Distills Linus Torvalds' code-review methodology from his LKML emails into a reusable, language-agnostic skill.
Обзор
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
-
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)
-
Add it to your system prompt or skill registry:
- Copy the contents of
SKILL.mdinto your AI assistant’s system prompt, OR - Register the skill file path in your tool’s skill configuration
- Copy the contents of
-
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
-
Pick a soul variant from
soul/:soul.md— gpt-oss-120bsoul-glm.md— glm5.2 (most detailed)soul-mistral.md— mistral
-
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.
Рекомендуемые инструменты
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Установка
npx skillfish add mte90/linus-torvalds-skill