Someone took every code review email Linus Torvalds ever sent to the Linux kernel mailing list, all 32,000 of them, and turned them into an AI skill. Not a summary of his opinions. A distilled personality the model loads before it looks at your diff. This Reddit user, u/Mte90, posted the follow-up report in r/PromptEngineering, and it’s not the first pass. It’s a “here’s what changed since I announced this” writeup, with real fixes and real comparisons attached.
That framing matters. A lot of “I built a persona skill” posts stop at the announcement. This one comes back with a report card: what got better, what the first version missed, and how the thing performs against a baseline. That is rarer than it should be.
Here is the twist. The build splits into two files instead of one. A Skill.md carries the procedure, the actual review technique: what to flag, what to ignore, how to phrase a rejection. A separate Soul.md carries the voice, and the post is upfront that it keeps the profanity intact. That is the part people are arguing about in the comments, one calling the whole idea disrespectful. The technical trick underneath is worth stealing regardless of how you feel about the source material. I get why the tone split makes some people uneasy, cloning someone’s exact voice, swearing included, is a different move than cloning their reasoning. But that’s a debate about the target, not the method.
The second twist: the creator did not stop at one version. Four different LLMs each got the same 32k-email corpus and ran their own distillation pass, producing four separate Skill/Soul pairs from identical inputs. Then came the part I actually care about, a side-by-side comparison of code reviews done with the skill loaded versus without it. That before-and-after is exactly what most persona-skill posts skip.
Here is the mini-workflow, reconstructed from what the original poster laid out.
- 🗂️ Pull the raw corpus. Mailing list archives, in this case 32k review emails, are the training ground. Any large body of one person’s written feedback works the same way: support tickets, old PR comments, a Slack channel history.
- 🧠 Split the extraction into two passes. One pass distills the “how,” the review procedure. A second pass distills the “who,” the tone and word choice, kept in its own Soul.md so it can be swapped without touching the logic.
- 🔁 Run the same distillation prompt across multiple LLMs. Four models, four independent takes on the same source material. That way you can compare which one actually sounds right, instead of trusting the first output you get.
- 📊 Test with and without the skill loaded. Same diffs, same base prompt, skill on versus skill off. Then compare the review quality side by side to see if the persona is actually adding signal.
One sharp comment on the thread added a detail worth stealing on its own. Hold back a set of diffs that never touch the training process. Use that held-out set to check whether the skill actually generalizes, instead of just memorizing the examples it was built on. Skip that step and you can’t tell if your persona skill learned a style or just parroted its inputs back at you.
Pro tip: the Skill/Soul split is the real takeaway here, more than the Linus angle specifically. Procedure and personality are different kinds of knowledge, and cramming them into one file makes both harder to tune later. Build the “how” and the “who” as separate files, and you can swap either one without breaking the other.
Second pro tip: running the same distillation across four different LLMs is a cheap way to stress-test your own assumptions about what “the voice” even is. If four models converge on similar phrasing, you probably captured something real. If they diverge wildly, your source material was thinner than it looked.
Third pro tip: keep that held-out diff set. It’s the difference between “this feels like Linus” and “this measurably reviews code the way Linus would.” One is vibes, the other is a benchmark you can rerun every time you tweak the files.
The original poster included a reproducible pipeline, so this is not a one-off party trick. It’s a method you can point at your own inbox, your own old PR history, or any expert’s written feedback you have sitting around. Worth a read if you’ve ever wanted to bottle someone’s expertise into something a model can actually use.
Go check out the full writeup and the Reddit thread. The pipeline details, the four Skill/Soul versions, and the with/without comparison data are all there. 🏴☠
Linus Torvalds Skill/Soul or how I distilled the knowledge for code reviews from 32~k emails
by u/Mte90 in PromptEngineering