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mte90/linus-torvalds-skill

Developer tools
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Distills Linus Torvalds' code-review methodology from his LKML emails into a reusable, language-agnostic skill.

概要

Distills Linus Torvalds' code-review methodology from his LKML emails into a reusable, language-agnostic skill.

README

Torvalds Skill

Distills Linus Torvalds’ code-review methodology from his LKML emails into a reusable, language-agnostic skill.

Built from 38,293 real review moves extracted from 31,397 of his emails (2002–2026) on the Linux kernel mailing list, plus 67 interview transcripts.

Quick Start

Use the skill in your AI coding assistant

  1. Pick a skill variant from linus-torvalds-skill/:

    • SKILL.md — gpt-oss-120b (balanced, recommended)
    • SKILL-GLM.md — glm5.2 (most detailed, reasoning model)
    • SKILL-Mistral.md — mistral (concise)
  2. Add it to your system prompt or skill registry:

    • Copy the contents of SKILL.md into your AI assistant’s system prompt, OR
    • Register the skill file path in your tool’s skill configuration
  3. What you get: The skill instructs the AI to review code with Linus’ principles:

    • Correctness > Performance > Complexity > Style (precedence hierarchy)
    • Language-agnostic triggers (works for any language, not just C/kernel)
    • Severity calibration from 38,000+ real review moves
    • Concrete definitions for “bug”, “hack”, “patch”, “API contract”

Use the soul persona

  1. Pick a soul variant from soul/:

    • soul.md — gpt-oss-120b
    • soul-glm.md — glm5.2 (most detailed)
    • soul-mistral.md — mistral
  2. Use it as a system prompt for Linus-style code review persona.

Setup

uv sync
cp .env.example .env
# Edit .env: LLM_HOST, LLM_MODEL, LLM_API_KEY

Pre-built Data

The data/ directory (mbox, extracted moves, patterns, calibration data) is not committed — it’s large and regenerable. It is published as a release asset on the repository’s Releases page and updated when the pipeline produces new artifacts.

Download and extract it into the project root instead of running the full pipeline:

# From the Releases page, download data.tar.gz and extract:
tar xzf data.tar.gz

This gives you data/moves.jsonl, data/patterns.json, data/calibration.json, and all other artifacts needed to regenerate skill and soul files without fetching 31,000 emails or spending LLM API calls.

Run the full pipeline only if you want to re-extract from source (costs ~$5–8 in API calls, several hours).

Configuration

Variable Default Description
LLM_HOST https://api.regolo.ai/v1 LLM API endpoint
LLM_MODEL gpt-oss-120b Model for extraction and distillation
LLM_API_KEY — API key (required)

CLI flags override env vars: --model, --out.

Documentation

Document Purpose
docs/pipeline.md Full pipeline architecture, data flow, stage details
docs/models.md Model variants, word counts, tradeoffs
docs/validation.md SmallChat validation (with-skill vs baseline methodology)
soul/README.md What a soul document is and how to generate it
report/comparison.md Three-model comparison with delta analysis

License

Everything in this repository — source code, pipeline scripts, the distilled skill, the soul document, and documentation — is released to the public domain under CC0 1.0.

View this README on GitHub

推奨ツール

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インストール

npx skillfish add mte90/linus-torvalds-skill