feat(eval): add reproducible research campaign runbook - #51
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What does this PR do?
Adds a reusable ShellBench research campaign skill plus native planning and
post-run analysis support for retained ten-task r0 qualification followed by
reproducible n=3 through n=6 benchmark matrices.
Why?
The native planner previously rejected repetition zero, treated every task
subset as a repair, limited full repetitions to 1-3, and produced identical run
labels across reasoning levels. That makes a safe family/harness qualification
stage impossible and leaves model identity, installed harness versions,
turn/tool usage, and cost provenance spread across raw artifacts.
This change makes those requirements explicit and machine-auditable before a
campaign is called clean.
Related trace publishing work remains in
#48. The runbook uses that verified
uploader when available and documents a private AWS CLI fallback without
embedding a bucket or credentials.
Changes
shellbench-research-runbookskill with:gpt-5.6-solathighas the default judgejudge model metadata, and configurable positive repetition counts.
representative model, recorded family provenance, and automatic leaderboard
exclusion.
r0throughr6and preserve r0 exclusionmetadata through retries and remote manifests.
research_audit.pyexports for task traces, run model identity, turnusage, tool calls, installed harness versions, and cost provenance.
Tests
python -m pytest -q tests/test_native_eval_runner.py tests/test_native_eval_fleet.py tests/test_native_eval_research_audit.pypython -m ruff check scripts/native_eval tests/test_native_eval_runner.py tests/test_native_eval_research_audit.pypython -m ruff check clawbench app.py scripts tests && python -m pytest -qtbx_01kypnawgaghyp5syryqspbqqs