name: style-fingerprint
description: "Study 3-5 documents the user actually shipped and distil a compact style card — so every skill writes in their voice, not the model's. Use when asked to learn my writing style, make outputs sound like me, build a voice profile, or when a user complains AI drafts don't sound like them. Produces a style card (rhythm, register, structure habits, pet phrases, banned moves) saved to the Brain where every other skill reads it."
Style Fingerprint Skill
The #1 complaint about AI drafts is "it doesn't sound like me." This skill fixes it at the root: it studies writing the user has actually shipped, extracts the mechanical, imitable features of their voice, and writes a style card into the Brain — after which every brain-aware skill drafts in their register instead of the model's default.
What This Skill Produces
- A style card (~200-300 words, structured) capturing the user's voice as reproducible rules, not adjectives
- Before/after proof: one paragraph rewritten from model-default into the fingerprinted voice, so the user can verify the card works
- The card saved to
brain/knowledge/style.md (with the user's approval), plus a one-line pointer in context.md voice section
Required Inputs
Ask for (if not already provided):
- 3-5 samples the user wrote and shipped — real emails, updates, PRD sections, posts. More samples of the same genre beat variety. Politely reject samples the user merely approved but didn't write — an edited-by-committee doc fingerprints the committee.
- The target register if samples span several (exec formal vs team casual) — or fingerprint each as a named variant
Extraction Method
Analyse mechanics, not impressions. For each dimension, extract a rule an imitator could follow:
| Dimension | What to extract |
|---|
| Sentence rhythm | Median sentence length; short-sentence frequency; do they open paragraphs long or punchy? |
| Register & warmth | Contractions? First person singular or plural? Directness of asks ("please could we" vs "let's") |
| Structure habits | Bullets vs prose ratio; headers or none; do they front-load the conclusion (BLUF) or build to it? |
| Signature moves | Recurring phrases, connectors ("net-net", "the short version:"), characteristic openings/closings |
| Emphasis style | Bold? Italics? Caps? Em-dashes vs parentheses? Emoji policy (which ones, where, never)? |
| Numbers | Precision habits ("~40%" vs "42.3%"), units, how they hedge estimates |
| Banned moves | What never appears in their writing (corporate filler, exclamation marks, "I hope this finds you well", passive voice…) — the banned list does more work than the rest combined |
Then verify: rewrite one neutral paragraph in the extracted voice and check it against the samples. If it reads generic, the card is adjectives, not rules — extract harder.
Output Format
Style card: [name / register variant] — fingerprinted [date] from [n] samples
Rhythm: [rules]
Register: [rules]
Structure: [rules]
Signature moves: [phrases/patterns, quoted from samples]
Emphasis & numbers: [rules]
Never: [the banned list]
Calibration line: (one sentence from the samples that is peak them — future skills imitate toward this)
Proof — same paragraph, twice:
[model-default version]
[fingerprinted version]
📥 Save to Brain: propose writing this card to brain/knowledge/style.md and adding a voice: see knowledge/style.md pointer in context.md. Show the write, get a yes, then use ../professional-brain/scripts/brain_write.py … --commit. Brain-aware skills read context.md voice on every run — the fingerprint takes effect immediately and everywhere.
Quality Checks
Anti-Patterns
Example Trigger Phrases
- "Learn my writing style."
- "Make outputs sound like me."
- "Build a voice profile."