Voice System
The Anti-Slop Checker removes AI tells and leaves you with clean, generic text. The Voice System goes further — it learns your specific patterns from samples, then rewrites AI output to sound like you instead of like nobody.
What this skill does
The free Anti-Slop Checker tells you "this phrase is AI." The Voice System tells you "this phrase is AI, and here is how YOU would say it instead, based on how you actually write." The free tool gives you clean text. This one gives you your text. Detection vs transformation — that's the difference.
It works in two modes. Voice Learning takes your samples and analyses them across four dimensions: vocabulary fingerprint (the 10-15 words you overuse, the ones you never touch, your jargon comfort, your profanity threshold), rhythm signature (sentence length and variance, paragraph habits, parenthetical style), structural patterns (how you open, transition, and close), and personality markers (humour type, confidence level, formality gradient). The output is a Voice Profile you save and paste into future chats. Voice Rewriting takes any AI text plus your profile (or fresh samples) and rebuilds it to match — not word substitution, structural transformation. Sentence rhythms rebuilt to your variance. Your vocabulary home-base words injected where natural. Your punctuation quirks applied. Then a diff report showing every change mapped to a profile rule, plus a 1-10 Voice Match Score.
It's honest about sample size. Under 200 words it tells you the input is too thin and asks for more. 500-1500 words gets you a functional profile with most dimensions captured. 1500+ across multiple content types is the target. 2000+ is ideal. It will not fabricate voice characteristics that aren't supported by samples — if you've only fed it LinkedIn posts, the profile says so and flags structural patterns as speculative.
There's a Brand Voice mode for teams: feed it 3+ samples across at least two content types, get a one-page Brand Voice Card, a Do/Don't list with real examples from the content, a voice spectrum showing where the brand sits on four axes, and a rewrite rubric specific enough that two different writers applying it produce consistent output. One deliberate principle worth flagging: when in doubt, the rewrite matches your worst habits, not your best. Polishing out the quirks makes it sound like a ghostwriter, not like you.
When this triggers
- ·You've used AI to draft and the result is clean but reads like nobody in particular wrote it
- ·You want a Voice Profile saved that you can paste into future chats for instant voice-matched output
- ·You're building team brand-voice guidelines and need rules two different people could apply consistently
- ·Someone said 'this doesn't sound like you' and you need to know which patterns are missing
- ·You write enough that you'd like a forensic readout of your own signature moves and quirks
Example
Trigger
User pastes 1,800 words of their LinkedIn posts and says 'learn my voice'.
Output
## Voice Profile In one sentence: Direct, slightly sardonic. Short sentences for emphasis. Opens with stories or specific numbers. Closes with provocative questions. Comfortable with "damn" but not "shit." Signature moves: · Opens 60% of posts with a single short sentence (under 10 words) · Uses em-dashes — frequently — for asides · Drops industry jargon in plain-language frames ("activation rate, which is just whether they came back") · Ends with one-line questions, no exclamation marks Anti-patterns (never do this): · "Let's unpack this" — never appears in samples · "Game-changer" — never appears · Three-item parallel lists — almost never Vocabulary home base: 14 words/phrases recycled across samples. Voice rules for rewriting (8 rules). Paste this profile into future chats for voice-matched output.
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- 146-line SKILL.md, ready to drop into ~/.claude/skills/
- Tested through 3 Karpathy-loop iterations (versions v1.0.0 → v1.3.0)
- Triggers automatically when relevant — no command to remember
- Lifetime updates as the skill is refined further
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