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Metis

Metis is a coding skill for LLM agents. It gives the model strong defaults for design, implementation, testing, code review, and pre-commit verification, and loads section by section so only the current phase's guidance is in play.

What it pushes models toward

  • plain-data domain models over behavior-heavy object hierarchies
  • top-down API design and call-site-first thinking
  • explicit control flow
  • boundary parsing and internal invariants instead of implicit assumptions
  • behavior-focused testing
  • early performance thinking
  • sparse, useful comments instead of code narration
  • tiered code review: sequential lens passes by default, sub-agent fan-out for large diffs

These are defaults, not laws. Metis is opinionated, but it should improve the local codebase, not fight it.

Install

npx skills add https://github.com/gitRasheed/metis-skill --skill metis -g

Add -a codex or -a claude-code to install for a single agent. Update with npx skills update -g. Release notes live on the releases page.

Use

Metis auto-loads for relevant coding work. You can also invoke it explicitly with $metis or /metis, depending on the agent UI.

Evals

Every non-trivial rule was validated or discovered through isolated baseline-vs-Metis runs, and changes that failed their A/B were rejected rather than shipped.

The latest round (evals/cases-v6/) ran two preregistered waves over six skill texts plus an external leg on the Aider polyglot benchmark: every arm passed all 72 test runs, and a blinded judge still preferred Metis-guided code 3:1 over the no-skill baseline. The winner shipped as v1.3.0. evals/cases-v6/REPORT.md has the full record, including scoring method; earlier rounds live in evals/cases-v3/ through evals/cases-v5/.

What is in this repo

  • skills/metis/ is the canonical skill
  • .claude/skills/metis/ mirrors it for Claude Code
  • PORTABLE_PROMPT.md is the plain-markdown version for tools without skill support
  • evals/ holds the eval cases, scoring keys, and reports; raw run output stays git-excluded

Sources

The core ideas are borrowed, then filtered through the evals:

The rest (review lenses, fan-out thresholds, thrash detection, LLM-test cleanup) came out of the eval rounds themselves.

About

Steers coding agents toward call-site-first design, plain-data domain models, and tests that pin behavior.

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