Show HN: Distill anyone's writing style into a skill A developer released a tool called mimic that distills an author's writing style into a reusable agent skill — a system prompt built from stylometry measurements plus extracted rhetorical moves — rather than fine-tuning a model. The tool's stylometry script measures function-word rates, punctuation, sentence rhythm and lexical richness from 2–8k words of a corpus, while the agent reads 15–25 passages to extract performable moves; in a worked example, a fresh model given the skill as a system prompt dropped mean sentence length from 19 to 8 words and cut cosine distance to the author from 1.11 to 0.85. On a de-contaminated benchmark of 6 authors × 4 tasks, surface stylometry could not reliably distinguish the skill from a plain "write as X" prompt (Elo 1047 versus 1066, with no-style at 887), but a blind LLM judge preferred the skill's output on 24 of 24 items. Distill a writing voice into a reusable agent skill — a system prompt that makes an LLM write like a specific author or persona on any topic. No fine-tuning, no gradients, no model of its own. Just measurement and a prompt. Point your agent at ~2–8k words of someone's writing. It fingerprints the voice, writes the skill, checks its own work against the corpus, and — if you want — installs the skill into your agent for future sessions. It asks first. A voice is not a word list. It's a distribution over structure : how long sentences run, how often they repeat, which function words and punctuation recur, and — the part most imitations miss — the rhetorical moves the writer keeps performing. Authorship attribution identifies writers almost entirely from these structural statistics, not from topic words. So if you can measure them and write explicit instructions to reproduce them, a prompt can carry a voice. Mimic fingerprints in two layers: - L1 — the distribution the writer can't help. Function-word rates, punctuation, sentence rhythm, lexical richness. Measured by stylometry.py . This gets you the right shape . - L2 — the moves the writer performs. The agent reads the corpus closely and extracts performable rhetorical moves — "a move is something the writer does , not a word they reach for": digress-and-snap-back, embedded mini-anecdote, vague-authority sourcing, the escalation ladder, ring composition. These are written into the skill as "deploy this" actions, and the model is told to perform 2–3 per passage. This is what separates voice from caricature. Everything is driven by the skill. There's no distillation script — the agent does the work by following skills/author-voice/SKILL.md https://github.com/xatuke/mimic/blob/main/skills/author-voice/SKILL.md : 1. Corpus. Collect ~2–8k words of the author's own writing → corpus.txt . 2. Measure L1 . python3 skills/author-voice/scripts/stylometry.py corpus.txt — read off rhythm, richness, function words. 3. Analyze L2 . The agent reads 15–25 passages closely and extracts the moves, with real examples quoted from the corpus. 4. Write the skill. Grounded in the numbers and the moves, following the template. 5. Validate. Generate a few neutral prompts with the draft as system prompt, save to SAMPLE.txt , and re-run stylometry.py corpus.txt SAMPLE.txt — it prints exactly what to push. Iterate two or three times. 6. Offer to install — asking first. On an explicit yes, the skill is copied to ~/.claude/skills/