Yesterday a small repo called mimic showed up on r/PromptEngineering, and it goes after the thing most of us quietly hate about LLM writing. The output isn’t bad. It’s just flat. You read three paragraphs and nothing is wrong, but nobody is home. Mimic is a way to point Claude at a folder of text and get back a skill that writes like that author.
What’s new
The builder (u/kaisoma) ran one prompt through different voices: “tell a friend who keeps staying up too late that they should go to bed earlier.” Same task every time, which keeps the comparison honest. If you change the task between runs, you can’t tell whether a difference came from the voice or from the topic.
Poe gave a lamp-lit scolding: “Very, very wakeful you declare yourself to be.” Austen gave a drawing room: “you are very agreeable at dinner, and not at all so by breakfast.” Paul Graham gave a tight argument: “The person who stays up isn’t the person who wakes up.”
Read those three lines again. The advice underneath is identical. Go to bed. What changes is the shape of the sentence: the repetition in Poe, the polite sting in Austen, the compressed logic in Graham. That shape is what the project is trying to capture.
There’s no fine-tuning and no custom model. It’s one markdown skill file plus a roughly 150-line stylometry script with zero dependencies. That matters for a practical reason. You don’t need a GPU, an API budget for training, or a pile of installs. If you can run a small script and drop a file into your skills folder, you can try this today.
The twist
The author’s claim is that a voice is mostly structure, not vocabulary. That means sentence length and how much it varies, how often words repeat, which function words and punctuation keep coming back, and the rhetorical moves a writer makes again and again. Think about how a writer who loves semicolons or opens with a short blunt sentence feels different from one who never does, even when both use plain words.
Then the part I found interesting. On a pure function-word and rhythm metric, the skill was basically indistinguishable from just telling the model “write like X.” But when a blind LLM judge read the outputs, it picked the skill version every time. The numbers can’t see the moves. A reader notices them right away.
That gap is the whole lesson. A metric can say two texts have similar rhythm while a human, or a judge model, still feels one of them is the real thing. So don’t trust a score alone. Read the output out loud and see if it sounds like a person.
Treat that as one builder’s result, not a benchmark. The post also says Claude works best and GPT works worst, so test on your model before you trust it. If you run a different model, expect to do a few more rounds of tweaking.
Mini-workflow
- 📁 Collect a folder of text from the writer you want. Your own emails, posts, or newsletters count too. Plain text or markdown files are the easiest, so convert PDFs first and strip out headers, footers, and page numbers.
- 🔍 Point your agent at the repo (github.com/xatuke/mimic) and the folder. Name the folder clearly so the agent doesn’t mix it up with other files in your project.
- ✍️ The script measures the corpus (rhythm, repetition, punctuation, function words), then the agent writes the skill and checks its own output back against those numbers.
- ✅ It asks before installing anything. Say yes only if the sample reads right. If it feels off, tell the agent what’s wrong in plain words, like “too formal” or “sentences are too even,” and let it revise.
Pro tips
- Keep one fixed test prompt for every voice you build, just like the post did. Otherwise you’re comparing tasks, not styles. The bedtime prompt works because it’s small, personal, and has an obvious point of view to argue.
- Do the blind check a commenter suggested. Hand someone the outputs without author labels and see if they can tell which is which. If you can’t guess, the skill is working. Better still, include a plain “write like X” version in the pile and see whether people prefer the skill output.
- Feed it a lot of text from one source. More samples give the script steadier numbers, and a mix of formats (long pieces and short ones) shows the range of the writer. A commenter asked whether it works on whole non-fiction books. Nobody has answered yet, so that’s an open experiment.
- Don’t mix authors in one folder. If two voices go in, you’ll get an average that sounds like neither. One folder per voice, one skill per folder.
- Your best use might not be a famous author. Distilling your own voice from your own writing is the practical version. Your sent emails and past newsletters are a ready-made corpus, and the result is something you’d actually use every week.
Your move
Grab a folder of your best writing, run it through mimic, and compare the result against a plain “write like me” prompt. Use the same task for both, then give the two outputs to a friend with no labels. If the skill version wins on a blind read, you’ve got a reusable voice file you own. Tell me which author you tried first 🏴☠
Frequently Asked Questions
Q: How would you know the output sounds like the author and not just their catchphrases?
One commenter suggested a blind check: give people the outputs without author labels and see if they can match them to the names. Poe and Austen are easy because their tics are loud, so the harder test is two authors from the same period and genre. If those still come out distinguishable, the method is picking up rhythm and structure, not just surface phrases.
Q: Why would sentence structure matter more than vocabulary for style?
A commenter who has worked on style transfer for months said they kept getting stuck on vocabulary when the real signal was structure. Function words like ‘the’, ‘but’, and ‘of’ are used almost automatically, so their distribution works like a fingerprint that survives any topic. If you’re tuning a prompt, check rhythm and sentence shape before you chase word choice.
Q: Can you add a plain context file for vocabulary level and explanation depth, like a children’s science series, without setting up RAG or MCP?
One commenter floated exactly that: a static file describing the target vocabulary and explanation depth, loaded alongside the style profile. Since it’s just text in the context window, you don’t need a retrieval layer for it. The thread didn’t test this, so treat it as a promising idea and run the same blind check on the output before you trust it.
a meta skill to distill anyone’s writing style
by u/kaisoma in PromptEngineering