voice-matching
Skill19 starsChanged 29 days agoMITView on GitHub
Use when writing in a creator's voice, analyzing a creator's style, or maintaining voice consistency across content.
1 other project uses a version of this.
Adopted by
Most recently taken up by
- gothicpixie/opendubstagetheir own version, first seen Oct 17, 2026
Other versions1 project uses a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 1
- gothicpixie/opendubstage★ 1same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository23 other files it gives its agents.
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- audio-acquisitionSkill
- brand-kitSkill
- content-autopsySkill
- edit-handoffSkill
- editor-beat-syncSkill
- editor-camera-directingSkill
- editor-captionsSkill
- editor-motion-graphicsSkill
- editor-plan-and-checkSkill
- editor-sfx-sparseSkill
- editor-soundtrackSkill
- editor-versionsSkill
- hook-anatomySkill
- media-acquisitionSkill
- platform-fluencySkill
- read-the-roomSkill
- repurpose-engineSkill
- studio-setupSkill
- subject-compositingSkill
- trend-radarSkill
- video-formatsSkill
- video-productionSkill
The file
We split this skill into the rules it gives an agent
3 rules. About 0 are common, 3 are distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈3
Distinctive · estimate
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 12
When given examples of a creator's content, extract these dimensions:
- Distinctive: line 30
Always prefer analyzing actual content over self-descriptions.
- Distinctive: line 32
When analyzing examples, quote specific phrases that capture the voice.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Voice Matching
Voice is the consistent identity — vocabulary, rhythm, worldview, humor type. Tone shifts with context (celebratory, serious, casual). You can write in someone's voice while varying tone. Get the voice right first.
Voice Decomposition
When given examples of a creator's content, extract these dimensions:
Vocabulary — what words do they reach for? What would they never say? How formal or casual? Do they use slang, and which kind? Do they curse? Do they use filler words deliberately?
Sentence rhythm — short and punchy? Long and flowing? Fragments as emphasis? How do they use line breaks? Do they build momentum or stay staccato?
Opening patterns — how do they start? Direct statement? Question? Story drop? Address the audience? Jump straight into the point?
Humor style — dry/deadpan, self-deprecating, observational, absurdist, or none? How often does humor appear? Is it the main mode or occasional seasoning?
CTA patterns — how do they ask for engagement? "Follow for more" or subtle? Do they even do CTAs? How do they end posts?
Emotional register — enthusiastic, chill, intense, vulnerable, authoritative, conspiratorial? What's their default energy?
Examples Over Descriptions
"I'm funny and relatable" tells you almost nothing. Five example captions tell you everything.
Always prefer analyzing actual content over self-descriptions. If you only have a self-description to work from, flag that the voice match will be approximate and ask for examples if possible.
When analyzing examples, quote specific phrases that capture the voice. "Notice how they always start with a one-word fragment" is more useful than "they have a punchy style."
Authenticity Markers
The small patterns that make content feel like a specific person, not just a generic voice:
- Characteristic phrases they return to
- Specific emoji they always use (or never use)
- How they address their audience (name for the community? "you guys"? never address directly?)
- Topics they always come back to, even tangentially
- Structural tics — always end with a question? Always use numbered lists? Always include a callback?
These markers are what separate "sounds like a person" from "sounds like THAT person."
Anti-patterns
Generic influencer voice — "Hey guys! So excited to share..." This is nobody's real voice. It's a template. If the content you're generating could be attributed to any creator, the voice matching has failed.
Corporate casual — reads like it was approved by a marketing team. Technically warm but substantively empty. "We're thrilled to announce..." energy.
Over-optimization — stripping personality to maximize engagement metrics. The quirks and rough edges ARE the voice. Smoothing them out makes content forgettable.
Voice drift — starting in the creator's voice but gradually reverting to default AI writing patterns. The longer the piece, the more this happens. Re-anchor to the original examples regularly. If you notice yourself drifting, stop and re-read the examples before continuing.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
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People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
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Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
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