I was wondering if we have https://github.com/multica-ai/andrej-karpathy-skills/ that is a condensed way to create a AGENTS.md file from an opinion of a specific person what we can do with more content from a person?
I used the karpathy skill to create my AGENTS.md but it will be a topic for another blog post
So I was thinking, where I can find content from a person, that can be helpful for an AI that is public domain?
Why don’t distill the Code Review skills from Linus Torvalds in over 2 decades in the Kernel mailing list?
So after some working I got https://github.com/Mte90/linus-torvalds-skill that I decide to be always reproducible.
Every generated file carries traceability metadata in its frontmatter (prompt hash, input hash, model, pipeline version). Generated artifacts are never hand-edited — any change goes through the generator script.
The data folder is a release asset, the full pipeline ~2,000 LLM calls, and python3 scripts/run_pipeline.py --dry-run shows every stage before spending a cent.
This means that there are scripts to download and parse the various emails, exclude the one that are commits and stuff not useful (so from 32~k emails they are 19~k).
Before public announcement
The first version was a single SKILL.md generated from the email corpus — no soul, no calibration, no interviews, no validation.
The first step it was to ask to the agents to build a pipeline to download and evaluate the various emails, and a script that generates the skill based on the email content.
The next step it was to generate the same skill with different models to see the difference, as I am part of Regolo.AI that part it was easy.
The first generation attempts included C-specific terms like BUG_ON and READ_ONCE, making the skill useless for non-C projects.
Fixing this required a forbidden-terms list and a post-processor.
With this first version I was thinking that was ready for the world…
After public announcement
- https://www.reddit.com/r/opensource/comments/1vgbn0i/github_mte90linustorvaldsskill_distilled_code/
- https://www.reddit.com/r/LLMDevs/comments/1vgbshe/mte90linustorvaldsskill_distilled_code_reviewer/
- https://www.reddit.com/r/PromptEngineering/comments/1vgbvym/from_prompts_to_reusable_skills_a_linusinspired/
- https://www.reddit.com/r/codereview/comments/1vgxvwp/mte90linustorvaldsskill_distilled_code_reviewer/
- https://x.com/Mte90Net/status/2090088911850618919
After sharing to the world (apart the usual sarcastic people) I got some useful suggestions:
What the community asked — and what changed
| Feedback | What I shipped |
|---|---|
| “Did you even use the skill?” | Validated on antirez/smallchat: 8 reviews (4 models × with-skill/baseline), consensus matrix + a 43-bug ground-truth benchmark |
| “Why not a SOUL.md?” | 4 soul variants (identity/values/voice, separate from the rules) |
| “The skill quality is bad” | Quality gates: verify_skill.py scores 0–100 (SKILL.md: 95/100), 1,026 tests, forbidden-C-terms enforcement |
| “The report isn’t useful” | Consensus matrix, severity disagreement table, trigger effectiveness metrics |
| “Why not interviews?” | 67 interview transcripts fused into the corpus |
| “Upload the data folder” | Published as release assets (data.tar.gz) — regenerate with your own models |
| Where are the profanities? | In soul.md are present |
Right now I want to present to you the first official release of the project with this all these improvements (and many others)!
I used a lot GLM 5.3 that it was released in the meantime to review it and improve it (GLM 5.2 and Qwen3.5-122b are used for the development).
The actual LLMs used (from Regolo.AI):
- GLM 5.2
- Mistral-small-4-119b
- GPT-OSS-120b
- Qwen3.8-27b
The pipeline, the prompt generation and everything else is the same for the various models but the output is completely different.
Actual pipeline is:
- 31,397 emails fetched (192MB mbox from gmane NNTP)
- After classify.py filters out git-pull/patch/RFC/announcements → review-only subset
- 38,293 moves extracted (not emails — each email can yield multiple moves)
- 325 representative patterns (25 per category × 13 categories)
- 1 skill/soul file per model
In the report folder there is a comparison from all the models with and without the skill with the antirez/smallchat project.
Some excerpts from the comparison (auto-generated) as 10/09/2026:
| Model | Total Findings | Critical Findings | Skill-Only Critical | Verdict |
|---|---|---|---|---|
| gpt-oss-120b | 15 | 4 | 4 | Skill adds value |
| glm5.2 | 7 | 2 | 0 | Skill reduces coverage |
| mistral | 22 | 9 | 9 | Skill adds value |
| qwen3.8-27b | 18 | 6 | 4 | Skill adds value |
The skill adds the most value for mistral, which gained 9 critical finding(s) exclusive to the with-skill review.
For each model, comparing findings with the skill vs without (baseline):
| Model | Baseline Total | With-Skill Total | Baseline CRITICAL | With-Skill CRITICAL | Critical Overlap | Skill-Only CRITICAL | Baseline-Only CRITICAL | Skill Added Value |
|---|---|---|---|---|---|---|---|---|
| gpt-oss-120b | 7 | 15 | 0 | 4 | 0 | 4 | 0 | yes (+4 net critical: 4 found, 0 lost) |
| glm5.2 | 12 | 7 | 4 | 2 | 2 | 0 | 2 | no (-2 net critical: 0 found, 2 lost) |
| mistral | 15 | 22 | 0 | 9 | 0 | 9 | 0 | yes (+9 net critical: 9 found, 0 lost) |
| qwen3.8-27b | 11 | 18 | 2 | 6 | 2 | 4 | 0 | yes (+4 net critical: 4 found, 0 lost) |
Findings confirmed by 2+ models are treated as real bugs. Findings reported by only one model are unverified (could be real or false positive).
| Model | Total Findings | Confirmed (2+ models) | Unverified (1 model only) | Consensus Rate |
|---|---|---|---|---|
| gpt-oss-120b | 15 | 12 | 3 | 80% |
| glm5.2 | 9 | 9 | 0 | 100% |
| mistral | 22 | 14 | 8 | 64% |
| qwen3.8-27b | 19 | 14 | 5 | 74% |
Looking a the comparison is clear that GLM and Mistral are the most interesting to find bugs.
I suggest to read the comparison (that is generated automatically so has margin for improvements).
Conclusion
This project was fully generated by AI – and my only real job was reviewing its work.
Which is exactly what the skill teaches. I just never expected to be on the receiving end of a Linus-style review of my own pipeline.
But I think that is my actual most used duty in my job as developer, review what an agent does.
The repository is online and open to everyone for feedback and suggestions.
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