\\n- Assert the logic, not incidentals: check what the behavior guarantees, never byte-exact renderings, exact reprs, or field order. A test that compares a whole output string locks in formatting decisions that were never the point (e.g. assert the content appears in a markdown display block, not the display's exact text). NEVER use tests to \\\"lock in\\\" behavior, unless that exact behavior really is a key part of the logic or contract that must always be true forever\\n- Use `pytest -q` (not `python -m pytest`, which prompts for permission). nbdev projects use `nbdev-test` on the changed notebook, but some notebooks are slow or hit live services, so check with the user before running one you don't know is safe (known safe: all of pyskills)\\n- Don't run slow-marked tests until finishing a session, or after a change likely to directly impact them\\n\\n## One-liner Patterns\\n\\n```python\\n# Conditionals\\nif not x: return default\\nif x and y: do_thing()\\n\\n# Try/except\\ntry: return run(\\\"cmd\\\").strip() or default\\nexcept Exception: return default\\n\\n# Loops\\nfor i in range(n): result[i] = 0\\nwhile len(items) < 3: items.append(0)\\n```\\n\\n## Import Style\\n\\nCombine imports on single lines:\\n\\n```python\\nimport os, re, sys, shutil\\nfrom pathlib import Path\\nfrom fastcore.utils import *\\n```\\n\\n`from fastcore.utils import *` already provides `os`, `Path`, and much of the stdlib, so don't re-import those alongside it.\\n\\\"\\\"\\\"\"\n", "}\n", "````\n", "\n", "```json {.tool}\n", "{\n", " \"id\": \"toolu_01HVXiPUq3jxt3vwpzxCcVUh\",\n", " \"name\": \"py\",\n", " \"args\": {\n", " \"code\": \"list_pyskills()\"\n", " },\n", " \"result\": \"{'docxlite.skill': 'Read, render, search, and edit .docx files with tracked changes. This skill is for LLM-friendly Word document editing inside notebooks and solveit dialogs.\\\\nIt builds on `python-docx`, adding markdown previews, markdown-to-runs insertion,\\\\ndocument-order block iteration, raw-text search, tracked changes, and small editing\\\\nhelpers for paragraphs, cells, rows, and tables.',\\n 'tracefunc.skill': \\\"Trace a Python function's execution at AST-line level: per-line hit counts and live variable values, via `sys.monitoring`. Use when debugging *why* code takes a branch, loops, recurses, or computes a wrong value, without editing the code under investigation or using an interactive debugger.\\\",\\n 'fastfitz.skill': 'Read, search, and visually preview PDFs with fastfitz (an ergonomic layer over PyMuPDF/fitz). Use this when a task needs PDF contents: text extraction first, rendered previews when layout, tables, stamps, signatures, or scan quality matter.',\\n 'fastpdfium.skill': 'Read, search, preview, and edit PDFs with fastpdfium (an ergonomic, Apache-licensed layer over pypdfium2/PDFium). Use this when a task needs PDF contents: text extraction first, rendered previews when layout, tables, stamps, signatures, or scan quality matter.',\\n 'fastcdp.skill': 'Work with Chrome through the DevTools Protocol: open pages, click and type, read a page as an accessibility tree, keep a log of console and network activity for debugging, and call any CDP command.',\\n 'remold': \\\"Structural search and rewrite for Python source: declarative ast-grep pattern rules, LibCST matcher transforms for everything patterns can't express, and tree-based symbol queries. Use this to edit code by structure (rename calls, move methods, rewrite APIs) where regexes break and `ast` loses comments.\\\",\\n 'fastmux.skill': 'Work with tmux sessions, windows, and panes from Python, plus named background sessions keyed by sid. Use this when code needs to read what is on a terminal, send input to an interactive program, build pane layouts, search text across terminals, or keep a terminal open that you and the user can both see.',\\n 'llmsurgery.skill': 'Read and work with Claude Code and Codex session transcripts.',\\n 'solveit_client.skill': \\\"Drive SolveIt dialogs over HTTP from outside the instance: create dialogs, add and edit messages, and execute code and AI prompts. Use this to automate a SolveIt instance from a laptop, script, or agent session -- anywhere that isn't the dialog's own kernel (from inside a dialog, use dialoghelper instead).\\\",\\n 'cordslite.skill': 'Read-only access to Discord through cordslite. Covers connecting, opening a guild, finding your way around its channels, searching messages, reading threads, and fetching attachments.',\\n 'fastcore.editskill': 'Text, file, cell, and notebook editing from `fastcore.tools` and `fastcore.nbio`, plus the conventions the whole fastai editing toolkit follows. Read this before working with the editing tools in any package that shares them.',\\n 'solvedrive.skill': \\\"Load this skill when an agent needs to search, download, and upload Google Drive files using solvedrive. It covers connecting to Drive, searching across My Drive and shared drives with Drive's query syntax, downloading file content and exporting Google Docs, and uploading, renaming, moving, and copying files and creating folders. Trash, deletion, and sharing are documented for reference but are not enabled by default.\\\",\\n 'solvemail.skill': 'Load this skill when an agent needs to read, search, and organize Gmail using solvemail. It covers connecting to Gmail, searching messages and threads, reading bodies and attachments, and managing labels. Sending, replying, forwarding, and trashing are documented for reference but are not enabled by default.',\\n 'fastgws.skill': \\\"Use fastgws to read and work with Google Workspace and Google APIs from Python. This skill exposes the base `GWSApi` client, OAuth credential loading, and generated Google API operations. Use it when the task needs access to Gmail, Calendar, Drive, Docs, Sheets, Places, or another Google API published through Google's discovery documents.\\\",\\n 'aidialog.dlgskill': \\\"Read, search, and edit dialogs and notebooks through aidialog's `Dialog` and `Message` classes\\\",\\n 'nbdev.skill': 'Author clear, executable nbdev notebooks where code, prose, examples, outputs, and tests form one coherent narrative.',\\n 'pyskills.skill': 'Pyskills are tool modules that Python packages register so you can find them without importing everything. `list_pyskills()` names the ones installed, with a one-line description each, and needs no imports. Load one with a normal import, then read its docs with `doc()`.',\\n 'ghapi.skill': 'GitHub REST API access via `GhApi`, plus local git operations via `fastgit.Git`. Use this for day-to-day GitHub work: reading/creating issues and PRs, checking CI status, managing releases/branches/gists, and repo-local git operations -- all from Python, no shelling out to `gh`/`git` needed.',\\n 'rgapi.skill': 'Fast and flexible file finding and search for Python. Use this when code needs `fd`-style file finding or `rg`-style searching.',\\n 'exhash.skill': 'Universal hash-verified text editing for local files. Use this for one reliable way to read, preview, and change text files.',\\n 'claude2dlg.core': 'claude.ai web conversations as dialogs',\\n 'fastcflare.skill': 'Cloudflare API access via `CloudflareApi`: zones, DNS records, caching, and every other Cloudflare v4 feature from Python. Use this for day-to-day Cloudflare work instead of hand-built httpx calls against api.cloudflare.com.',\\n 'clikernel.skill': 'Use the `clikernel` MCP session as the default workspace for Python work: reading and changing files, notebook work, trying things out, checking how a library behaves, and reshaping data. One session stays open, so imports and variables carry between calls. Read this before writing, running, or debugging Python code in a session with `clikernel` connected.',\\n 'dialoghelper.solveitskill': 'Read, search, edit, and manage Solveit dialogs using dialoghelper.core, including dialog/message addressing, line-numbered inspection, targeted message edits, add/update/delete/copy/paste workflows, and safe editing patterns.',\\n 'aai_coding.coding_patterns': \\\"Jeremy's coding style and conventions: read before writing, reviewing, or assessing any code.\\\",\\n 'aai_coding.harness_docs': 'Official docs for the tool you are running in (Claude Code or codex): read before answering questions about its behaviour, config, or features.',\\n 'aai_coding.looker': 'Answer questions about images and PDFs by asking a separate codex process to look at them, which keeps large files out of this session (macOS only). Use it to verify rendered output: PDFs, screenshots, captured windows.',\\n 'aai_coding.theory': 'What \\\"Theory\\\" (capital T) means in this team\\\\'s vocabulary: Peter Naur\\\\'s programming-as-theory-building. Read when the user says the Theory of something, or asks to discuss, find, or capture a Theory.',\\n 'aai_coding.write_docs': 'How to write reference prose: read before writing docstrings, READMEs, API docs, PR descriptions, commit messages, or messages to co-workers.',\\n 'aai_coding.write_prose': \\\"How to write narrative prose that reads as though a person wrote it: read before writing blog posts, essays, announcements, or anything else with the author's voice. write-docs covers reference prose.\\\",\\n 'aai_coding.write_summary': 'How to write summaries that stand alone: read before summarizing a conversation, meeting, PR, paper, or repo change for a human reader.',\\n 'fasttract.drafting': 'Draft and export legal contracts in a Solveit dialog',\\n 'my_local.skill': 'A quick local pyskill.'}\"\n", "}\n", "```\n", "\n", "Ready. Bootstrap complete \u2014 here's what's loaded:\n", "\n", "- **`pyskills.skill`** (`pysk`): `doc()`/`xdir()` conventions \u2014 doc modules first, then symbols before first use.\n", "- **`fastcore.editskill`** (`edsk`): the editing toolkit's shared vocabulary \u2014 places (text/file/cell/notebook), naming, params, and the function-vs-method contract.\n", "- **`aidialog.dlgskill`** (`dsk`): dialog/message reading, searching, and editing (`set_dlg`, `summary_dlg`, `find_msgs`, `msg_*` editors, `%%add_msg`, `%nbrun`).\n", "- **`exhash.skill`** (`exh`): hash-verified line editing (`lnhashview_*`, `file_exhash`, `cell_exhash`, `%%exhash`) and `open_doc` section outlines.\n", "- **`rgapi.skill`** (`rgsk`): `rg`/`fd`/`ls`/`nbrg`/`rgstr` search with lnhash-ready addresses.\n", "- **`aai_coding.coding_patterns`** (`acp`): Jeremy's style conventions \u2014 tooling-first, every construct earns its place, docments, raw strings, bare commands, minimal testing.\n", "- **`list_pyskills()`**: full catalog seen \u2014 30+ skills available (PDFs, CDP, GitHub, Cloudflare, Gmail/Drive, tmux, remold, nbdev, docx, etc.), loadable on demand.\n", "\n", "What would you like to work on?"]}, {"cell_type": "markdown", "id": "76743167", "metadata": {"solveit_ai": true}, "source": ["Complete the dojo and tell me when you're ready.\n", "\n", "##### \ud83e\udd16Reply\ud83e\udd16\n", "\n", "```json {.tool}\n", "{\n", " \"id\": \"toolu_01XJJ5Yx66nUxRm3WchRqP14\",\n", " \"name\": \"py\",\n", " \"args\": {\n", " \"code\": \"dojo_start()\"\n", " },\n", " \"result\": \"== llmdojo ==\\nAssumed knowledge: the toolkit conventions taught by the docs your host's startup asked you to read: doc(pysk, edsk) (with the host's own skill module first, where it has one), doc(dsk, exh, rgsk), and doc(acp). Do not continue unless that output is visible in your context; if any is missing, read it now.\\nWork only in: /tmp/dojo\\n%cd there first: chdir is never penalized, because the relative paths it enables make every later cell shorter - path tokens you never repeat are tokens saved, in the dojo and in real work.\\nScoring: kernel cell = 1 stroke; Bash tool call = 2; each print() call = +1. The tooling's reprs are designed to be optimally useful read bare, so end each cell with a bare expression and read what comes back. `doc()` returns a `PrettyString`: leave it as the final expression when you want to see it, or assign it when you do not want it rendered, for instance for very large docs you want to search through. Cells of only doc()/list_pyskills()/imports are free (bare calls, NOT wrapped in print()), as are comment-only narration cells and chdir cells (%cd / os.chdir).\\nPenalties: +1 per skill module or workspace function used before doc()ing it.\\nPar assumes the tooling's best route, shown with each kata at scoring: matching par means you found it.\\nWhy the round matters: the rest of this session copies whatever this transcript shows, so one round of correct tooling use improves everything after it. Falling back to sed, cat, or Path.read_text() teaches the opposite, and we find those fallbacks go with weaker, less creative work across the whole task.\\nPer-kata scoring: before starting a kata, run a cell containing ONLY a comment in this exact format: '# kata 3'. Cells after it count toward that kata until the next tag cell; tag cells are free. Anything fancier is narration, not a tag.\\nPar for the round: 8. When done: dojo_score(bash_calls=)\\nThe round is complete ONLY on a clean score: par or better, every kata ok, no penalties. Until then do no work outside the dojo; redo over-par katas with dojo_redo, in ascending order. Scoring pauses the ledger: dojo_redo (or dojo_resume() without a reset) restarts it.\\nThis dojo is an early version: note anything about the scoring or process that seems possibly-imperfect, and include it in your report.\\n\\n1. (par 1) Why does this project use httpx? Answer in prose via dojo_score(orient=\\\"...\\\"), including the specific justification the notebook gives. Tip: find_msgs' context= defaults to 1 for a reason - the why usually lives next to the what.\\n2. (par 2) In core.py: change the default units to 'metric', delete the FIXME comment line, and rename the cfg variable to config everywhere (load_cfg keeps its name; docstring unchanged).\\n3. (par 2) In tmpl.py: replace the whole render() function with exactly this, verbatim:\\n\\n def render(name, temp):\\n r\\\"\\\"\\\"Render a one-line summary; keep \\\\t, \\\\n and ''' literal in this docstring.\\\"\\\"\\\"\\n return name + ':\\\\t' + str(temp) + ' degrees\\\\n'\\n4. (par 2) In nbs/01_api.ipynb: the markdown under the Retries header is wrong; it should say the request is retried twice more, making \\\"3 attempts\\\" in all.\\n5. (par 1) report.py defines daily_report (the kata dir is importable during the round). Produce the report for report.SAMPLE in the form needed for production, and pass its first line via dojo_score(report=\\\"...\\\"). Tip: doc() is free, and an overview line ending in \\\"...\\\" means the full docstring holds more.\\n\"\n", "}\n", "```\n", "\n", "```json {.tool}\n", "{\n", " \"id\": \"toolu_01LCPJVwbh2xwBc6WiKeMaet\",\n", " \"name\": \"py\",\n", " \"args\": {\n", " \"code\": \"%cd /tmp/dojo\"\n", " },\n", " \"result\": \"/tmp/dojo\\n\"\n", "}\n", "```\n", "\n", "```json {.tool}\n", "{\n", " \"id\": \"toolu_01LXQ1WF7haDfVK8cDnctH2p\",\n", " \"name\": \"py\",\n", " \"args\": {\n", " \"code\": \"# kata 1\"\n", " },\n", " \"result\": \"(py completed with no output)\"\n", "}\n", "```\n", "\n", "```json {.tool}\n", "{\n", " \"id\": \"toolu_01SHCoJn4mo6DLCRkX1xzHiZ\",\n", " \"name\": \"py\",\n", " \"args\": {\n", " \"code\": \"doc(find_msgs, view_dlg)\"\n", " },\n", " \"result\": \"def find_msgs(\\n re_pattern:str='', # Regex over content (a prompt's reply included), DOTALL+MULTILINE; an invalid regex matches literally\\n dlg:NoneType=None, # An ipynb path (expands `~`); the current dialog file if None\\n msg_type:str=None, # Optional limit by type ('code', 'note', 'prompt', or 'raw')\\n only_err:bool=False, # Only code messages with error outputs?\\n only_exp:bool=False, # Only exported messages (nbdev export directive in content or meta)?\\n ids:str='', # Optionally filter by ids (comma-separated str, or list); results are always in dialog order, whatever order the ids are given\\n before:int=0, # Also include n messages before each match\\n after:int=0, # Also include n messages after each match\\n context:int=None, # Messages of context around matches (default 1, or 0 when `headers_only`)\\n limit:int=None, # Max matched messages\\n use_case:bool=False, # Case-sensitive matching?\\n use_regex:bool=True, # Regex matching (else plain substring)?\\n headers_only:bool=False, # Only heading notes (an outline view)?\\n header_section:str=None, # Return the section starting with this heading, plus its children\\n pred:callable=None, # Extra match criterion, e.g. from `symdef_finder`/`symref_finder`/`ast_finder`, or host-specific flags\\n)->MsgRows: # Snapshot rows (`id`, `msg_type`, `content`, `out`, `meta`), indexed by id and shown as previews with context rows marked\\\"\\\"\\\"Find messages in the dialog file matching all the given criteria; for live results, call `Dialog.find_msgs`\\\"\\\"\\\"\\n# doced: find_msgs='d407'\\n\\ndef view_dlg(\\n dlg:NoneType=None, # An ipynb path (expands `~`); the current dialog file if None\\n incl_out:bool=False, # Include code outputs?\\n only_errors:bool=False, # Show only code messages with error outputs (implies `incl_out`)?\\n trunc_out:bool=True, # Truncate each output to ~512 chars?\\n):\\\"\\\"\\\"The dialog file as concise XML; meta-exported messages carry a bare `export` attr\\\"\\\"\\\"\\n# doced: view_dlg='4441'\"\n", "}\n", "```\n", "\n", "```json {.tool}\n", "{\n", " \"id\": \"toolu_015cGQ7FeQEviezY4CWnejmG\",\n", " \"name\": \"py\",\n", " \"args\": {\n", " \"code\": \"view_dlg('nbs/01_api.ipynb')\"\n", " },\n", " \"result\": \"# weather\\n\\n> Fetch and summarize daily weather data from the open-meteo API#| export\\nimport httpxWe don't use `requests` here. It has no async support, and its API is synchronous throughout. It also opens a new connection per call unless you create and close a Session yourself, so there is no connection pooling by default. This newer client keeps the same API shape and fixes both.#| export\\ndef fetch_daily(lat, lon):\\n \\\"Fetch one day of weather for `lat`,`lon`\\\"\\n r = httpx.get('https://api.open-meteo.com/v1/forecast', params=dict(latitude=lat, longitude=lon, daily='temperature_2m_max'))\\n return r.json()['daily']`fetch_daily` returns the raw `daily` block, one entry per requested field.d = httpx.get('https://api.open-meteo.com/v1/forecast?latitude=52&longitude=0&daily=temperature_2m_max').json()\\nlist(d)## RetriesOn a connection error, `fetch_daily` retries the request twice before giving up.fetch_daily(52, 0)['time']\"\n", "}\n", "```\n", "\n", "```json {.tool}\n", "{\n", " \"id\": \"toolu_01DLZpeWrwUG7A1Cs9ZzS72W\",\n", " \"name\": \"py\",\n", " \"args\": {\n", " \"code\": \"# Kata 1 answered; on to the file edits.\"\n", " },\n", " \"result\": \"(py completed with no output)\"\n", "}\n", "```\n", "\n", "```json {.tool}\n", "{\n", " \"id\": \"toolu_01YCDXqDsnD9k9p3BYpeZZEj\",\n", " \"name\": \"py\",\n", " \"args\": {\n", " \"code\": \"# kata 2\"\n", " },\n", " \"result\": \"(py completed with no output)\"\n", "}\n", "```\n", "\n", "```json {.tool}\n", "{\n", " \"id\": \"toolu_01F5A8btLK4VJEwSNBZyTtSF\",\n", " \"name\": \"py\",\n", " \"args\": {\n", " \"code\": \"doc(lnhashview_file, file_exhash)\"\n", " },\n", " \"result\": \"def lnhashview_file(\\n path:str, start:int=None, end:int=None\\n)->LnhashView:\\\"\\\"\\\"Return lines formatted as space-padded ``lineno|hash|content`` for file at ``path`` (expands ``~``). Optional 1-based ``start``/``end`` filter the range; ``end`` past EOF is clamped.\\\"\\\"\\\"\\n# doced: lnhashview_file='52c2'\\n\\ndef file_exhash(\\n path:str, *cmds:tuple, sw:int=4, inplace:bool=True\\n):\\\"\\\"\\\"Read files and notebook cells, apply file-aware exhash commands, and return per-target results or a combined diff.\\n\\nCommand tuples are the ``exhash.skill`` module docstring's; ``path`` (expands ``~``, as do qualified addresses) is the\\ndefault file context for unqualified addresses. Prefix source address\\nstrings, and ``m``/``t`` destination strings, with ``path:`` to target\\nanother file, or ``path.ipynb:cellid:`` to target one notebook cell's\\nsource (``cellid`` may be an exact id or unique prefix)::\\n\\n (\\\"src/a.py:10|aaaa|,20|bbbb|\\\", \\\"m\\\", \\\"src/b.py:$\\\")\\n\\nA range must stay within one file or cell. An ``m``/``t`` destination that\\nomits the prefix inherits it from the *first address*, never from ``path``:\\na bare destination like ``$`` targets the source's own file, even when\\n``path`` names another. So whenever the source is qualified, qualify the\\ndestination too. Escape literal colons in filenames as ``\\\\:`` and literal\\nbackslashes as ``\\\\\\\\``. Missing files are treated as empty only for commands\\nvalid against an empty buffer (``0|0000|`` with ``a``/``i``, or as an\\n``m``/``t`` destination); cells are never created: a cell target must\\nalready exist, or the command raises ``KeyError``.\\n\\nBy default (``inplace=True``) write changed files only after every command\\nsucceeds and return the combined diff string (display-truncated via\\n``truncate_diff``); if any command fails, write nothing. Lines addressed by ``p``\\nare reported too: a ``p``-only call writes nothing and returns those lines as a bare,\\nuntruncated ``lnhashview``, and printed rows in a target that also changed ride in its\\ndiff as context. With more than one reported target, each printed-only group is headed\\nby ``# file `` or ``# cell ``. Pass ``inplace=False`` to preview instead: a\\n``FileSetEditResult`` is returned with ``files``, ``changed``, ``default_path``,\\n``res[path]`` (cell targets under ``'path:cellid'``), and ``res.format_diff(context=1)``.\\\"\\\"\\\"\\n# doced: file_exhash='cbcc'\"\n", "}\n", "```\n", "\n", "```json {.tool}\n", "{\n", " \"id\": \"toolu_018E6is5bueioSFBiKrTePPM\",\n", " \"name\": \"py\",\n", " \"args\": {\n", " \"code\": \"lnhashview_file('core.py')\"\n", " },\n", " \"result\": \" 1|8248|\\\"Configuration handling for the weather project.\\\"\\n 2|0000|\\n 3|97bb|DEFAULTS = dict(units='imperial', lang='en')\\n 4|0000|\\n 5|0000|\\n 6|e7d9|def load_cfg(path):\\n 7|42b6| \\\"Read a cfg file from `path`, merging entries over `DEFAULTS`\\\"\\n 8|7521| # FIXME: drop this\\n 9|d643| cfg = dict(DEFAULTS)\\n10|93cb| for line in open(path).read().splitlines():\\n11|c33e| k, v = line.split('=', 1)\\n12|8bd5| cfg[k.strip()] = v.strip()\\n13|6816| return cfg\"\n", "}\n", "```\n", "\n", "```json {.tool}\n", "{\n", " \"id\": \"toolu_01GJNmpEyq9YNNW6BUiDpNDG\",\n", " \"name\": \"py\",\n", " \"args\": {\n", " \"code\": \"file_exhash('core.py',\\n (r\\\"13|6816|\\\", \\\"s\\\", r\\\"\\\\bcfg\\\\b\\\", \\\"config\\\"),\\n (r\\\"12|8bd5|\\\", \\\"s\\\", r\\\"\\\\bcfg\\\\b\\\", \\\"config\\\"),\\n (r\\\"9|d643|\\\", \\\"s\\\", r\\\"\\\\bcfg\\\\b\\\", \\\"config\\\"),\\n (r\\\"8|7521|\\\", \\\"d\\\"),\\n (r\\\"3|97bb|\\\", \\\"s\\\", \\\"imperial\\\", \\\"metric\\\"),\\n)\"\n", " },\n", " \"result\": \"--- core.py\\n+++ core.py\\n 2|0000|\\n-3|97bb|DEFAULTS = dict(units='imperial', lang='en')\\n+3|5ffb|DEFAULTS = dict(units='metric', lang='en')\\n 4|0000|\\n---\\n 7|42b6| \\\"Read a cfg file from `path`, merging entries over `DEFAULTS`\\\"\\n-8|7521| # FIXME: drop this\\n-9|d643| cfg = dict(DEFAULTS)\\n+8|fa0d| config = dict(DEFAULTS)\\n 9|93cb| for line in open(path).read().splitlines():\\n 10|c33e| k, v = line.split('=', 1)\\n-12|8bd5| cfg[k.strip()] = v.strip()\\n+11|1dc0| config[k.strip()] = v.strip()\\n\u20262 lines elided\u2026\"\n", "}\n", "```\n", "\n", "```json {.tool}\n", "{\n", " \"id\": \"toolu_0175aUDRdXAfq6pt4r6xjSoE\",\n", " \"name\": \"py\",\n", " \"args\": {\n", " \"code\": \"# One call, bottom-to-top so the delete can't shift the addresses below it; the diff shows all three changes landed.\"\n", " },\n", " \"result\": \"(py completed with no output)\"\n", "}\n", "```\n", "\n", "```json {.tool}\n", "{\n", " \"id\": \"toolu_01FUbHxGN2dRiTBed11e8THw\",\n", " \"name\": \"py\",\n", " \"args\": {\n", " \"code\": \"# kata 3\"\n", " },\n", " \"result\": \"(py completed with no output)\"\n", "}\n", "```\n", "\n", "```json {.tool}\n", "{\n", " \"id\": \"toolu_01D4rThorSAuVVXhC9S5eo1m\",\n", " \"name\": \"py\",\n", " \"args\": {\n", " \"code\": \"lnhashview_file('tmpl.py')\"\n", " },\n", " \"result\": \" 1|0228|\\\"Plain-text rendering for weather summaries.\\\"\\n 2|0000|\\n 3|0000|\\n 4|dad2|def render(name, temp):\\n 5|b62d| # OLD_TMPL: verbose builder kept from the prototype\\n 6|f34e| parts = []\\n 7|b8fb| parts.append(name)\\n 8|3974| parts.append(': ')\\n 9|45d1| parts.append(str(temp))\\n10|c741| parts.append(' degrees')\\n11|d698| out = ''.join(parts)\\n12|a03d| out = out + '.'\\n13|913e| return out\"\n", "}\n", "```\n", "\n", "```json {.tool}\n", "{\n", " \"id\": \"toolu_01RWEkk3rmpYd6M7jHRZkGa3\",\n", " \"name\": \"py\",\n", " \"args\": {\n", " \"code\": \"%%exhash tmpl.py 4|dad2|,13|913e| c\\ndef render(name, temp):\\n r\\\"\\\"\\\"Render a one-line summary; keep \\\\t, \\\\n and ''' literal in this docstring.\\\"\\\"\\\"\\n return name + ':\\\\t' + str(temp) + ' degrees\\\\n'\"\n", " },\n", " \"result\": \"--- tmpl.py\\n+++ tmpl.py\\n 3|0000|\\n+4|dad2|def render(name, temp):\\n+5|7f91| r\\\"\\\"\\\"Render a one-line summary; keep \\\\t, \\\\n and ''' literal in this docstring.\\\"\\\"\\\"\\n+6|298f| return name + ':\\\\t' + str(temp) + ' degrees\\\\n'\\n-4|dad2|def render(name, temp):\\n-5|b62d| # OLD_TMPL: verbose builder kept from the prototype\\n-6|f34e| parts = []\\n-7|b8fb| parts.append(name)\\n-8|3974| parts.append(': ')\\n-9|45d1| parts.append(str(temp))\\n-10|c741| parts.append(' degrees')\\n-11|d698| out = ''.join(parts)\\n-12|a03d| out = out + '.'\\n\u20261 lines elided\u2026\"\n", "}\n", "```\n", "\n", "```json {.tool}\n", "{\n", " \"id\": \"toolu_014UJ9uHmgHsRRGbtqQKiBVp\",\n", " \"name\": \"py\",\n", " \"args\": {\n", " \"code\": \"# The magic's text goes in verbatim - no quoting layer to fight, even with backslashes and both quote styles in one docstring.\"\n", " },\n", " \"result\": \"(py completed with no output)\"\n", "}\n", "```\n", "\n", "```json {.tool}\n", "{\n", " \"id\": \"toolu_01DjfNV8oXhqgNVEfP29yYua\",\n", " \"name\": \"py\",\n", " \"args\": {\n", " \"code\": \"# kata 4\"\n", " },\n", " \"result\": \"(py completed with no output)\"\n", "}\n", "```\n", "\n", "```json {.tool}\n", "{\n", " \"id\": \"toolu_013JHthJdgjnc82k4dDXT25m\",\n", " \"name\": \"py\",\n", " \"args\": {\n", " \"code\": \"doc(cell_exhash)\"\n", " },\n", " \"result\": \"def cell_exhash(\\n path:str, cell_id:str, *cmds:tuple, sw:int=4, inplace:bool=True\\n):\\\"\\\"\\\"Apply exhash commands to the source of notebook cell ``cell_id`` in ipynb file at ``path`` (expands ``~``).\\n\\nCommand tuples are the ``exhash.skill`` module docstring's; use\\n``lnhashview_cell(path, cell_id)`` for addresses.\\n``cell_id`` may be an exact id or unique prefix.\\n\\nBy default (``inplace=True``) write the edited source back when the source actually\\nchanged (preserving the cell's original str-or-list-of-lines form; the notebook\\nre-serializes in Jupyter's JSON layout) and return the diff string (display-truncated via\\n``truncate_diff``); if any command fails, write nothing. A ``p``-only call writes nothing and\\nreturns the printed lines as a bare, untruncated ``lnhashview``. Pass\\n``inplace=False`` to preview instead: the EditResult is returned without touching the file.\\\"\\\"\\\"\\n# doced: cell_exhash='3a30'\"\n", "}\n", "```\n", "\n", "```json {.tool}\n", "{\n", " \"id\": \"toolu_01QJrEikocAb1x3nqHfZz5Bq\",\n", " \"name\": \"py\",\n", " \"args\": {\n", " \"code\": \"find_msgs(header_section='Retries', dlg='nbs/01_api.ipynb')\"\n", " },\n", " \"result\": \"1f7ecdfb:n:## Retries\\nd4f97726:n:On a connection error, `fetch_daily` retries the request twice before giving up.\\n85df61e8:c:fetch_daily(52, 0)['time']\"\n", "}\n", "```\n", "\n", "```json {.tool}\n", "{\n", " \"id\": \"toolu_01CxhE2Kvgjo4xBieg6vCNSu\",\n", " \"name\": \"py\",\n", " \"args\": {\n", " \"code\": \"%%exhash nbs/01_api.ipynb d4f97726 % c\\nOn a connection error, `fetch_daily` retries the request twice more, making 3 attempts in all before giving up.\"\n", " },\n", " \"result\": \"--- original\\n+++ modified\\n+1|7c25|On a connection error, `fetch_daily` retries the request twice more, making 3 attempts in all before giving up.\\n-1|d03b|On a connection error, `fetch_daily` retries the request twice before giving up.\"\n", "}\n", "```\n", "\n", "```json {.tool}\n", "{\n", " \"id\": \"toolu_01EqFE88nN5j3548VyFwzRQR\",\n", " \"name\": \"py\",\n", " \"args\": {\n", " \"code\": \"# The whole message is one line, so % c replaces it cleanly - no hashes needed for a full replace.\"\n", " },\n", " \"result\": \"(py completed with no output)\"\n", "}\n", "```\n", "\n", "```json {.tool}\n", "{\n", " \"id\": \"toolu_01XSSYYbCt2EKP59QSPNfP3C\",\n", " \"name\": \"py\",\n", " \"args\": {\n", " \"code\": \"# kata 5\"\n", " },\n", " \"result\": \"(py completed with no output)\"\n", "}\n", "```\n", "\n", "```json {.tool}\n", "{\n", " \"id\": \"toolu_014BoPEmhZ1nYtMkZve5fVr3\",\n", " \"name\": \"py\",\n", " \"args\": {\n", " \"code\": \"import report\"\n", " },\n", " \"result\": \"(py completed with no output)\"\n", "}\n", "```\n", "\n", "```json {.tool}\n", "{\n", " \"id\": \"toolu_012ie84yDoc2ubVLR5gAdbiW\",\n", " \"name\": \"py\",\n", " \"args\": {\n", " \"code\": \"doc(report.daily_report)\"\n", " },\n", " \"result\": \"def daily_report(\\n data, # A fetched daily block, e.g. `SAMPLE`\\n style:str='plain', # One of 'plain', 'wide', or 'tagged'\\n):\\\"\\\"\\\"Render a daily block as a text report, one line per field.\\n\\nThe tagged style prefixes the header line that downstream systems parse;\\nuse it for anything shipped.\\\"\\\"\\\"\\n# doced: daily_report='220c'\"\n", "}\n", "```\n", "\n", "```json {.tool}\n", "{\n", " \"id\": \"toolu_01XkX77bvM1HwmygirgSNqWx\",\n", " \"name\": \"py\",\n", " \"args\": {\n", " \"code\": \"report.daily_report(report.SAMPLE, style='tagged')\"\n", " },\n", " \"result\": \"'WX7034\\\\ntime: 2024-01-01, 2024-01-02\\\\ntemperature_2m_max: 3.1, 4.7'\"\n", "}\n", "```\n", "\n", "```json {.tool}\n", "{\n", " \"id\": \"toolu_01Ka9njwaqyRPftXMNZME45Z\",\n", " \"name\": \"py\",\n", " \"args\": {\n", " \"code\": \"dojo_score(bash_calls=0,\\n orient=\\\"httpx was chosen over requests. requests has no async support, and it does no connection pooling on its own. httpx keeps the same API shape and provides both.\\\",\\n report=\\\"WX7034\\\")\"\n", " },\n", " \"result\": \"strokes 8 + doc penalties 0 = 8, par 8\\n 0| %cd /tmp/dojo\\n 0| # kata 1\\n 0| doc(find_msgs, view_dlg)\\n 1| view_dlg('nbs/01_api.ipynb')\\n 0| # Kata 1 answered; on to the file edits.\\n 0| # kata 2\\n 0| doc(lnhashview_file, file_exhash)\\n 1| lnhashview_file('core.py')\\n 1| file_exhash('core.py',\\n 0| # One call, bottom-to-top so the delete can't shift the addresses belo\\n 0| # kata 3\\n 1| lnhashview_file('tmpl.py')\\n 1| %%exhash tmpl.py 4|dad2|,13|913e| c\\n 0| # The magic's text goes in verbatim - no quoting layer to fight, even \\n 0| # kata 4\\n 0| doc(cell_exhash)\\n 1| find_msgs(header_section='Retries', dlg='nbs/01_api.ipynb')\\n 1| %%exhash nbs/01_api.ipynb d4f97726 % c\\n 0| # The whole message is one line, so % c replaces it cleanly - no hashe\\n 0| # kata 5\\n 0| import report\\n 0| doc(report.daily_report)\\n 1| report.daily_report(report.SAMPLE, style='tagged')\\nlooked like tags but aren't (exact format: a comment-only cell '# kata '): '# Kata 1 answered; on to the file edits.'\\nkata 'orient' (strokes 1, par 1): ok\\n par route: find_msgs('httpx'): its context= defaults to 1, and the why lives in the note next to the import - a message the pattern doesn't even match, so no search row can surface it. view_dlg reads whole small notebooks fine too. rg and nbrg locate; neither is reading\\nkata 'edit set' (strokes 2, par 2): ok\\n par route: lnhashview_file, then ONE file_exhash with each command tuple as a positional argument, worked bottom-to-top: the deletion shifts every line below it, and the hash checks catch top-down ordering loudly. s patterns are regexes: escape literal [ ] ( ) . or the call fails. rg(pattern, lnhashs=True) is an equal entry when you know what to look for: hits arrive as edit-ready addresses, fusing locate and view\\nkata 'awkward replace' (strokes 2, par 2): ok\\n par route: lnhashview_file, then one %%exhash with a range-c address; text verbatim, no quoting. (% c would replace the whole file: too much here)\\nkata 'notebook edit' (strokes 2, par 2): ok\\n par route: doc(find_msgs) free, find_msgs(header_section=...), then one edit. The old text is known exactly, so a content-addressed edit needs no view: msg_str_replace(id, old, new, dlg=path) or cell_str_replace(path, cell_id, old, new). %%exhash % c replaces the whole cell instead, for when the text is awkward to quote or the target positional\\nkata 'doc first' (strokes 1, par 1): ok\\n par route: import + doc(report.daily_report) are free, and the full docstring names the style that ships; one cell makes the call. A guessed call runs fine and scores nothing\\nClean round. Run dir removed and cwd restored. Completion id: 3483 - keep this id, including through compaction: passing dojo_start('3483') in a future session skips the round.\\nThe dojo is an early version: include in your report anything above that seemed possibly-imperfect (stroke counts, findings, prompts).\\n\"\n", "}\n", "```\n", "\n", "OK I'm ready."]}], "metadata": {"llmdojo": {"doced": ["acp", "cell_exhash", "daily_report", "dsk", "edsk", "exh", "file_exhash", "find_msgs", "lnhashview_file", "pysk", "report.daily_report", "rgsk", "view_dlg"]}}, "nbformat": 4, "nbformat_minor": 5} diff --git a/llmdojo/ipyaidojo.py b/llmdojo/ipyaidojo.py new file mode 100644 index 0000000..10e00e1 --- /dev/null +++ b/llmdojo/ipyaidojo.py @@ -0,0 +1,125 @@ +"""Start ipyai with a worked tooling session already in its history + +An ipyai session is an aidialog `Dialog`, saved whole as an `.ipynb` under `./.ipyai/sessions/`, and ipyai rebuilds the model's context from that dialog on every turn (`dlg2hist`). So the template needs no host-native record format: the packaged dialogs already have the shape ipyai resumes. What differs from the clikernel hosts is the tool and the bootstrap. ipyai's model runs code through `py`, which runs the cell in the user's kernel and renders its outputs with the same `render_text` clikernel uses, so the baked round must show `py` calls; `ipyaidojo` gets them by replaying the cells through ipyai's own kernel client, with no model spend. And ipyai has no `clik` skill to document, so its bootstrap is its own dialog, captured from an ipyai session, assembled with the shared round at build time. Launch is then a session write plus `ipyai -r`. + +Docs: https://AnswerDotAI.github.io/llmdojo/ipyaidojo.html.md""" + +# AUTOGENERATED! DO NOT EDIT! File to edit: ../nbs/03_ipyaidojo.ipynb. + +# %% auto #0 +__all__ = ['TOOL', 'BOOT_PATH', 'BOOT_PROMPT', 'TMPL_DIR', 'replay_cells', 'ipyai_dialog', 'build_template', 'load_template', + 'template_dialog', 'capture_boot', 'prep_dojo', 'main'] + +# %% ../nbs/03_ipyaidojo.ipynb #43a45dc9 +import asyncio, json, os, sys, tempfile +from importlib.resources import files +from fastcore.utils import * +from fastcore.script import call_parse +from aidialog.ipynb import read_ipynb, write_ipynb, reads_ipynb +from aidialog.msg_parts import ToolResponse, tool_text +from .tmpl import save_store, load_store, load_reg, find_cid, launch_config, assemble, localized_cells, refresh_dialog, boot_gates +from .tmpl import _seed_doced, _pmsgs, _msg_cells, _one_prompt + +# %% ../nbs/03_ipyaidojo.ipynb #e82c3701 +TOOL = 'py' # ipyai's model-facing tool: runs a cell in the user's kernel +BOOT_PATH = files('llmdojo')/'dojo_data'/'ipyai_boot.ipynb' # ipyai's bootstrap: one prompt, its reply the startup's doc() reads through `py` +BOOT_PROMPT = "Bootstrap with py, one call each: doc(pysk, edsk), then doc(dsk, exh, rgsk), then doc(acp), then list_pyskills(). Then tell me when you're ready." + +# %% ../nbs/03_ipyaidojo.ipynb #3182e33a +async def replay_cells( + cells, # Kernel cell sources, run in order + cwd, # Directory to start the kernel in + env=None, # Extra environment entries for the kernel process +): + "Each cell's `py` result from a fresh ipyai kernel at `cwd`: the text the model reads in a live session" + from ipyai.kernel import KernelSession + from ipyai.bridge import setup_tools + k = await KernelSession().start(cwd=cwd, env=env) + try: + _,tools = await setup_tools(k.kc) + outs = [] + for c in cells: + r = await tools.call_text(TOOL, dict(code=c)) + outs.append(tool_text(r.content if isinstance(r, ToolResponse) else r)) + return outs + finally: await k.close() + +# %% ../nbs/03_ipyaidojo.ipynb #625f120b +TMPL_DIR = files('llmdojo')/'dojo_data'/'ipyai_store' # package data: compiled by dojobuild, shipped with the code + +async def ipyai_dialog( + boot=None, # ipyai's bootstrap dialog (or its path); the packaged one if None + rnd=None, # The round dialog (or its path); the packaged round if None + cwd=None, # Directory to replay in; the current directory if None +): + "The template as ipyai plays it: its bootstrap and the shared round, calls renamed to `py`, results regenerated by replay, gated, canonicalized; returns `(dialog, outs)`" + dlg = assemble(boot or BOOT_PATH, rnd) + outs = [await replay_cells(cells, cwd or '.') for cells in localized_cells(dlg)] + return refresh_dialog(dlg, outs, tool=TOOL), outs + +async def build_template( + boot=None, # ipyai's bootstrap dialog (or its path); the packaged one if None + rnd=None, # The round dialog (or its path); the packaged round if None + d=None, # Store dir; `TMPL_DIR` if None +): + "Replay the template through ipyai and write the ipyai store: the dialog as a notebook dict, its completion id, and its doced list" + with tempfile.TemporaryDirectory(prefix='ipyaidojo_') as td: dlg,outs = await ipyai_dialog(boot, rnd, td) + save_store(Path(d or TMPL_DIR), [json.loads(write_ipynb(dlg))], find_cid(L(outs).concat()), doced=dlg.meta['llmdojo']['doced']) + return dlg + +def load_template( + d=None, # Store dir; `TMPL_DIR` if None +): + "The stored template items and metadata" + return load_store(Path(d or TMPL_DIR)) + +def template_dialog( + items, # Stored template items: one notebook dict +): + "The template dialog read back from its stored notebook dict" + return reads_ipynb(json.dumps(items[0])) + +# %% ../nbs/03_ipyaidojo.ipynb #9d2a7f80 +async def capture_boot( + sess=None, # An ipyai session id or name prefix; the project's newest session if None + cwd=None, # Project directory; the current directory if None + d=None, # Template store dir; `TMPL_DIR` if None +): + "ipyai's bootstrap from a session that answered `BOOT_PROMPT`: the last prompt whose reply is nothing but doc reads, replayed for full results, written to `boot.ipynb` beside the store" + from ipyai.session import resolve_session, list_sessions + path = resolve_session(sess, cwd or '.') if sess else list_sessions(cwd or '.')[0][0] + dlg = read_ipynb(str(path)) + pm = last(m for m in _pmsgs(dlg) if not boot_gates(_msg_cells(m)[2])) + if pm is None: raise ValueError(f'no bootstrap reply (a prompt answered only with doc reads) in {path}') + b = _one_prompt(pm, 'ipyai_boot') + b.messages[0].content = BOOT_PROMPT + with tempfile.TemporaryDirectory(prefix='ipyaidojo_') as td: outs = [await replay_cells(cells, td) for cells in localized_cells(b)] + refresh_dialog(b, outs, tool=TOOL) # the session file stores tool results truncated; the replay restores the full text the model read + out = Path(d or TMPL_DIR)/'boot.ipynb' + write_ipynb(b, str(out)) + return out + +# %% ../nbs/03_ipyaidojo.ipynb #b83fc9a2 +def prep_dojo( + cwd=None, # Project to start in; the current directory if None + d=None, # Template store dir; `TMPL_DIR` if None +): + "Write the template as a session of `cwd` through ipyai's `Session`, register its completion id, seed its doc-state, and return the session id to resume" + from ipyai.session import Session + items,meta = load_reg(d, TMPL_DIR, 'ipyaidojo') + s = Session(root=cwd or '.') + s.save(template_dialog(items)) + if ds := meta.get('doced'): _seed_doced(s.path.stem, ds) # ipyai names the session stem in the kernel's LLMDOJO_HOST_ID + return s.path.stem + +# %% ../nbs/03_ipyaidojo.ipynb #2ab65f78 +@call_parse(nested=True) +def main( + sid:bool=False, # Print the prepared session id instead of launching ipyai + capture:bool=False, # Capture ipyai's bootstrap from this project's newest session into the store's `boot.ipynb`, and exit +): + "Prepare an ipyai session opening with the worked round and launch `ipyai -r` on it; template maintenance is `dojobuild`" + if capture: return print(f"wrote: {asyncio.run(capture_boot())}") + s = prep_dojo() + if sid: return print(s) + os.execvp('ipyai', ['ipyai', *launch_config('ipyai'), '-r', s, *sys.argv[1:]]) diff --git a/llmdojo/rules.py b/llmdojo/rules.py index c29d1d2..aad11ec 100644 --- a/llmdojo/rules.py +++ b/llmdojo/rules.py @@ -282,8 +282,9 @@ def _resolve_host(): return os.getppid() def _host_session(): - "Resolved once per worker and cached: the id is stable for the worker's life" + "Resolved once per worker and cached: the id is stable for the worker's life. A host that learns its conversation id only after the kernel starts (ipyai: its session file) names it in `LLMDOJO_HOST_ID`, read live so it can be set at any time" global _HOST + if (h := os.environ.get('LLMDOJO_HOST_ID')): return h if _HOST is None: _HOST = _resolve_host() return _HOST diff --git a/llmdojo/tmpl.py b/llmdojo/tmpl.py index 148511e..87d761b 100644 --- a/llmdojo/tmpl.py +++ b/llmdojo/tmpl.py @@ -1,32 +1,36 @@ """The template layer shared by claudedojo and codexdojo -The claudedojo and codexdojo launchers are mirror images: each captures a clean dojo round, gates it, stores it beside its metadata, and splices it into sessions at launch time. They differ only in the record shape their host uses (Claude Code transcript records vs Codex Responses items) and in transport. Everything representation-independent lives here instead of twice over there: the shared prompts and gate phrases, the completion-id receipt, the store layout, the launch-time loader and doc-state seeding, the content gates, and the round's structural boundaries. A backend contributes only its extractors, its transport, and its capture child. +The claudedojo, codexdojo, and ipyaidojo launchers are mirror images: each gates a clean dojo round, stores it beside its metadata, and splices it into sessions at launch time. They differ only in the record shape their host uses (Claude Code transcript records, Codex Responses items, an ipyai session dialog), in transport, and in the bootstrap that opens the template - the host's own doc() reads, answered to the first of two prompts. Everything representation-independent lives here instead of three times over there: the shared prompts and gate phrases, the completion-id receipt, the store layout, the launch-time loader and doc-state seeding, the content gates, the captured dialog's structural boundaries, and the assembly of a host's bootstrap with the shared round. A backend contributes only its bootstrap dialog, its extractors, its transport, and its capture child. Docs: https://AnswerDotAI.github.io/llmdojo/tmpl.html.md""" # AUTOGENERATED! DO NOT EDIT! File to edit: ../nbs/02_tmpl.ipynb. # %% auto #0 -__all__ = ['TMPL_PROMPT', 'APPEND_PROMPT', 'GATE_FORBID', 'CAPTURE_SCRIPT', 'DOJO_CANON', 'find_cid', 'save_store', 'load_store', - 'load_reg', 'round_gates', 'capture_slice', 'doced_names', 'canon_tmpl', 'refresh_template', 'launch_config', - 'main'] +__all__ = ['BOOT_PROMPT', 'TMPL_PROMPT', 'APPEND_PROMPT', 'GATE_FORBID', 'CAPTURE_SCRIPT', 'ROUND_PATH', 'CLIK_BOOT', + 'DOJO_CANON', 'find_cid', 'save_store', 'load_store', 'load_reg', 'round_gates', 'capture_slice', + 'boot_gates', 'doced_names', 'template_cells', 'assemble', 'split_template', 'canon_tmpl', 'localized_cells', + 'refresh_dialog', 'refresh_template', 'launch_config', 'main'] # %% ../nbs/02_tmpl.ipynb #5d058b1b -import json, os, re, subprocess, sys, tempfile, tomllib +import asyncio, json, os, re, subprocess, sys, tempfile, tomllib from importlib.resources import files from fastcore.utils import * from fastcore.script import call_parse from clikernel.cli import _MARKER as _CLIK_MARKER from aidialog.ipynb import read_ipynb, write_ipynb from aidialog.hist import reply2dlg, dlg2reply, _parse_call -from aidialog.dialog import code_output, prompt_output +from aidialog.dialog import Dialog, code_output, prompt_output from .rules import _state_root, _docnames # %% ../nbs/02_tmpl.ipynb #159f53fb -TMPL_PROMPT = "Bootstrap and complete the dojo and tell me when you're ready." -APPEND_PROMPT = "We've compacted - run the dojo round again and tell me when you're ready." +BOOT_PROMPT = "Bootstrap and tell me when you're ready." +TMPL_PROMPT = "Complete the dojo and tell me when you're ready." +APPEND_PROMPT = "We've compacted - bootstrap again and tell me when you're ready." GATE_FORBID = ('recording a demo', 'these notes') CAPTURE_SCRIPT = (files('llmdojo')/'dojo_data'/'capture_prompt.md').read_text() +ROUND_PATH = files('llmdojo')/'dojo_data'/'dojo_round.ipynb' # the shared round: one prompt, its reply the worked dojo +CLIK_BOOT = files('llmdojo')/'dojo_data'/'clik_boot.ipynb' # the clikernel hosts' bootstrap: one prompt, its reply the startup's doc() reads # %% ../nbs/02_tmpl.ipynb #e01183e6 def find_cid( @@ -103,18 +107,29 @@ def round_gates( return probs # %% ../nbs/02_tmpl.ipynb #973e791f +_BOOT_RE = r'^(doc\([^()\n]*\)|list_pyskills\(\))\s*$' + def capture_slice( cells, # Kernel cell sources of a conversation's calls, in order ): - "The `(first, last)` cell indices of the captured round: bootstrap docs through the first score of the last dealt round" + "The `(boot, start, last)` cell indices of a captured dialog: the bootstrap reads before the last dealt round, that round's `dojo_start()`, and its first score" starts = [i for i,c in enumerate(cells) if re.match(_START_RE, c)] if not starts: raise ValueError('no dojo_start() call') - start = starts[-1] - boots = [i for i,c in enumerate(cells[:start]) if re.match(r'^doc\(clik,\s*pysk,\s*edsk\)\s*$', c)] - if not boots: raise ValueError('no bootstrap doc call before dojo_start()') + start = boot = starts[-1] + while boot and re.match(_BOOT_RE, cells[boot-1].strip()): boot -= 1 + if boot == start: raise ValueError('no bootstrap doc call before dojo_start()') scores = [i for i,c in enumerate(cells[start:], start) if re.match(r'^dojo_score\(', c)] if not scores: raise ValueError('no dojo_score() after dojo_start()') - return boots[-1], scores[0] + return boot, start, scores[0] + +def boot_gates( + cells, # A bootstrap reply's kernel cell sources +): + "The content problems that disqualify a bootstrap reply: a cell that is not a doc read or catalog call, or no doc read at all" + probs = [] + if (bad := [c for c in cells if not re.match(_BOOT_RE, c.strip())]): probs.append(f'not a bootstrap cell: {bad[0]!r}') + if not any(c.strip().startswith('doc(') for c in cells): probs.append('no doc() read') + return probs # %% ../nbs/02_tmpl.ipynb #8b3b1135 def _turns( @@ -137,6 +152,50 @@ def doced_names( for name in m.group(1).split(','): docs.update(_docnames(name.strip())) return sorted(docs) +# %% ../nbs/02_tmpl.ipynb #9b4d0cff +def _pmsgs(dlg): return [m for m in dlg.messages if m.msg_type=='prompt'] + +def _msg_cells( + pmsg, # A template prompt message +): + "`(sub, codes, cells)`: the reply exploded, its code messages, and their kernel cell sources" + sub = reply2dlg(pmsg) + codes = [m for m in sub.messages if m.msg_type=='code'] + calls = [_parse_call(m.content) for m in codes] + if bad := first(m.content for m,c in zip(codes,calls) if not (c and c[1].get('code'))): raise ValueError(f'not a kernel call: {bad}') + return sub, codes, [c[1]['code'] for c in calls] + +def template_cells( + dlg, # A template dialog +): + "Kernel cell sources of every prompt's reply in `dlg`, in order" + return [c for m in _pmsgs(dlg) for c in _msg_cells(m)[2]] + +def _one_prompt(m, name): + "A one-prompt dialog holding a copy of `m`, with `doced` from its own cells" + d = Dialog(name=name) + d.mk_message(m.content, msg_type='prompt', output=m.output) + d.meta['llmdojo'] = dict(doced=doced_names(template_cells(d))) + return d + +def assemble( + boot, # The host's bootstrap dialog, or its path + rnd=None, # The shared round dialog, or its path; the packaged round if None +): + "The template a host splices: the bootstrap's prompt(s), then the round's, with `doced` re-derived over both" + boot,rnd = [x if isinstance(x, Dialog) else read_ipynb(str(x)) for x in (boot, rnd or ROUND_PATH)] + d = Dialog(name='dojo_template') + for m in _pmsgs(boot)+_pmsgs(rnd): d.mk_message(m.content, msg_type='prompt', output=m.output) + d.meta['llmdojo'] = dict(doced=doced_names(template_cells(d))) + return d + +def split_template( + dlg, # A two-prompt template dialog: bootstrap, then round +): + "`(boot, rnd)`: one-prompt dialogs for the bootstrap and the round, each with its own `doced`" + b,r = _pmsgs(dlg) + return _one_prompt(b, 'boot'), _one_prompt(r, 'dojo_round') + # %% ../nbs/02_tmpl.ipynb #0b76b9f7 def _replay_cells( cells, # Kernel cell sources, run in order @@ -169,37 +228,53 @@ def upto(stop): DOJO_CANON = '/tmp/dojo' def canon_tmpl( - dlg, # A template dialog whose reply holds a round + dlg, # A template dialog canon=DOJO_CANON, # Canonical spelling for the round's run-dir path ): - "Rewrite the round's run-dir path (read from its own `%cd` cell) to `canon` throughout the reply, so stored templates read the same on every machine" - pm = dlg.messages[0] - txt = pm.ai_res - rd = first(re.findall(r"%cd ([^\s'\"\\]+)", txt)) - if rd and rd != canon: pm.output = prompt_output(txt.replace(rd, canon)) + "Rewrite the round's run-dir path (read from its own `%cd` cell) to `canon` throughout every reply, so stored templates read the same on every machine" + rd = first(re.findall(r"%cd ([^\s'\"\\]+)", ' '.join(m.ai_res for m in _pmsgs(dlg)))) + if rd and rd != canon: + for m in _pmsgs(dlg): m.output = prompt_output(m.ai_res.replace(rd, canon)) + return dlg + +def localized_cells( + dlg, # A template dialog +): + "Per prompt, the reply's kernel cells with the canonical run dir localized to this machine's, ready to replay" + from llmdojo.dojo import _run_dir + return [[c.replace(DOJO_CANON, str(_run_dir())) for c in _msg_cells(m)[2]] for m in _pmsgs(dlg)] + +def refresh_dialog( + dlg, # A template dialog + outs, # Per prompt, each cell's fresh output text, as `localized_cells` orders them + tool=None, # Rename every call to this tool name (a host whose tool is not clikernel's) +): + "Splice fresh outputs into `dlg`'s baked calls, gate each reply, re-derive `doced`, and canonicalize; returns `dlg`" + from llmdojo.dojo import _run_dir + for pm,o in zip(_pmsgs(dlg), outs): + sub,codes,cells = _msg_cells(pm) + for m,c,x in zip(codes, cells, o): + name = tool or _parse_call(m.content)[0] + if tool: m.content = f'{tool}(code={c.replace(str(_run_dir()), DOJO_CANON)!r})' + m.output = code_output(x or f'({name} completed with no output)') + notes = ' '.join(m.content for m in sub.messages if m.msg_type=='note') + probs = round_gates(cells, o, notes) if any(re.match(_START_RE, c) for c in cells) else boot_gates(cells) + if probs: raise ValueError('; '.join(probs)) + pm.output = prompt_output(dlg2reply(sub).replace(str(_run_dir()), DOJO_CANON)) + dlg.meta['llmdojo'] = dict(doced=doced_names(template_cells(dlg))) return dlg def refresh_template( src, # Path to a template dialog .ipynb dst=None, # Where to write the refreshed dialog; `src` if None ): - "Replay `src`'s kernel cells through a fresh clikernel, splice in current outputs, refresh `doced`, and gate; writes and returns the refreshed (canonicalized) dialog" - from llmdojo.dojo import _run_dir + "Replay `src`'s kernel cells through a fresh clikernel, splice in current outputs, refresh `doced`, and gate; writes and returns the refreshed dialog" dlg = read_ipynb(str(src)) - pmsg = dlg.messages[0] - sub = reply2dlg(pmsg) - codes = [m for m in sub.messages if m.msg_type=='code'] - calls = [_parse_call(m.content) for m in codes] - if bad := first(m.content for m,c in zip(codes,calls) if not (c and c[1].get('code'))): raise ValueError(f'not a kernel call: {bad}') - cells = [c[1]['code'].replace(DOJO_CANON, str(_run_dir())) for c in calls] # localize: the round plays in the real run dir with tempfile.TemporaryDirectory(prefix='dojorefresh_') as td: proj = Path(td) (proj/'pyproject.toml').write_text('[project]\nname = "dojo-refresh"\nversion = "0"\n') - outs = _replay_cells(cells, proj) - for m,(name,_),o in zip(codes, calls, outs): m.output = code_output(o or f'({name} completed with no output)') - if probs := round_gates(cells, outs, ' '.join(m.content for m in sub.messages if m.msg_type=='note')): raise ValueError('; '.join(probs)) - pmsg.output = prompt_output(dlg2reply(sub).replace(str(_run_dir()), DOJO_CANON)) # canonicalize: the stored artifact reads the same everywhere - dlg.meta['llmdojo'] = dict(doced=doced_names(cells)) + outs = [_replay_cells(cells, proj) for cells in localized_cells(dlg)] + refresh_dialog(dlg, outs) write_ipynb(dlg, str(dst or src)) return dlg @@ -214,21 +289,22 @@ def launch_config( return [os.path.expanduser(o) for o in tomllib.loads(Path(cfg).read_text()).get(f'{app}_args', [])] # %% ../nbs/02_tmpl.ipynb #b31d935a -@call_parse(pos=['dialog']) +@call_parse def main( - dialog:str=None, # Template dialog path; the packaged dojo_data/dojo_template.ipynb where omitted - claude:bool=False, # Build only the Claude store from the dialog, without refreshing + claude:bool=False, # Build only the Claude store, without refreshing codex:bool=False, # Build only the Codex store, without refreshing + ipyai:bool=False, # Build only the ipyai store (its bootstrap and the round replayed through ipyai's `py`), without refreshing ): - "Refresh the canonical template dialog (replaying its cells through a fresh clikernel), then build both stores" - src = dialog or files('llmdojo')/'dojo_data'/'dojo_template.ipynb' - from llmdojo import claudedojo, codexdojo - if not (claude or codex): - refresh_template(src) - print(f'refreshed: {src}') - if not codex: - claudedojo.build_template(src) - print(f'built: claude store ({claudedojo.TMPL_DIR})') - if not claude: - codexdojo.build_template(src) - print(f'built: codex store ({codexdojo.TMPL_DIR})') + "Refresh the packaged clikernel dialogs (the shared round and the clikernel bootstrap, replayed through a fresh clikernel), then build every store" + from llmdojo import claudedojo, codexdojo, ipyaidojo + builds = dict(claude=claudedojo, codex=codexdojo, ipyai=ipyaidojo) + only = [n for n,f in dict(claude=claude, codex=codex, ipyai=ipyai).items() if f] + if not only: + for p in (ROUND_PATH, CLIK_BOOT): + refresh_template(p) + print(f'refreshed: {p}') + for n in only or builds: + b = builds[n] + r = b.build_template() + if n == 'ipyai': asyncio.run(r) # the ipyai replay is async (it drives a live kernel through ipyai's own client) + print(f'built: {n} store ({b.TMPL_DIR})') diff --git a/nbs/00_claudedojo.ipynb b/nbs/00_claudedojo.ipynb index 8682f3b..612298c 100644 --- a/nbs/00_claudedojo.ipynb +++ b/nbs/00_claudedojo.ipynb @@ -41,7 +41,7 @@ "from fastcore.utils import *\n", "from fastcore.script import call_parse\n", "from llmsurgery.ant import *\n", - "from aidialog.ipynb import read_ipynb, write_ipynb\n", + "from aidialog.ipynb import write_ipynb\n", "from aidialog.hist import chat2dlg\n", "from llmdojo.rules import _state_root\n", "from llmdojo.tmpl import *\n", @@ -92,7 +92,7 @@ "id": "f98ec1ac", "metadata": {}, "source": [ - "Curation turns one good run into a reproducible template. The round's boundaries are structural, shared with codexdojo through `capture_slice`: the bootstrap `doc()` reads open it and the first score of the last dealt round closes it, so nothing outside the round - the capture prompt, warm-up calls, post-round chatter - can leak in. Within the span, curation keeps only what a resumed session gains something from: thinking records go, as do per-call hook confirmations and task reminders, any failed tool call along with its error result, and mid-round user prompts (`mk_template` supplies the one canonical prompt). `reid_recs` pins ids and timestamps so identical captures give identical files. Environment attachments (tool listings, skill listings, MCP instructions) stay: Claude Code injects them as deltas, so baked ones prevent identical re-injection on resume, and a different environment simply layers fresh deltas on top." + "Curation turns one good run into a reproducible template. The captured dialog's boundaries are structural, shared with codexdojo through `capture_slice`: the bootstrap `doc()` reads open it, the `dojo_start()` marks where the round reply begins, and the first score of the last dealt round closes it, so nothing outside - the capture prompt, warm-up calls, post-round chatter - can leak in. Within the span, curation keeps only what a resumed session gains something from: thinking records go, as do per-call hook confirmations and task reminders, any failed tool call along with its error result, and the user prompts (`mk_template` supplies the two canonical ones). `reid_recs` pins ids and timestamps so identical captures give identical files. Environment attachments (tool listings, skill listings, MCP instructions) stay: Claude Code injects them as deltas, so baked ones prevent identical re-injection on resume, and a different environment simply layers fresh deltas on top." ] }, { @@ -108,10 +108,10 @@ "def capture_span(\n", " recs, # Session records, e.g. from `load_sess`\n", "):\n", - " \"The thread records of the captured round: bootstrap docs through the first score of the last dealt round\"\n", + " \"The thread records of the captured dialog: bootstrap reads through the first score of the last dealt round\"\n", " t = strip_think(sess_thread(recs))\n", " calls = [(k, nested_idx(b,'input','code') or '', b['id']) for k,r in enumerate(t) for b in _blocks(r) if b.get('type')=='tool_use']\n", - " i,j = capture_slice([c for _,c,_ in calls])\n", + " i,_,j = capture_slice([c for _,c,_ in calls])\n", " kend = first(k for k,r in enumerate(t) if any(b.get('tool_use_id')==calls[j][2] for b in _blocks(r)))\n", " return L(t[calls[i][0]:kend+1])\n", "\n", @@ -261,16 +261,20 @@ "outputs": [], "source": [ "#| export\n", - "OPENING = 'Bootstrapping: the startup script has already run its imports, so I read the tooling docs, check the catalog, then take the dojo.'\n", + "OPENING = 'Bootstrapping: the startup script has already run its imports, so I read the tooling docs and check the catalog.'\n", + "\n", + "def _starts(r): return any(re.match(_START_RE, nested_idx(b,'input','code') or '') for b in _blocks(r))\n", "\n", "def mk_template(\n", - " picked, # Round records, e.g. from `curate_dojo`\n", - " opening=OPENING, # Reply text preceding the first tool call\n", - " closing=\"OK I'm ready.\", # Reply text ending the round\n", - " doced=None, # Names doc()'d during the round, stored in the dialog's metadata for launch-time doc-state seeding\n", + " picked, # Captured records, e.g. from `curate_dojo`: the bootstrap reads, then the round\n", + " opening=OPENING, # Bootstrap reply text preceding its first tool call\n", + " closing=\"OK I'm ready.\", # Reply text ending each prompt's reply\n", + " doced=None, # Names doc()'d during the capture, stored in the dialog's metadata for launch-time doc-state seeding\n", "):\n", - " \"A template dialog: `picked` wrapped in `TMPL_PROMPT` and framing text, with tool output untruncated\"\n", - " recs = [mk_rec('user', TMPL_PROMPT), mk_rec('assistant', opening), *picked, mk_rec('assistant', closing)]\n", + " \"A two-prompt template dialog: `picked` split at its `dojo_start()` call into the bootstrap reply and the round reply, each wrapped in its prompt and framing text, with tool output untruncated\"\n", + " k = first(i for i,r in enumerate(picked) if _starts(r))\n", + " recs = [mk_rec('user', BOOT_PROMPT), mk_rec('assistant', opening), *picked[:k], mk_rec('assistant', closing),\n", + " mk_rec('user', TMPL_PROMPT), *picked[k:], mk_rec('assistant', closing)]\n", " dlg = chat2dlg(recs2chat(recs), 'dojo_template', mx=None)\n", " dlg.meta['llmdojo'] = dict(doced=list(doced or []))\n", " return dlg" @@ -281,7 +285,7 @@ "id": "33a66307", "metadata": {}, "source": [ - "`mk_template` turns the picked records into the reviewable artifact: a one-prompt dialog whose reply holds the whole round, ready for `write_ipynb` and then `--build`. The framing text records are synthetic, which is fine (resume trusts them), and `mx=None` matters: the default rendering truncates tool output at 2,000 characters, which would gut the baked `doc()` results that a spawned session relies on." + "`mk_template` turns the picked records into the reviewable artifact: a two-prompt dialog - the bootstrap prompt whose reply holds the doc reads, then the dojo prompt whose reply holds the round - split at the `dojo_start()` call, ready for `split_template` and `write_ipynb`. The framing text records are synthetic, which is fine (resume trusts them), and `mx=None` matters: the default rendering truncates tool output at 2,000 characters, which would gut the baked `doc()` results that a spawned session relies on." ] }, { @@ -293,11 +297,10 @@ "source": [ "tdlg = mk_template(c1, opening='Practice first.', doced=['doc','rg'])\n", "test_eq(tdlg.meta, dict(llmdojo=dict(doced=['doc','rg'])))\n", - "test_eq(tdlg.messages[0].content, TMPL_PROMPT)\n", - "test_eq(len(tdlg.messages), 1)\n", - "assert 'dojo_start()' in str(tdlg.messages[0].output)\n", + "test_eq([m.content for m in tdlg.messages], [BOOT_PROMPT, TMPL_PROMPT])\n", + "assert 'doc(clik' in str(tdlg.messages[0].output) and 'dojo_start()' in str(tdlg.messages[1].output)\n", "tmsgs = dlg2msgs(tdlg)\n", - "test_eq([m['role'] for m in tmsgs], ['user', 'assistant']*4)" + "test_eq([m['role'] for m in tmsgs], ['user', 'assistant']*5)" ] }, { @@ -350,14 +353,14 @@ "source": [ "#| export\n", "def build_template(\n", - " src, # Path to a template dialog .ipynb\n", + " boot=None, # The bootstrap dialog (or its path); the packaged clikernel bootstrap if None\n", + " rnd=None, # The round dialog (or its path); the packaged round if None\n", " d=None, # Store dir; `TMPL_DIR` if None\n", " cwd='.', # Project directory recorded in the records\n", "):\n", - " \"Convert a template dialog into the store; the dialog's `llmdojo.doced` metadata rides along for launch-time doc-state seeding\"\n", - " dlg = read_ipynb(str(src))\n", - " save_template(msgs2recs(dlg2msgs(dlg), key='claudedojo', cwd=str(cwd), model=None), d,\n", - " doced=nested_idx(dlg.meta, 'llmdojo', 'doced'))" + " \"Assemble the bootstrap and the round into the store; the assembled `doced` rides along for launch-time doc-state seeding\"\n", + " dlg = assemble(boot or CLIK_BOOT, rnd)\n", + " save_template(msgs2recs(dlg2msgs(dlg), key='claudedojo', cwd=str(cwd), model=None), d, doced=dlg.meta['llmdojo']['doced'])" ] }, { @@ -383,7 +386,7 @@ "metadata": {}, "outputs": [], "source": [ - "packaged = read_ipynb(str(files('llmdojo')/'dojo_data'/'dojo_template.ipynb'))\n", + "packaged = assemble(CLIK_BOOT)\n", "packaged_recs = msgs2recs(dlg2msgs(packaged), key='claudedojo', cwd='.', model=None)\n", "assert dojo_cid(packaged_recs)\n", "test_eq(is_clean(packaged_recs), [])\n", @@ -411,7 +414,7 @@ "test_eq(rmeta, dict(cid='ab12', v=_dojo_v(), doced=['rg','view_nb']))\n", "test_eq(canon(list(back)), canon([obj2dict(r) for r in c1]))\n", "bstore = Path(tempfile.mkdtemp())\n", - "build_template(files('llmdojo')/'dojo_data'/'dojo_template.ipynb', bstore)\n", + "build_template(d=bstore)\n", "bmeta = load_template(bstore)[1]\n", "test_eq(bmeta['cid'], dojo_cid(packaged_recs))\n", "test_eq(bmeta['v'], _dojo_v())\n", @@ -447,15 +450,15 @@ " sid=None, # Session id or name; the project's newest transcript if None\n", " cwd=None, # Project directory; the current directory if None\n", " d=None, # Template store dir; `TMPL_DIR` if None\n", - " docs=None, # Documented names; derived from the round's `doc(...)` calls if None\n", + " docs=None, # Documented names; derived from the captured `doc(...)` calls if None\n", "):\n", - " \"Gate an existing session's clean round and store its template\"\n", + " \"Gate an existing session's clean round and store its template; writes `boot.ipynb` and `round.ipynb` beside the store for review\"\n", " selected = curate_dojo(load_sess(sid, cwd))\n", " if probs := is_clean(selected): raise ValueError('; '.join(probs))\n", " if docs is None: docs = doced_names(_cells(selected))\n", " dlg = canon_tmpl(mk_template(selected, doced=docs))\n", " save_template(msgs2recs(dlg2msgs(dlg), key='claudedojo', model=None), d, doced=docs)\n", - " write_ipynb(dlg, Path(d or TMPL_DIR)/'template.ipynb')\n", + " for part,nm in zip(split_template(dlg), ('boot', 'round')): write_ipynb(part, Path(d or TMPL_DIR)/f'{nm}.ipynb')\n", " return dlg" ] }, @@ -470,7 +473,8 @@ "cdlg1 = capture_current(msid, mproj, cstore)\n", "test_eq(load_template(cstore)[1], dict(cid='ab12', v=_dojo_v(), doced=['clik', 'edsk', 'pysk']))\n", "test_eq(is_clean(load_template(cstore)[0]), [])\n", - "test_eq(cdlg1.messages[0].content, TMPL_PROMPT)\n", + "test_eq([m.content for m in cdlg1.messages], [BOOT_PROMPT, TMPL_PROMPT])\n", + "test_eq(sorted(p.name for p in cstore.glob('*.ipynb')), ['boot.ipynb', 'round.ipynb'])\n", "shutil.rmtree(cstore)\n", "test_fail(lambda: capture_current(save_sess(boot+card, cwd=mproj), mproj, cstore), contains='no dojo_score')" ] @@ -496,9 +500,9 @@ "id": "5eb1c7e0", "metadata": {}, "source": [ - "The shared capture prompt names each cell and the non-obvious details; the model supplies the narration. Claude passes the prompt as an addition to the `claude_code` preset. Codex writes the same bytes to the scratch project's `AGENTS.md`. Each Claude attempt runs in a fresh project with a `pyproject.toml`, so clikernel starts eagerly and prints the expected bootstrap. `LLMDOJO_STATE_DIR` also points into the scratch project, so a failed attempt cannot touch machine-global state. The round's doc-names come from its own `doc()` calls (`doced_names`), so no state travels from the child at all.\n", + "The shared capture prompt names each cell and the non-obvious details; the model supplies the narration. Claude passes the prompt as an addition to the `claude_code` preset and sends the two template prompts in turn, the second resuming the first's session. Codex writes the same bytes to the scratch project's `AGENTS.md`. Each Claude attempt runs in a fresh project with a `pyproject.toml`, so clikernel starts eagerly and prints the expected bootstrap. `LLMDOJO_STATE_DIR` also points into the scratch project, so a failed attempt cannot touch machine-global state. The doc-names come from the capture's own `doc()` calls (`doced_names`), so no state travels from the child at all.\n", "\n", - "A clean capture stores the template and writes it as `template.ipynb` beside the store. That notebook is the review point: read it, curate the prose, then rebuild the stores with `dojobuild --claude` and `--codex`. The store works before curation, but the baked history teaches by example, so the reviewed copy should be exemplary." + "A clean capture stores the template and writes `boot.ipynb` and `round.ipynb` beside the store. Those notebooks are the review point: read them, curate the prose, copy them over the packaged `clik_boot.ipynb` and `dojo_round.ipynb`, then rebuild the stores with `dojobuild`. The store works before curation, but the baked history teaches by example, so the reviewed copies should be exemplary." ] }, { @@ -516,24 +520,25 @@ " attempts=3, # Gated attempts before giving up\n", " budget=10.0, # Max USD per attempt\n", "):\n", - " \"Play a scripted dojo round headlessly, gate it with `is_clean`, and store the curated template\"\n", + " \"Play a scripted dojo round headlessly (the bootstrap prompt, then the dojo prompt), gate it with `is_clean`, and store the curated template\"\n", " from claude_agent_sdk import query, ClaudeAgentOptions, ResultMessage, AssistantMessage, TextBlock, ToolUseBlock\n", " for i in range(attempts):\n", " proj = Path(tempfile.mkdtemp(prefix='claudedojo_'))\n", " (proj/'pyproject.toml').write_text('[project]\\nname = \"dojo-capture\"\\nversion = \"0\"\\n')\n", " sid = str(uuid.uuid4())\n", - " opts = ClaudeAgentOptions(cwd=proj, session_id=sid, model=model, max_budget_usd=budget, permission_mode='bypassPermissions',\n", + " def _opts(**kw): return ClaudeAgentOptions(cwd=proj, model=model, max_budget_usd=budget, permission_mode='bypassPermissions',\n", " setting_sources=[], mcp_servers=dict(clikernel=dict(type='stdio', command=shutil.which('clikernel-mcp'))), strict_mcp_config=True,\n", " thinking=dict(type='adaptive'), effort=effort, env={'LLMDOJO_STATE_DIR': str(proj/'state'), 'MAX_MCP_OUTPUT_TOKENS': '50000'},\n", - " system_prompt=dict(type='preset', preset='claude_code', append=CAPTURE_SCRIPT))\n", + " system_prompt=dict(type='preset', preset='claude_code', append=CAPTURE_SCRIPT), **kw)\n", " cost,err = 0,None\n", " try:\n", - " async for m in query(prompt=TMPL_PROMPT, options=opts):\n", - " if isinstance(m, ResultMessage): cost = m.total_cost_usd or 0\n", - " elif isinstance(m, AssistantMessage):\n", - " for b in m.content:\n", - " if isinstance(b, ToolUseBlock): print('>', str(b.input.get('code', b.input))[:80], flush=True)\n", - " elif isinstance(b, TextBlock) and b.text.strip(): print('.', b.text.strip()[:80], flush=True)\n", + " for prompt,opts in ((BOOT_PROMPT, _opts(session_id=sid)), (TMPL_PROMPT, _opts(resume=sid))):\n", + " async for m in query(prompt=prompt, options=opts):\n", + " if isinstance(m, ResultMessage): cost += m.total_cost_usd or 0\n", + " elif isinstance(m, AssistantMessage):\n", + " for b in m.content:\n", + " if isinstance(b, ToolUseBlock): print('>', str(b.input.get('code', b.input))[:80], flush=True)\n", + " elif isinstance(b, TextBlock) and b.text.strip(): print('.', b.text.strip()[:80], flush=True)\n", " except Exception as e: err = f'run failed: {e}'\n", " recs = [] if err else sess_thread(load_sess(sid, proj))\n", " probs = [err] if err else is_clean(recs)\n", @@ -541,7 +546,7 @@ " if not probs:\n", " tdlg = capture_current(sid, proj, d)\n", " print(f\"stored: cid {load_template(d)[1]['cid']}, {(Path(d or TMPL_DIR)/'template.jsonl').stat().st_size//1024}KB\")\n", - " print(f\"review dialog: {Path(d or TMPL_DIR)/'template.ipynb'} - curate it, then rebuild the stores with dojobuild --claude (and --codex)\")\n", + " print(f\"review dialogs: {Path(d or TMPL_DIR)/'boot.ipynb'} and round.ipynb: curate, copy over the packaged pair, then dojobuild\")\n", " shutil.rmtree(proj)\n", " shutil.rmtree(sess_dir(proj), ignore_errors=True)\n", " if not probs: return tdlg\n", @@ -656,14 +661,18 @@ "source": [ "#| export\n", "def _is_prompt(r): return r.get('type')=='user' and rec_role(r)=='user'\n", - "\n", - "def _dealt(t): return any(re.match(_START_RE, nested_idx(b,'input','code') or '') for r in t for b in _blocks(r))\n", + "def _tcells(t): return [nested_idx(b,'input','code') or '' for r in t for b in _blocks(r) if b.get('type')=='tool_use']\n", + "def _dealt(t): return any(re.match(_START_RE, c) for c in _tcells(t))\n", + "def _boots(t): return bool(cs := _tcells(t)) and not boot_gates(cs)\n", "\n", "def strip_dojo(\n", " recs, # Session records, e.g. from `load_sess`\n", "):\n", - " \"Copy of `recs` without the user turns that dealt a round (bare `dojo_start()`)\"\n", - " return _turns(recs, _is_prompt).filter(_dealt, negate=True).concat()" + " \"Copy of `recs` without the user turns that dealt a round (bare `dojo_start()`), nor a bootstrap turn (only doc reads) right before one\"\n", + " ts = _turns(recs, _is_prompt)\n", + " drop = {i for i,t in enumerate(ts) if _dealt(t)}\n", + " drop |= {i-1 for i in drop if i and _boots(ts[i-1])}\n", + " return L(t for i,t in enumerate(ts) if i not in drop).concat()" ] }, { @@ -671,7 +680,7 @@ "id": "af096111", "metadata": {}, "source": [ - "On the synthetic capture, the dealt turn goes and everything else stays: the round's prompt, its noise records, and its receipt vanish as one turn, while the neighbouring turns survive untouched. Stripping twice changes nothing, so the pre-pass is safe on sessions with no round at all." + "On the synthetic capture, the dealt turn goes and so does the bootstrap turn right before it: the round's prompt, its noise records, its receipt, and the doc reads that warmed it up vanish together, while the neighbouring turns survive untouched. Stripping twice changes nothing, so the pre-pass is safe on sessions with no round at all." ] }, { @@ -682,7 +691,7 @@ "outputs": [], "source": [ "stripped = strip_dojo(mini)\n", - "test_eq(len(stripped), len(mini)-8)\n", + "test_eq(len(stripped), len(mini)-12)\n", "assert not any(_dealt([r]) for r in stripped)\n", "test_eq(strip_dojo(stripped), stripped)\n", "[rec_txt(r) for r in stripped if _is_prompt(r)]" @@ -904,7 +913,7 @@ "id": "b103f1bf", "metadata": {}, "source": [ - "When the dojo tooling version changes, `prep_dojo` warns and the baked completion id stops validating. When only outputs have staled, a bare `dojobuild` (the shared `refresh_template`, plus both store builds) regenerates them without a model; a change to the round's *cells* needs a fresh round. The steps to play are the capture script's numbered cells (`llmdojo/dojo_data/capture_prompt.md` - the single source of truth for the round; adjust it first if the dojo changed). The live workflow, all from one session: play the script's cells in order, narrating via comment-only kernel cells, then run the cells below from that same session (`capture_current` resolves to the newest transcript, which is the conversation that just played, and derives the doc-names from the round's own `doc()` calls). Failed calls are curated away; a redo or a second score means the round wasn't clean, so play it again." + "When the dojo tooling version changes, `prep_dojo` warns and the baked completion id stops validating. When only outputs have staled, a bare `dojobuild` (the shared `refresh_template` on the packaged pair, plus every store build) regenerates them without a model; a change to the round's *cells* needs a fresh round. The steps to play are the capture script's numbered cells (`llmdojo/dojo_data/capture_prompt.md` - the single source of truth for the round; adjust it first if the dojo changed). The live workflow, all from one session: answer the bootstrap prompt with the script's doc reads, then the dojo prompt with the round, narrating via comment-only kernel cells, then run the cells below from that same session (`capture_current` resolves to the newest transcript, which is the conversation that just played, and derives the doc-names from the capture's own `doc()` calls). Failed calls are curated away; a redo or a second score means the round wasn't clean, so play it again." ] }, { @@ -916,7 +925,7 @@ "source": [ "#| eval: false\n", "dlg = capture_current()\n", - "write_ipynb(dlg, 'llmdojo/dojo_data/dojo_template.ipynb')" + "for part,p in zip(split_template(dlg), (CLIK_BOOT, ROUND_PATH)): write_ipynb(part, str(p))" ] }, { @@ -935,7 +944,7 @@ "outputs": [], "source": [ "#| eval: false\n", - "build_template('llmdojo/dojo_data/dojo_template.ipynb', cwd=Path.cwd())\n", + "build_template(cwd=Path.cwd())\n", "load_template()[1]" ] }, diff --git a/nbs/01_codexdojo.ipynb b/nbs/01_codexdojo.ipynb index b274352..200504a 100644 --- a/nbs/01_codexdojo.ipynb +++ b/nbs/01_codexdojo.ipynb @@ -34,7 +34,7 @@ "from fastcore.script import call_parse\n", "from llmsurgery.oai import *\n", "from llmsurgery.compact import compact_chat\n", - "from aidialog.ipynb import read_ipynb, write_ipynb\n", + "from aidialog.ipynb import write_ipynb\n", "from llmdojo.rules import _state_root\n", "from llmdojo.tmpl import *\n", "from llmdojo.tmpl import _dojo_v, _seed_doced, _START_RE, _turns" @@ -415,7 +415,7 @@ "source": [ "#| export\n", "def _capture_span(src):\n", - " \"Native custom-call pairs from the bootstrap docs through the first dojo score\"\n", + " \"Native custom-call pairs from the bootstrap reads through the first dojo score\"\n", " items = _items(src)\n", " starts = [i for i,x in enumerate(items) if re.match(_START_RE,_cell(x))]\n", " if not starts: raise ValueError('no dojo_start() call')\n", @@ -423,8 +423,8 @@ " users = [i for i,x in enumerate(items[:start]) if x.get('type')=='message' and x.get('role')=='user']\n", " if not users: raise ValueError('no user message before dojo_start()')\n", " end = first((i for i,x in enumerate(items[start+1:],start+1) if x.get('type')=='message' and x.get('role')=='user'),len(items))\n", - " pairs = collapse_deferred(items[users[-1]+1:end])\n", - " i,j = capture_slice([_cell(x) for x in pairs[::2]])\n", + " pairs = collapse_deferred(items[users[0]+1:end])\n", + " i,_,j = capture_slice([_cell(x) for x in pairs[::2]])\n", " return pairs[i*2:(j+1)*2]\n", "\n", "def capture_current(\n", @@ -432,42 +432,44 @@ " d=None, # Template store dir; `TMPL_DIR` if None\n", " docs=None, # Documented names; derived from captured `doc(...)` calls if None\n", "):\n", - " \"Gate an existing Codex thread's clean round and store its native template\"\n", + " \"Gate an existing Codex thread's clean round and store its native template; writes `boot.ipynb` and `round.ipynb` beside the store for review\"\n", " selected = reid_items(_capture_span(response_items(load_rollout(ref))),'codexdojo')\n", " if probs:=is_clean(selected): raise ValueError('; '.join(probs))\n", " if docs is None: docs = doced_names([_cell(x) for x in selected])\n", " dlg = canon_tmpl(mk_template(selected,doced=docs))\n", " save_template(dlg2items(dlg),d,doced=docs)\n", - " write_ipynb(dlg,Path(d or TMPL_DIR)/'template.ipynb')\n", + " for part,nm in zip(split_template(dlg), ('boot', 'round')): write_ipynb(part, Path(d or TMPL_DIR)/f'{nm}.ipynb')\n", " return dlg\n", "\n", "def capture_dojo(\n", " d=None, # Template store dir; `TMPL_DIR` if None\n", "):\n", - " \"Run a clean dojo in a bare Codex child and store its review dialog\"\n", + " \"Run a clean dojo in a bare Codex child (the bootstrap prompt, then the dojo prompt resumed onto it) and store its review dialogs\"\n", " with tempfile.TemporaryDirectory(prefix='codexdojo_') as td:\n", " proj = Path(td)\n", " (proj/'pyproject.toml').write_text('[project]\\nname = \"dojo-capture\"\\nversion = \"0\"\\n')\n", " (proj/'AGENTS.md').write_text(CAPTURE_SCRIPT)\n", " env = os.environ | {'LLMDOJO_STATE_DIR': str(proj/'state')}\n", - " cmd = ['codex','-a','never','-s','workspace-write','exec','--json','--skip-git-repo-check','-C',str(proj),TMPL_PROMPT]\n", - " proc = subprocess.Popen(cmd,stdout=subprocess.PIPE,text=True,env=env)\n", + " base = ['codex','-a','never','-s','workspace-write','exec','--json','--skip-git-repo-check','-C',str(proj)]\n", " sid = None\n", - " for line in proc.stdout:\n", - " event = json.loads(line)\n", - " if event.get('type')=='thread.started':\n", - " sid = event['thread_id']\n", - " print(f'thread: {sid}',flush=True)\n", - " elif event.get('type')=='item.completed':\n", - " it = event.get('item',{})\n", - " typ = it.get('type')\n", - " if typ=='agent_message': print(it['text'],flush=True)\n", - " elif typ!='reasoning':\n", - " c = parse_exec(it.get('input') or '')\n", - " desc = c.arguments.get('code',str(c.arguments)).splitlines()[0] if c else truncstr(json.dumps(it),160)\n", - " print(f'[{typ}] {desc}',flush=True)\n", - " if rc:=proc.wait(): raise subprocess.CalledProcessError(rc,cmd)\n", - " if not sid: raise RuntimeError('capture produced no thread id')\n", + " for cmd in (base+[BOOT_PROMPT], lambda: base+['resume',sid,TMPL_PROMPT]):\n", + " if callable(cmd): cmd = cmd()\n", + " proc = subprocess.Popen(cmd,stdout=subprocess.PIPE,text=True,env=env)\n", + " for line in proc.stdout:\n", + " event = json.loads(line)\n", + " if event.get('type')=='thread.started':\n", + " sid = event['thread_id']\n", + " print(f'thread: {sid}',flush=True)\n", + " elif event.get('type')=='item.completed':\n", + " it = event.get('item',{})\n", + " typ = it.get('type')\n", + " if typ=='agent_message': print(it['text'],flush=True)\n", + " elif typ!='reasoning':\n", + " c = parse_exec(it.get('input') or '')\n", + " desc = c.arguments.get('code',str(c.arguments)).splitlines()[0] if c else truncstr(json.dumps(it),160)\n", + " print(f'[{typ}] {desc}',flush=True)\n", + " if rc:=proc.wait(): raise subprocess.CalledProcessError(rc,cmd)\n", + " if not sid: raise RuntimeError('capture produced no thread id')\n", " return capture_current(sid,d)" ] }, @@ -507,7 +509,7 @@ "source": [ "### Building the review dialog\n", "\n", - "`mk_template` adds the synthetic user prompt and short framing messages around already selected native tool pairs. Disabling tool truncation preserves every receipt byte-for-byte for rebuilding." + "`mk_template` adds the two synthetic user prompts and short framing messages around already selected native tool pairs, split at the `dojo_start()` call: the bootstrap reads answer the first prompt, the round answers the second. Disabling tool truncation preserves every receipt byte-for-byte for rebuilding." ] }, { @@ -518,16 +520,18 @@ "outputs": [], "source": [ "#| export\n", - "OPENING = 'Bootstrapping: I read the tooling docs, check the catalog, then take the dojo.'\n", + "OPENING = 'Bootstrapping: I read the tooling docs and check the catalog.'\n", "\n", "def mk_template(\n", - " picked, # Native custom call/result pairs\n", - " opening=OPENING, # Reply text preceding the first tool call\n", - " closing=\"OK I'm ready.\", # Reply text ending the round\n", - " doced=None, # Names doc()'d during the round\n", + " picked, # Native custom call/result pairs: the bootstrap reads, then the round\n", + " opening=OPENING, # Bootstrap reply text preceding its first tool call\n", + " closing=\"OK I'm ready.\", # Reply text ending each prompt's reply\n", + " doced=None, # Names doc()'d during the capture\n", "):\n", - " \"A template dialog with native tool output left untruncated\"\n", - " items = [codex_msg('user',TMPL_PROMPT),codex_msg('assistant',opening),*picked,codex_msg('assistant',closing)]\n", + " \"A two-prompt template dialog with native tool output left untruncated: `picked` split at its `dojo_start()` call into the bootstrap reply and the round reply\"\n", + " k = first(i for i,x in enumerate(picked) if re.match(_START_RE, _cell(x)))\n", + " items = [codex_msg('user',BOOT_PROMPT),codex_msg('assistant',opening),*picked[:k],codex_msg('assistant',closing),\n", + " codex_msg('user',TMPL_PROMPT),*picked[k:],codex_msg('assistant',closing)]\n", " dlg = items2dlg(items,'codexdojo_template',mx=None)\n", " dlg.meta['llmdojo'] = dict(doced=list(doced or []))\n", " return dlg" @@ -540,12 +544,12 @@ "metadata": {}, "outputs": [], "source": [ - "tdlg = mk_template(mini, opening='Practice first.', doced=['doc','rg'])\n", + "tdlg = mk_template(span, opening='Practice first.', doced=['doc','rg'])\n", "test_eq(tdlg.meta, dict(llmdojo=dict(doced=['doc','rg'])))\n", - "test_eq(tdlg.messages[0].content, TMPL_PROMPT)\n", + "test_eq([m.content for m in tdlg.messages], [BOOT_PROMPT, TMPL_PROMPT])\n", "back = dlg2items(tdlg)\n", - "call = first(x for x in back if x.get('type')=='custom_tool_call')\n", - "test_eq(parse_exec(call['input']).arguments['code'], 'dojo_start()')" + "calls = [parse_exec(x['input']).arguments['code'] for x in back if x.get('type')=='custom_tool_call']\n", + "test_eq(calls[:2], ['doc(clik, pysk, edsk)', 'dojo_start()'])" ] }, { @@ -563,7 +567,7 @@ "metadata": {}, "outputs": [], "source": [ - "packaged = read_ipynb(str(files('llmdojo')/'dojo_data'/'dojo_template.ipynb'))\n", + "packaged = assemble(CLIK_BOOT)\n", "packaged_items = dlg2items(packaged)\n", "assert dojo_cid(packaged_items)\n", "test_eq(is_clean(packaged_items), [])" @@ -733,13 +737,13 @@ "source": [ "#| export\n", "def build_template(\n", - " src, # Path to a template dialog\n", + " boot=None, # The bootstrap dialog (or its path); the packaged clikernel bootstrap if None\n", + " rnd=None, # The round dialog (or its path); the packaged round if None\n", " d=None, # Store dir; `TMPL_DIR` if None\n", "):\n", - " \"Convert a reviewed template dialog back into the native store\"\n", - " dlg = read_ipynb(str(src))\n", - " items = reid_items(dlg2items(dlg),'codexdojo')\n", - " save_template(items,d,doced=nested_idx(dlg.meta,'llmdojo','doced') or [])" + " \"Assemble the bootstrap and the round into the native store\"\n", + " dlg = assemble(boot or CLIK_BOOT, rnd)\n", + " save_template(reid_items(dlg2items(dlg),'codexdojo'),d,doced=dlg.meta['llmdojo']['doced'])" ] }, { @@ -749,10 +753,11 @@ "metadata": {}, "outputs": [], "source": [ - "review = store/'review.ipynb'\n", - "write_ipynb(tdlg,review)\n", + "b,r = split_template(tdlg)\n", + "write_ipynb(b, store/'boot.ipynb')\n", + "write_ipynb(r, store/'round.ipynb')\n", "rebuilt = store/'rebuilt'\n", - "build_template(review,rebuilt)\n", + "build_template(store/'boot.ipynb', store/'round.ipynb', rebuilt)\n", "test_eq(dojo_cid(load_template(rebuilt)[0]),'ab12')" ] }, @@ -891,7 +896,7 @@ "id": "5790c583", "metadata": {}, "source": [ - "The DSL compact should carry the conversation's work, not its warm-up: the fresh round appended afterwards supersedes any earlier one. As on the Claude side, a round occupies whole user turns, so `strip_dojo` chunks the items with the shared `_turns` and drops the turns that dealt a round; `compact_session`'s `strip` hook applies it before anything is compacted. A thread holding nothing but rounds is compacted whole rather than emptied." + "The DSL compact should carry the conversation's work, not its warm-up: the fresh round appended afterwards supersedes any earlier one. As on the Claude side, a round occupies whole user turns, so `strip_dojo` chunks the items with the shared `_turns` and drops the turns that dealt a round, along with a bootstrap turn right before one; `compact_session`'s `strip` hook applies it before anything is compacted. A thread holding nothing but rounds is compacted whole rather than emptied." ] }, { @@ -903,14 +908,17 @@ "source": [ "#| export\n", "def _is_prompt(x): return x.get('type')=='message' and x.get('role')=='user'\n", - "\n", "def _dealt(t): return any(re.match(_START_RE, _cell(x)) for x in t)\n", + "def _boots(t): return bool(cs := [c for x in t if (c := _cell(x))]) and not boot_gates(cs)\n", "\n", "def strip_dojo(\n", " items, # Responses items in conversation order\n", "):\n", - " \"Copy of `items` without the user turns that dealt a round (bare `dojo_start()`)\"\n", - " return _turns(items, _is_prompt).filter(_dealt, negate=True).concat()\n", + " \"Copy of `items` without the user turns that dealt a round (bare `dojo_start()`), nor a bootstrap turn (only doc reads) right before one\"\n", + " ts = _turns(items, _is_prompt)\n", + " drop = {i for i,t in enumerate(ts) if _dealt(t)}\n", + " drop |= {i-1 for i in drop if i and _boots(ts[i-1])}\n", + " return L(t for i,t in enumerate(ts) if i not in drop).concat()\n", "\n", "def _strip(items):\n", " \"`strip_dojo`, unless nothing else would remain to compact\"\n", @@ -926,8 +934,10 @@ "outputs": [], "source": [ "work = custom_turn('What season is it?', 'tools.mcp__clikernel__execute', dict(code='season()'), 'winter', 'Winter.')\n", + "booted = custom_turn('Bootstrap.', 'tools.mcp__clikernel__execute', dict(code='doc(pysk, edsk)'), 'module docs', 'Ready.')\n", "played = custom_turn('Play the round.', 'tools.mcp__clikernel__execute', dict(code='dojo_start()'), '== llmdojo ==', 'Underway.')\n", - "test_eq(strip_dojo(work+played+work), work+work)\n", + "test_eq(strip_dojo(work+booted+played+work), work+work)\n", + "test_eq(strip_dojo(booted+work), booted+work) # a bootstrap turn stays unless a dealt turn follows it\n", "test_eq(_strip(played), played)\n", "[item_txt(x) for x in _strip(work+played) if x.get('type')=='message']" ] @@ -1023,7 +1033,7 @@ "source": [ "## Rebuilding\n", "\n", - "Run `codexdojo --capture` to start a clean dojo round in a separate scratch project and write the accepted round to the local template store. The command uses the same packaged capture prompt as Claude, delivered through the scratch project's `AGENTS.md`. It also writes `template.ipynb` beside the store for review. Curate that dialog with `reply2dlg` and `dlg2reply`, then rebuild the stores with `dojobuild --codex` (and `--claude`). Use `--current` when the source thread already completed a clean round." + "Run `codexdojo --capture` to start a clean dojo round in a separate scratch project and write the accepted round to the local template store. The command uses the same packaged capture prompt as Claude, delivered through the scratch project's `AGENTS.md`, and sends the two template prompts in turn (`codex exec`, then `codex exec resume`). It also writes `boot.ipynb` and `round.ipynb` beside the store for review. Curate those dialogs with `reply2dlg` and `dlg2reply`, copy them over the packaged `clik_boot.ipynb` and `dojo_round.ipynb`, then rebuild the stores with `dojobuild`. Use `--current` when the source thread already completed a clean round." ] }, { diff --git a/nbs/02_tmpl.ipynb b/nbs/02_tmpl.ipynb index 7644cd3..1466b21 100644 --- a/nbs/02_tmpl.ipynb +++ b/nbs/02_tmpl.ipynb @@ -26,7 +26,7 @@ "metadata": {}, "source": [ "#| export\n", - "The claudedojo and codexdojo launchers are mirror images: each captures a clean dojo round, gates it, stores it beside its metadata, and splices it into sessions at launch time. They differ only in the record shape their host uses (Claude Code transcript records vs Codex Responses items) and in transport. Everything representation-independent lives here instead of twice over there: the shared prompts and gate phrases, the completion-id receipt, the store layout, the launch-time loader and doc-state seeding, the content gates, and the round's structural boundaries. A backend contributes only its extractors, its transport, and its capture child." + "The claudedojo, codexdojo, and ipyaidojo launchers are mirror images: each gates a clean dojo round, stores it beside its metadata, and splices it into sessions at launch time. They differ only in the record shape their host uses (Claude Code transcript records, Codex Responses items, an ipyai session dialog), in transport, and in the bootstrap that opens the template - the host's own doc() reads, answered to the first of two prompts. Everything representation-independent lives here instead of three times over there: the shared prompts and gate phrases, the completion-id receipt, the store layout, the launch-time loader and doc-state seeding, the content gates, the captured dialog's structural boundaries, and the assembly of a host's bootstrap with the shared round. A backend contributes only its bootstrap dialog, its extractors, its transport, and its capture child." ] }, { @@ -37,14 +37,14 @@ "outputs": [], "source": [ "#| export\n", - "import json, os, re, subprocess, sys, tempfile, tomllib\n", + "import asyncio, json, os, re, subprocess, sys, tempfile, tomllib\n", "from importlib.resources import files\n", "from fastcore.utils import *\n", "from fastcore.script import call_parse\n", "from clikernel.cli import _MARKER as _CLIK_MARKER\n", "from aidialog.ipynb import read_ipynb, write_ipynb\n", "from aidialog.hist import reply2dlg, dlg2reply, _parse_call\n", - "from aidialog.dialog import code_output, prompt_output\n", + "from aidialog.dialog import Dialog, code_output, prompt_output\n", "from llmdojo.rules import _state_root, _docnames" ] }, @@ -72,7 +72,7 @@ "id": "9f4ff246", "metadata": {}, "source": [ - "Whichever host plays the round, the words around it are the same: the user prompt that opens a template, the prompt used when re-splicing after a compaction, the capture script a scripted child follows, and the script phrases that must never leak into a template's visible text." + "Whichever host plays the round, the words around it are the same: the two user prompts that open a template (bootstrap, then the dojo), the prompt used when re-splicing after a compaction, the capture script a scripted child follows, and the script phrases that must never leak into a template's visible text. The two packaged dialogs are the review artifacts a build starts from: the shared round, and the clikernel hosts' bootstrap (ipyai keeps its own beside its backend)." ] }, { @@ -83,10 +83,13 @@ "outputs": [], "source": [ "#| export\n", - "TMPL_PROMPT = \"Bootstrap and complete the dojo and tell me when you're ready.\"\n", - "APPEND_PROMPT = \"We've compacted - run the dojo round again and tell me when you're ready.\"\n", + "BOOT_PROMPT = \"Bootstrap and tell me when you're ready.\"\n", + "TMPL_PROMPT = \"Complete the dojo and tell me when you're ready.\"\n", + "APPEND_PROMPT = \"We've compacted - bootstrap again and tell me when you're ready.\"\n", "GATE_FORBID = ('recording a demo', 'these notes')\n", - "CAPTURE_SCRIPT = (files('llmdojo')/'dojo_data'/'capture_prompt.md').read_text()" + "CAPTURE_SCRIPT = (files('llmdojo')/'dojo_data'/'capture_prompt.md').read_text()\n", + "ROUND_PATH = files('llmdojo')/'dojo_data'/'dojo_round.ipynb' # the shared round: one prompt, its reply the worked dojo\n", + "CLIK_BOOT = files('llmdojo')/'dojo_data'/'clik_boot.ipynb' # the clikernel hosts' bootstrap: one prompt, its reply the startup's doc() reads" ] }, { @@ -352,7 +355,7 @@ "id": "62b240c3", "metadata": {}, "source": [ - "A captured round has structural boundaries that need no sentinels or bookkeeping: the bootstrap `doc()` reads open it and the score closes it. `capture_slice` finds that span in a list of kernel cell sources - the last bootstrap before the last dealt round, through that round's first score - so an abandoned earlier round in the same conversation is skipped, and each backend only maps its native call/result pairs to cell sources before slicing." + "A captured dialog has structural boundaries that need no sentinels or bookkeeping: the bootstrap `doc()` reads, the `dojo_start()` that deals the round, and the score that closes it. `capture_slice` finds all three in a list of kernel cell sources - the run of bootstrap reads immediately before the last dealt round, that round's start, and its first score - so an abandoned earlier round in the same conversation is skipped, unrelated earlier calls are not bootstrap, and each backend only maps its native call/result pairs to cell sources before slicing. The split at the start index is what makes two prompt messages of one capture: the bootstrap reply and the round reply. `boot_gates` is the bootstrap's own acceptance test: nothing but doc reads and catalog calls." ] }, { @@ -363,18 +366,29 @@ "outputs": [], "source": [ "#| export\n", + "_BOOT_RE = r'^(doc\\([^()\\n]*\\)|list_pyskills\\(\\))\\s*$'\n", + "\n", "def capture_slice(\n", " cells, # Kernel cell sources of a conversation's calls, in order\n", "):\n", - " \"The `(first, last)` cell indices of the captured round: bootstrap docs through the first score of the last dealt round\"\n", + " \"The `(boot, start, last)` cell indices of a captured dialog: the bootstrap reads before the last dealt round, that round's `dojo_start()`, and its first score\"\n", " starts = [i for i,c in enumerate(cells) if re.match(_START_RE, c)]\n", " if not starts: raise ValueError('no dojo_start() call')\n", - " start = starts[-1]\n", - " boots = [i for i,c in enumerate(cells[:start]) if re.match(r'^doc\\(clik,\\s*pysk,\\s*edsk\\)\\s*$', c)]\n", - " if not boots: raise ValueError('no bootstrap doc call before dojo_start()')\n", + " start = boot = starts[-1]\n", + " while boot and re.match(_BOOT_RE, cells[boot-1].strip()): boot -= 1\n", + " if boot == start: raise ValueError('no bootstrap doc call before dojo_start()')\n", " scores = [i for i,c in enumerate(cells[start:], start) if re.match(r'^dojo_score\\(', c)]\n", " if not scores: raise ValueError('no dojo_score() after dojo_start()')\n", - " return boots[-1], scores[0]" + " return boot, start, scores[0]\n", + "\n", + "def boot_gates(\n", + " cells, # A bootstrap reply's kernel cell sources\n", + "):\n", + " \"The content problems that disqualify a bootstrap reply: a cell that is not a doc read or catalog call, or no doc read at all\"\n", + " probs = []\n", + " if (bad := [c for c in cells if not re.match(_BOOT_RE, c.strip())]): probs.append(f'not a bootstrap cell: {bad[0]!r}')\n", + " if not any(c.strip().startswith('doc(') for c in cells): probs.append('no doc() read')\n", + " return probs" ] }, { @@ -384,11 +398,15 @@ "metadata": {}, "outputs": [], "source": [ - "test_eq(capture_slice(boot+round_cells), (0, 8))\n", - "test_eq(capture_slice(boot+round_cells+boot+round_cells), (9, 17)) # an abandoned round is skipped\n", + "test_eq(capture_slice(boot+round_cells), (0, 4, 8))\n", + "test_eq(capture_slice(['x = 1']+boot+round_cells), (1, 5, 9)) # earlier unrelated calls are not bootstrap\n", + "test_eq(capture_slice(boot+round_cells+boot+round_cells), (9, 13, 17)) # an abandoned round is skipped\n", "test_fail(lambda: capture_slice(boot), contains='no dojo_start')\n", "test_fail(lambda: capture_slice(round_cells), contains='no bootstrap doc call')\n", - "test_fail(lambda: capture_slice(boot+['dojo_start()']), contains='no dojo_score')" + "test_fail(lambda: capture_slice(boot+['dojo_start()']), contains='no dojo_score')\n", + "test_eq(boot_gates(boot), [])\n", + "test_eq(boot_gates(['doc(pysk)', 'x = 1']), [\"not a bootstrap cell: 'x = 1'\"])\n", + "test_eq(boot_gates(['list_pyskills()']), ['no doc() read'])" ] }, { @@ -466,6 +484,90 @@ "doced_names(['doc(find_msgs, view_dlg)', 'x = 1', \"doc(report.daily_report)\\nreport.daily_report(SAMPLE)\"])" ] }, + { + "cell_type": "markdown", + "id": "2ed7db79", + "metadata": {}, + "source": [ + "A template is assembled from two dialogs: the host's bootstrap (one prompt, its reply the startup's doc reads) and the shared round. `assemble` concatenates them and re-derives `doced` over every cell; `split_template` is the inverse a capture uses to write the two review files from one two-prompt dialog. Both work on the parsed shape `_msg_cells` gives for a prompt message: its reply exploded into note and code messages, and the kernel cell source of each call." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9b4d0cff", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "def _pmsgs(dlg): return [m for m in dlg.messages if m.msg_type=='prompt']\n", + "\n", + "def _msg_cells(\n", + " pmsg, # A template prompt message\n", + "):\n", + " \"`(sub, codes, cells)`: the reply exploded, its code messages, and their kernel cell sources\"\n", + " sub = reply2dlg(pmsg)\n", + " codes = [m for m in sub.messages if m.msg_type=='code']\n", + " calls = [_parse_call(m.content) for m in codes]\n", + " if bad := first(m.content for m,c in zip(codes,calls) if not (c and c[1].get('code'))): raise ValueError(f'not a kernel call: {bad}')\n", + " return sub, codes, [c[1]['code'] for c in calls]\n", + "\n", + "def template_cells(\n", + " dlg, # A template dialog\n", + "):\n", + " \"Kernel cell sources of every prompt's reply in `dlg`, in order\"\n", + " return [c for m in _pmsgs(dlg) for c in _msg_cells(m)[2]]\n", + "\n", + "def _one_prompt(m, name):\n", + " \"A one-prompt dialog holding a copy of `m`, with `doced` from its own cells\"\n", + " d = Dialog(name=name)\n", + " d.mk_message(m.content, msg_type='prompt', output=m.output)\n", + " d.meta['llmdojo'] = dict(doced=doced_names(template_cells(d)))\n", + " return d\n", + "\n", + "def assemble(\n", + " boot, # The host's bootstrap dialog, or its path\n", + " rnd=None, # The shared round dialog, or its path; the packaged round if None\n", + "):\n", + " \"The template a host splices: the bootstrap's prompt(s), then the round's, with `doced` re-derived over both\"\n", + " boot,rnd = [x if isinstance(x, Dialog) else read_ipynb(str(x)) for x in (boot, rnd or ROUND_PATH)]\n", + " d = Dialog(name='dojo_template')\n", + " for m in _pmsgs(boot)+_pmsgs(rnd): d.mk_message(m.content, msg_type='prompt', output=m.output)\n", + " d.meta['llmdojo'] = dict(doced=doced_names(template_cells(d)))\n", + " return d\n", + "\n", + "def split_template(\n", + " dlg, # A two-prompt template dialog: bootstrap, then round\n", + "):\n", + " \"`(boot, rnd)`: one-prompt dialogs for the bootstrap and the round, each with its own `doced`\"\n", + " b,r = _pmsgs(dlg)\n", + " return _one_prompt(b, 'boot'), _one_prompt(r, 'dojo_round')" + ] + }, + { + "cell_type": "markdown", + "id": "03714466", + "metadata": {}, + "source": [ + "Assembling the packaged pair gives the two-prompt template; splitting it gives the pair back, and `doced` partitions the same way the cells do:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "28d712ff", + "metadata": {}, + "outputs": [], + "source": [ + "tmpl = assemble(CLIK_BOOT)\n", + "test_eq([m.content for m in tmpl.messages], [BOOT_PROMPT, TMPL_PROMPT])\n", + "b,r = split_template(tmpl)\n", + "test_eq(b.messages[0].output, read_ipynb(str(CLIK_BOOT)).messages[0].output)\n", + "test_eq(sorted(b.meta['llmdojo']['doced']+r.meta['llmdojo']['doced']), tmpl.meta['llmdojo']['doced'])\n", + "test_eq(boot_gates(template_cells(b)), [])\n", + "tmpl.meta" + ] + }, { "cell_type": "markdown", "id": "da0b635e", @@ -534,7 +636,7 @@ "id": "e3c434d7", "metadata": {}, "source": [ - "`refresh_template` is the whole operation on a template dialog: parse each baked call back to its kernel cell, replay, splice each fresh output in (an empty response renders as the host's no-output line, matching what a live session records), re-derive `doced`, and gate with `round_gates` before writing. It refuses a dialog whose calls aren't all kernel cells, and a refresh that fails the gates writes nothing - if the round no longer plays clean, the cells need human attention, not fresher outputs." + "`refresh_dialog` is the splice: given each prompt's fresh outputs (from whichever replayer the host owns), it writes them into the baked calls (an empty response renders as the host's no-output line, matching what a live session records), optionally renames the calls to the host's tool, re-derives `doced`, and gates - `round_gates` on the prompt that deals a round, `boot_gates` on any other - before canonicalizing. `refresh_template` is the clikernel-hosted whole: parse a packaged dialog's cells, replay them through a fresh `clikernel`, splice, write. It refuses a dialog whose calls aren't all kernel cells, and a refresh that fails the gates writes nothing - if the round no longer plays clean, the cells need human attention, not fresher outputs." ] }, { @@ -548,37 +650,53 @@ "DOJO_CANON = '/tmp/dojo'\n", "\n", "def canon_tmpl(\n", - " dlg, # A template dialog whose reply holds a round\n", + " dlg, # A template dialog\n", " canon=DOJO_CANON, # Canonical spelling for the round's run-dir path\n", "):\n", - " \"Rewrite the round's run-dir path (read from its own `%cd` cell) to `canon` throughout the reply, so stored templates read the same on every machine\"\n", - " pm = dlg.messages[0]\n", - " txt = pm.ai_res\n", - " rd = first(re.findall(r\"%cd ([^\\s'\\\"\\\\]+)\", txt))\n", - " if rd and rd != canon: pm.output = prompt_output(txt.replace(rd, canon))\n", + " \"Rewrite the round's run-dir path (read from its own `%cd` cell) to `canon` throughout every reply, so stored templates read the same on every machine\"\n", + " rd = first(re.findall(r\"%cd ([^\\s'\\\"\\\\]+)\", ' '.join(m.ai_res for m in _pmsgs(dlg))))\n", + " if rd and rd != canon:\n", + " for m in _pmsgs(dlg): m.output = prompt_output(m.ai_res.replace(rd, canon))\n", + " return dlg\n", + "\n", + "def localized_cells(\n", + " dlg, # A template dialog\n", + "):\n", + " \"Per prompt, the reply's kernel cells with the canonical run dir localized to this machine's, ready to replay\"\n", + " from llmdojo.dojo import _run_dir\n", + " return [[c.replace(DOJO_CANON, str(_run_dir())) for c in _msg_cells(m)[2]] for m in _pmsgs(dlg)]\n", + "\n", + "def refresh_dialog(\n", + " dlg, # A template dialog\n", + " outs, # Per prompt, each cell's fresh output text, as `localized_cells` orders them\n", + " tool=None, # Rename every call to this tool name (a host whose tool is not clikernel's)\n", + "):\n", + " \"Splice fresh outputs into `dlg`'s baked calls, gate each reply, re-derive `doced`, and canonicalize; returns `dlg`\"\n", + " from llmdojo.dojo import _run_dir\n", + " for pm,o in zip(_pmsgs(dlg), outs):\n", + " sub,codes,cells = _msg_cells(pm)\n", + " for m,c,x in zip(codes, cells, o):\n", + " name = tool or _parse_call(m.content)[0]\n", + " if tool: m.content = f'{tool}(code={c.replace(str(_run_dir()), DOJO_CANON)!r})'\n", + " m.output = code_output(x or f'({name} completed with no output)')\n", + " notes = ' '.join(m.content for m in sub.messages if m.msg_type=='note')\n", + " probs = round_gates(cells, o, notes) if any(re.match(_START_RE, c) for c in cells) else boot_gates(cells)\n", + " if probs: raise ValueError('; '.join(probs))\n", + " pm.output = prompt_output(dlg2reply(sub).replace(str(_run_dir()), DOJO_CANON))\n", + " dlg.meta['llmdojo'] = dict(doced=doced_names(template_cells(dlg)))\n", " return dlg\n", "\n", "def refresh_template(\n", " src, # Path to a template dialog .ipynb\n", " dst=None, # Where to write the refreshed dialog; `src` if None\n", "):\n", - " \"Replay `src`'s kernel cells through a fresh clikernel, splice in current outputs, refresh `doced`, and gate; writes and returns the refreshed (canonicalized) dialog\"\n", - " from llmdojo.dojo import _run_dir\n", + " \"Replay `src`'s kernel cells through a fresh clikernel, splice in current outputs, refresh `doced`, and gate; writes and returns the refreshed dialog\"\n", " dlg = read_ipynb(str(src))\n", - " pmsg = dlg.messages[0]\n", - " sub = reply2dlg(pmsg)\n", - " codes = [m for m in sub.messages if m.msg_type=='code']\n", - " calls = [_parse_call(m.content) for m in codes]\n", - " if bad := first(m.content for m,c in zip(codes,calls) if not (c and c[1].get('code'))): raise ValueError(f'not a kernel call: {bad}')\n", - " cells = [c[1]['code'].replace(DOJO_CANON, str(_run_dir())) for c in calls] # localize: the round plays in the real run dir\n", " with tempfile.TemporaryDirectory(prefix='dojorefresh_') as td:\n", " proj = Path(td)\n", " (proj/'pyproject.toml').write_text('[project]\\nname = \"dojo-refresh\"\\nversion = \"0\"\\n')\n", - " outs = _replay_cells(cells, proj)\n", - " for m,(name,_),o in zip(codes, calls, outs): m.output = code_output(o or f'({name} completed with no output)')\n", - " if probs := round_gates(cells, outs, ' '.join(m.content for m in sub.messages if m.msg_type=='note')): raise ValueError('; '.join(probs))\n", - " pmsg.output = prompt_output(dlg2reply(sub).replace(str(_run_dir()), DOJO_CANON)) # canonicalize: the stored artifact reads the same everywhere\n", - " dlg.meta['llmdojo'] = dict(doced=doced_names(cells))\n", + " outs = [_replay_cells(cells, proj) for cells in localized_cells(dlg)]\n", + " refresh_dialog(dlg, outs)\n", " write_ipynb(dlg, str(dst or src))\n", " return dlg" ] @@ -599,12 +717,13 @@ "outputs": [], "source": [ "td = Path(tempfile.mkdtemp())\n", - "src = files('llmdojo')/'dojo_data'/'dojo_template.ipynb'\n", - "r1 = refresh_template(src, td/'r1.ipynb')\n", - "r2 = refresh_template(src, td/'r2.ipynb')\n", + "r1 = refresh_template(ROUND_PATH, td/'r1.ipynb')\n", + "r2 = refresh_template(ROUND_PATH, td/'r2.ipynb')\n", "test_eq((td/'r1.ipynb').read_bytes(), (td/'r2.ipynb').read_bytes())\n", - "test_eq(r1.meta['llmdojo']['doced'], read_ipynb(str(src)).meta['llmdojo']['doced'])\n", - "assert DOJO_CANON in r1.messages[0].ai_res" + "test_eq(r1.meta['llmdojo']['doced'], read_ipynb(str(ROUND_PATH)).meta['llmdojo']['doced'])\n", + "assert DOJO_CANON in r1.messages[0].ai_res\n", + "b1 = refresh_template(CLIK_BOOT, td/'b1.ipynb')\n", + "test_eq(b1.meta['llmdojo']['doced'], read_ipynb(str(CLIK_BOOT)).meta['llmdojo']['doced'])" ] }, { @@ -699,7 +818,7 @@ "id": "58ca5f50", "metadata": {}, "source": [ - "Template maintenance is one command, matching the layering: the launchers launch (and capture, which is backend-specific), while `dojobuild` owns the dialog-to-stores pipeline. Bare `dojobuild` is the common case - refresh the canonical dialog, then build both stores; chaining is safe because a refresh only regenerates machine-true receipts and `refresh_template` gates before writing. `--claude`/`--codex` build one store without refreshing: the post-review step after a capture or hand-curation." + "Template maintenance is one command, matching the layering: the launchers launch (and capture, which is backend-specific), while `dojobuild` owns the dialogs-to-stores pipeline. Bare `dojobuild` is the common case - refresh the two packaged clikernel dialogs (the shared round and the clikernel bootstrap), then build every store; chaining is safe because a refresh only regenerates machine-true receipts and `refresh_template` gates before writing. `--claude`/`--codex`/`--ipyai` build one store without refreshing: the post-review step after a capture or hand-curation. The ipyai build always replays, since its bootstrap is its own dialog and its tool renders through ipyai." ] }, { @@ -710,24 +829,25 @@ "outputs": [], "source": [ "#| export\n", - "@call_parse(pos=['dialog'])\n", + "@call_parse\n", "def main(\n", - " dialog:str=None, # Template dialog path; the packaged dojo_data/dojo_template.ipynb where omitted\n", - " claude:bool=False, # Build only the Claude store from the dialog, without refreshing\n", + " claude:bool=False, # Build only the Claude store, without refreshing\n", " codex:bool=False, # Build only the Codex store, without refreshing\n", + " ipyai:bool=False, # Build only the ipyai store (its bootstrap and the round replayed through ipyai's `py`), without refreshing\n", "):\n", - " \"Refresh the canonical template dialog (replaying its cells through a fresh clikernel), then build both stores\"\n", - " src = dialog or files('llmdojo')/'dojo_data'/'dojo_template.ipynb'\n", - " from llmdojo import claudedojo, codexdojo\n", - " if not (claude or codex):\n", - " refresh_template(src)\n", - " print(f'refreshed: {src}')\n", - " if not codex:\n", - " claudedojo.build_template(src)\n", - " print(f'built: claude store ({claudedojo.TMPL_DIR})')\n", - " if not claude:\n", - " codexdojo.build_template(src)\n", - " print(f'built: codex store ({codexdojo.TMPL_DIR})')" + " \"Refresh the packaged clikernel dialogs (the shared round and the clikernel bootstrap, replayed through a fresh clikernel), then build every store\"\n", + " from llmdojo import claudedojo, codexdojo, ipyaidojo\n", + " builds = dict(claude=claudedojo, codex=codexdojo, ipyai=ipyaidojo)\n", + " only = [n for n,f in dict(claude=claude, codex=codex, ipyai=ipyai).items() if f]\n", + " if not only:\n", + " for p in (ROUND_PATH, CLIK_BOOT):\n", + " refresh_template(p)\n", + " print(f'refreshed: {p}')\n", + " for n in only or builds:\n", + " b = builds[n]\n", + " r = b.build_template()\n", + " if n == 'ipyai': asyncio.run(r) # the ipyai replay is async (it drives a live kernel through ipyai's own client)\n", + " print(f'built: {n} store ({b.TMPL_DIR})')" ] }, { diff --git a/nbs/03_ipyaidojo.ipynb b/nbs/03_ipyaidojo.ipynb new file mode 100644 index 0000000..d502092 --- /dev/null +++ b/nbs/03_ipyaidojo.ipynb @@ -0,0 +1,452 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "c1d5e196", + "metadata": {}, + "source": [ + "# ipyaidojo\n", + "\n", + "> Start ipyai with a worked tooling session already in its history" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f48814c0", + "metadata": {}, + "outputs": [], + "source": [ + "#| default_exp ipyaidojo" + ] + }, + { + "cell_type": "markdown", + "id": "af404571", + "metadata": {}, + "source": [ + "#| export\n", + "An ipyai session is an aidialog `Dialog`, saved whole as an `.ipynb` under `./.ipyai/sessions/`, and ipyai rebuilds the model's context from that dialog on every turn (`dlg2hist`). So the template needs no host-native record format: the packaged dialogs already have the shape ipyai resumes. What differs from the clikernel hosts is the tool and the bootstrap. ipyai's model runs code through `py`, which runs the cell in the user's kernel and renders its outputs with the same `render_text` clikernel uses, so the baked round must show `py` calls; `ipyaidojo` gets them by replaying the cells through ipyai's own kernel client, with no model spend. And ipyai has no `clik` skill to document, so its bootstrap is its own dialog, captured from an ipyai session, assembled with the shared round at build time. Launch is then a session write plus `ipyai -r`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "43a45dc9", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "import asyncio, json, os, sys, tempfile\n", + "from importlib.resources import files\n", + "from fastcore.utils import *\n", + "from fastcore.script import call_parse\n", + "from aidialog.ipynb import read_ipynb, write_ipynb, reads_ipynb\n", + "from aidialog.msg_parts import ToolResponse, tool_text\n", + "from llmdojo.tmpl import save_store, load_store, load_reg, find_cid, launch_config, assemble, localized_cells, refresh_dialog, boot_gates\n", + "from llmdojo.tmpl import _seed_doced, _pmsgs, _msg_cells, _one_prompt" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8a2d1f45", + "metadata": {}, + "outputs": [], + "source": [ + "from fastcore.test import *\n", + "import shutil, ipyai.config as ipyai_config\n", + "from ipyai.kernel import KernelSession\n", + "from ipyai.bridge import setup_tools\n", + "from ipyai.session import resolve_session\n", + "from llmdojo.tmpl import TMPL_PROMPT\n", + "from aidialog.hist import reply2dlg, _parse_call" + ] + }, + { + "cell_type": "markdown", + "id": "bf8ef08e", + "metadata": {}, + "source": [ + "## The ipyai round" + ] + }, + { + "cell_type": "markdown", + "id": "f185805b", + "metadata": {}, + "source": [ + "The clikernel hosts' bootstrap opens with their own skill, `doc(clik, pysk, edsk)`, because their startup banner says so. ipyai's model never sees clikernel and gets no banner, so its bootstrap prompt names the reads itself, and its bootstrap dialog is captured from an ipyai session that answered it (`capture_boot`, below). The round is the shared one, every cell unchanged." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e82c3701", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "TOOL = 'py' # ipyai's model-facing tool: runs a cell in the user's kernel\n", + "BOOT_PATH = files('llmdojo')/'dojo_data'/'ipyai_boot.ipynb' # ipyai's bootstrap: one prompt, its reply the startup's doc() reads through `py`\n", + "BOOT_PROMPT = \"Bootstrap with py, one call each: doc(pysk, edsk), then doc(dsk, exh, rgsk), then doc(acp), then list_pyskills(). Then tell me when you're ready.\"" + ] + }, + { + "cell_type": "markdown", + "id": "1cd1c041", + "metadata": {}, + "source": [ + "## Replaying through ipyai" + ] + }, + { + "cell_type": "markdown", + "id": "cadbb70d", + "metadata": {}, + "source": [ + "`replay_cells` plays cells the way a live ipyai session's model does: a fresh kernel owned by ipyai's `KernelSession`, seeded by `setup_tools` (which also runs the user's `~/.config/ipyai/startup.py`, so the round's imports are the real ones, just as clikernel's replay runs the clikernel startup), and each cell through the `py` tool. The result is the text the model would read back, with media tags where a cell displayed an image." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3182e33a", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "async def replay_cells(\n", + " cells, # Kernel cell sources, run in order\n", + " cwd, # Directory to start the kernel in\n", + " env=None, # Extra environment entries for the kernel process\n", + "):\n", + " \"Each cell's `py` result from a fresh ipyai kernel at `cwd`: the text the model reads in a live session\"\n", + " from ipyai.kernel import KernelSession\n", + " from ipyai.bridge import setup_tools\n", + " k = await KernelSession().start(cwd=cwd, env=env)\n", + " try:\n", + " _,tools = await setup_tools(k.kc)\n", + " outs = []\n", + " for c in cells:\n", + " r = await tools.call_text(TOOL, dict(code=c))\n", + " outs.append(tool_text(r.content if isinstance(r, ToolResponse) else r))\n", + " return outs\n", + " finally: await k.close()" + ] + }, + { + "cell_type": "markdown", + "id": "65ce866a", + "metadata": {}, + "source": [ + "A bare expression comes back as its value, a printed line as its text with the newline it printed, and a silent cell as nothing at all, exactly as `py` renders them live (this needs a running rustygate):" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f6048361", + "metadata": {}, + "outputs": [], + "source": [ + "rd = Path(tempfile.mkdtemp())\n", + "outs = await replay_cells(['1+1', \"print('hi')\", 'x = 3'], rd)\n", + "test_eq(outs, ['2', 'hi\\n', ''])\n", + "outs" + ] + }, + { + "cell_type": "markdown", + "id": "ac099f8b", + "metadata": {}, + "source": [ + "## The template store" + ] + }, + { + "cell_type": "markdown", + "id": "9d7ae64b", + "metadata": {}, + "source": [ + "`ipyai_dialog` assembles ipyai's bootstrap with the shared round and makes it ipyai's: every baked call is parsed back to its cell, the cells are localized to the real run dir and replayed through `py`, each call is rewritten as a `py` call with its fresh result, the bootstrap and the round are gated, and the run dir is canonicalized again so the stored artifact reads the same everywhere. The store holds that dialog as one notebook dict, the completion id the replay earned, and the names the template documents." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "625f120b", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "TMPL_DIR = files('llmdojo')/'dojo_data'/'ipyai_store' # package data: compiled by dojobuild, shipped with the code\n", + "\n", + "async def ipyai_dialog(\n", + " boot=None, # ipyai's bootstrap dialog (or its path); the packaged one if None\n", + " rnd=None, # The round dialog (or its path); the packaged round if None\n", + " cwd=None, # Directory to replay in; the current directory if None\n", + "):\n", + " \"The template as ipyai plays it: its bootstrap and the shared round, calls renamed to `py`, results regenerated by replay, gated, canonicalized; returns `(dialog, outs)`\"\n", + " dlg = assemble(boot or BOOT_PATH, rnd)\n", + " outs = [await replay_cells(cells, cwd or '.') for cells in localized_cells(dlg)]\n", + " return refresh_dialog(dlg, outs, tool=TOOL), outs\n", + "\n", + "async def build_template(\n", + " boot=None, # ipyai's bootstrap dialog (or its path); the packaged one if None\n", + " rnd=None, # The round dialog (or its path); the packaged round if None\n", + " d=None, # Store dir; `TMPL_DIR` if None\n", + "):\n", + " \"Replay the template through ipyai and write the ipyai store: the dialog as a notebook dict, its completion id, and its doced list\"\n", + " with tempfile.TemporaryDirectory(prefix='ipyaidojo_') as td: dlg,outs = await ipyai_dialog(boot, rnd, td)\n", + " save_store(Path(d or TMPL_DIR), [json.loads(write_ipynb(dlg))], find_cid(L(outs).concat()), doced=dlg.meta['llmdojo']['doced'])\n", + " return dlg\n", + "\n", + "def load_template(\n", + " d=None, # Store dir; `TMPL_DIR` if None\n", + "):\n", + " \"The stored template items and metadata\"\n", + " return load_store(Path(d or TMPL_DIR))\n", + "\n", + "def template_dialog(\n", + " items, # Stored template items: one notebook dict\n", + "):\n", + " \"The template dialog read back from its stored notebook dict\"\n", + " return reads_ipynb(json.dumps(items[0]))" + ] + }, + { + "cell_type": "markdown", + "id": "8d4a4a00", + "metadata": {}, + "source": [ + "Building from the packaged dialog replays the whole round (about a minute, no model spend), here with the llmdojo state redirected to the store dir so the replay's receipts stay out of the real registry and a parallel notebook run cannot share its run dir. The result opens with the swapped bootstrap read and calls `py` throughout, and the store reads back to the same reply:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cd15bacb", + "metadata": {}, + "outputs": [], + "source": [ + "tstore = Path(tempfile.mkdtemp())\n", + "os.environ['LLMDOJO_STATE_DIR'] = str(tstore)\n", + "try: tdlg = await build_template(d=tstore)\n", + "finally: del os.environ['LLMDOJO_STATE_DIR']\n", + "test_eq([m.content for m in tdlg.messages], [BOOT_PROMPT, TMPL_PROMPT])\n", + "calls = [_parse_call(m.content) for pm in tdlg.messages for m in reply2dlg(pm).messages if m.msg_type=='code']\n", + "test_eq({c[0] for c in calls}, {TOOL})\n", + "assert 'clik' not in tdlg.meta['llmdojo']['doced']\n", + "items,meta = load_template(tstore)\n", + "test_eq(template_dialog(items).messages[1].ai_res, tdlg.messages[1].ai_res)\n", + "meta" + ] + }, + { + "cell_type": "markdown", + "id": "e717b72f", + "metadata": {}, + "source": [ + "## Capturing the bootstrap\n", + "\n", + "ipyai's bootstrap is played live, in ipyai: a fresh session in a Python project answers `BOOT_PROMPT` with the doc reads through `py`, and `capture_boot` takes that reply from the session file. The session is already a dialog in the template's shape, so the capture is a selection, not a conversion: the last prompt whose reply passes `boot_gates` (nothing but doc reads and catalog calls), with the prompt text set to the canonical `BOOT_PROMPT`, written as a one-prompt dialog for review and for copying over the packaged `ipyai_boot.ipynb`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9d2a7f80", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "async def capture_boot(\n", + " sess=None, # An ipyai session id or name prefix; the project's newest session if None\n", + " cwd=None, # Project directory; the current directory if None\n", + " d=None, # Template store dir; `TMPL_DIR` if None\n", + "):\n", + " \"ipyai's bootstrap from a session that answered `BOOT_PROMPT`: the last prompt whose reply is nothing but doc reads, replayed for full results, written to `boot.ipynb` beside the store\"\n", + " from ipyai.session import resolve_session, list_sessions\n", + " path = resolve_session(sess, cwd or '.') if sess else list_sessions(cwd or '.')[0][0]\n", + " dlg = read_ipynb(str(path))\n", + " pm = last(m for m in _pmsgs(dlg) if not boot_gates(_msg_cells(m)[2]))\n", + " if pm is None: raise ValueError(f'no bootstrap reply (a prompt answered only with doc reads) in {path}')\n", + " b = _one_prompt(pm, 'ipyai_boot')\n", + " b.messages[0].content = BOOT_PROMPT\n", + " with tempfile.TemporaryDirectory(prefix='ipyaidojo_') as td: outs = [await replay_cells(cells, td) for cells in localized_cells(b)]\n", + " refresh_dialog(b, outs, tool=TOOL) # the session file stores tool results truncated; the replay restores the full text the model read\n", + " out = Path(d or TMPL_DIR)/'boot.ipynb'\n", + " write_ipynb(b, str(out))\n", + " return out" + ] + }, + { + "cell_type": "markdown", + "id": "af753758", + "metadata": {}, + "source": [ + "## Launching" + ] + }, + { + "cell_type": "markdown", + "id": "6af3a0f4", + "metadata": {}, + "source": [ + "`prep_dojo` writes the template as a session of the target project through ipyai's own `Session` (so the `.ipyai/sessions/` layout and its self-ignoring `.gitignore` are ipyai's), registers the completion id, and returns the session id that `ipyai -r` resumes. Resuming paints the round and hands the dialog to the model as real `py` tool calls." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b83fc9a2", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "def prep_dojo(\n", + " cwd=None, # Project to start in; the current directory if None\n", + " d=None, # Template store dir; `TMPL_DIR` if None\n", + "):\n", + " \"Write the template as a session of `cwd` through ipyai's `Session`, register its completion id, seed its doc-state, and return the session id to resume\"\n", + " from ipyai.session import Session\n", + " items,meta = load_reg(d, TMPL_DIR, 'ipyaidojo')\n", + " s = Session(root=cwd or '.')\n", + " s.save(template_dialog(items))\n", + " if ds := meta.get('doced'): _seed_doced(s.path.stem, ds) # ipyai names the session stem in the kernel's LLMDOJO_HOST_ID\n", + " return s.path.stem" + ] + }, + { + "cell_type": "markdown", + "id": "5707bb90", + "metadata": {}, + "source": [ + "Against temporary dirs, with the llmdojo state redirected so the registration stays off the real completion record:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8e5b35bb", + "metadata": {}, + "outputs": [], + "source": [ + "mproj = Path(tempfile.mkdtemp())\n", + "os.environ['LLMDOJO_STATE_DIR'] = str(tstore)\n", + "try: psid = prep_dojo(mproj, tstore)\n", + "finally: del os.environ['LLMDOJO_STATE_DIR']\n", + "back = read_ipynb(resolve_session(psid, mproj))\n", + "test_eq([m.content for m in back.messages], [BOOT_PROMPT, TMPL_PROMPT])\n", + "test_eq(back.messages[1].ai_res, tdlg.messages[1].ai_res)\n", + "psid" + ] + }, + { + "cell_type": "markdown", + "id": "fe10b4ad", + "metadata": {}, + "source": [ + "The launcher is a `call_parse` CLI: `--sid` prints the prepared id instead of launching, `--capture` takes ipyai's bootstrap from the project's newest session, unrecognized flags are forwarded to `ipyai`, and standing arguments come from the `ipyai_args` list in `$XDG_CONFIG_HOME/ipyaidojo/config.toml` through `launch_config('ipyai')`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2ab65f78", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "@call_parse(nested=True)\n", + "def main(\n", + " sid:bool=False, # Print the prepared session id instead of launching ipyai\n", + " capture:bool=False, # Capture ipyai's bootstrap from this project's newest session into the store's `boot.ipynb`, and exit\n", + "):\n", + " \"Prepare an ipyai session opening with the worked round and launch `ipyai -r` on it; template maintenance is `dojobuild`\"\n", + " if capture: return print(f\"wrote: {asyncio.run(capture_boot())}\")\n", + " s = prep_dojo()\n", + " if sid: return print(s)\n", + " os.execvp('ipyai', ['ipyai', *launch_config('ipyai'), '-r', s, *sys.argv[1:]])" + ] + }, + { + "cell_type": "markdown", + "id": "15a1ab46", + "metadata": {}, + "source": [ + "## Rules in ipyai kernels" + ] + }, + { + "cell_type": "markdown", + "id": "a9be0e8f", + "metadata": {}, + "source": [ + "llmdojo's `ipyai/startup.py` (linked into `~/.config/ipyai/`) also installs the session rules, and applies them to the model's cells only: ipyai runs the model's `py` cells with `store_history=False` and the user's typed cells with `True`, so IPython's `pre_run_cell` info tells them apart. A blocking rule rejects the model's cell and the rejection reaches the model as the tool result; the same cell typed by the user runs untouched. Here against the checkout's startup file, with the kernel's llmdojo state kept in a temp dir:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "30a79a9a", + "metadata": {}, + "outputs": [], + "source": [ + "ipyai_config.STARTUP_PATH = Path(files('llmdojo')).parent/'ipyai'/'startup.py'\n", + "rd2 = Path(tempfile.mkdtemp())\n", + "k = await KernelSession().start(cwd=rd2, env=dict(LLMDOJO_STATE_DIR=str(rd2)))\n", + "try:\n", + " _,tools = await setup_tools(k.kc)\n", + " blocked = await tools.call_text(TOOL, dict(code='!echo hi'))\n", + " user = await k.kc.run('!echo hi')\n", + "finally: await k.close()\n", + "assert 'RuleBlock' in blocked and 'Bash tool' in blocked\n", + "test_eq(user[0]['text'].strip(), 'hi')\n", + "blocked.splitlines()[-1]" + ] + }, + { + "cell_type": "markdown", + "id": "270748da", + "metadata": {}, + "source": [ + "## Cleanup" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "da957120", + "metadata": {}, + "outputs": [], + "source": [ + "for p in (rd, rd2, tstore, mproj): shutil.rmtree(p)" + ] + }, + { + "cell_type": "markdown", + "id": "0671b0be", + "metadata": {}, + "source": [ + "## Export -" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "958d0b68", + "metadata": {}, + "outputs": [], + "source": [ + "#|hide\n", + "#|eval: false\n", + "import nbdev; nbdev.nbdev_export()" + ] + } + ], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/nbs/index.ipynb b/nbs/index.ipynb index fe7f773..eca6056 100644 --- a/nbs/index.ipynb +++ b/nbs/index.ipynb @@ -59,7 +59,7 @@ "$ pip install llmdojo\n", "```\n", "\n", - "Activation is two lines of clikernel user config: register the rules in `$XDG_CONFIG_HOME/clikernel/inspectors.py` (`from llmdojo.rules import make_inspector, RuleBlock; inspectors = [make_inspector()]`), and have your startup file print the bootstrap instructions that tell the agent to run the dojo." + "Activation is two lines of clikernel user config: register the rules in `$XDG_CONFIG_HOME/clikernel/inspectors.py` (`from llmdojo.rules import make_inspector, RuleBlock; inspectors = [make_inspector()]`), and have your startup file print the bootstrap instructions that tell the agent which docs to read. For ipyai, link `ipyai/startup.py` into `~/.config/ipyai/` (see `ipyai/README.md`): its kernels then carry the same imports and the rules, applied to the model's cells only, and `ipyaidojo` launches sessions with the bootstrap and the round already in history." ] }, { @@ -89,6 +89,12 @@ "```sh\n", "$ claudedojo -r\n", "$ codexdojo -r\n", + "```\n", + "\n", + "ipyai starts warm the same way. `ipyaidojo` writes the round as an ipyai session of the current directory and runs `ipyai -r` on it (standing arguments from `ipyai_args` in `$XDG_CONFIG_HOME/ipyaidojo/config.toml`, `--sid` prints the id). Its kernels get the round's imports from `~/.config/ipyai/startup.py`, see `ipyai/README.md`:\n", + "\n", + "```sh\n", + "$ ipyaidojo\n", "```\n" ] }, @@ -105,15 +111,15 @@ "id": "7db41ff1", "metadata": {}, "source": [ - "The templates ship inside the package - the canonical dialog plus the compiled per-host stores - so *using* fresh templates takes no action beyond updating llmdojo. Maintaining them is one command: when tooling docs or skills change, the baked round's outputs go stale, and `dojobuild` regenerates them with no model spend - it replays the round's cells through a fresh kernel, splices the current outputs into the canonical template dialog, and recompiles both stores, all in your checkout for review:\n", + "The templates ship inside the package - the packaged dialogs plus the compiled per-host stores - so *using* fresh templates takes no action beyond updating llmdojo. A template is two prompts: a bootstrap (the host's startup doc reads, captured by playing the bootstrap prompt in that host: `clik_boot.ipynb` for Claude and Codex, `ipyai_boot.ipynb` for ipyai) and the shared round (`dojo_round.ipynb`, captured once from Claude). Maintaining them is one command: when tooling docs or skills change, the baked outputs go stale, and `dojobuild` regenerates them with no model spend - it replays the packaged clikernel dialogs' cells through a fresh kernel, splices the current outputs in, and recompiles every store from each host's bootstrap plus the round, all in your checkout for review (the ipyai store replays its own bootstrap and the round through ipyai's `py`, so `dojobuild` needs ipyai installed and a running rustygate):\n", "\n", "```sh\n", "$ dojobuild\n", "```\n", "\n", - "Only a change to the round itself - its cells, not their outputs - needs a fresh capture: `claudedojo --capture` plays a scripted round headlessly, `codexdojo --capture` does the same in a Codex child, and `--current` on either stores a clean round an existing session already played. After reviewing a captured dialog, `dojobuild --claude` and `dojobuild --codex` compile it into each store without re-replaying.\n", + "Only a change to the round itself - its cells, not their outputs - needs a fresh capture: `claudedojo --capture` plays the two prompts headlessly, `codexdojo --capture` does the same in a Codex child, and `--current` on either stores a clean round an existing session already played; each writes `boot.ipynb` and `round.ipynb` beside its store for review, to copy over the packaged pair. A change to a host's bootstrap reads is a new bootstrap capture for that host (`ipyaidojo --capture` after playing its prompt in ipyai). After reviewing, `dojobuild --claude`, `--codex`, and `--ipyai` compile one store each without re-replaying the clikernel dialogs.\n", "\n", - "The bootstrap `doc()` reads (the cells before `dojo_start()`) live in two artifacts: `dojo_data/capture_prompt.md`, the script future captures replay, and the baked round in the canonical dialog itself. To change the set, update both, keeping each new `doc(x)` as its own cell placed after the `doc(clik, pysk, edsk)` one, then run `dojobuild`, which replays the new cell for a true output and recomputes the stored `doced` list. Updating only one leaves the demonstration and the capture script teaching different bootstraps." + "The bootstrap reads are named in two places that must agree: the host's startup text (`claude/startup.txt`, or `ipyaidojo.BOOT_PROMPT` for ipyai, which has no banner) and `dojo_data/capture_prompt.md`, the script future captures replay. To change the set, update both, then recapture the bootstrap." ] }, { diff --git a/nbs/llms.txt b/nbs/llms.txt index 8e38931..f253fc4 100644 --- a/nbs/llms.txt +++ b/nbs/llms.txt @@ -4,6 +4,7 @@ ## Modules +- [llmdojo.ipyaidojo](https://AnswerDotAI.github.io/llmdojo/ipyaidojo.html.md): Start ipyai with a worked tooling session already in its history - [llmdojo.tmpl](https://AnswerDotAI.github.io/llmdojo/tmpl.html.md): The template layer shared by claudedojo and codexdojo diff --git a/pyproject.toml b/pyproject.toml index 5835a36..28b7e5e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -31,6 +31,7 @@ llmdojo = "llmdojo._modidx:d" claudedojo = "llmdojo.claudedojo:main" codexdojo = "llmdojo.codexdojo:main" dojobuild = "llmdojo.tmpl:main" +ipyaidojo = "llmdojo.ipyaidojo:main" [tool.setuptools.dynamic] version = {attr = "llmdojo.__version__"}