voice-calibration
Skill5 starsChanged 5 months agoMITView on GitHub
Interactive voice profiling — asks writing prompts in terminal, collects live responses, and distills a reusable voice profile for content generation. Use when calibrating tone for social posts, newsletters, or any written output.
2 other projects use a version of this.
Adopted by
Most recently taken up by
- raulpetruta/voice-calibration-plugintheir own version, first seen Sep 29, 2026
- timscheuerai/gtm-architecture-templatetheir own version, first seen Sep 14, 2026
Other versions2 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 2
- raulpetruta/voice-calibration-plugin★ 13same name
- timscheuerai/gtm-architecture-template★ 2same 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 repository28 other files it gives its agents.
- AGENTS.mdAGENTS.md
- analyze-variationSkill
- arxiv-searchSkill
- blakean-sceneSkill
- codex-reviewSkill
- create-explanationSkill
- critic-loopSkill
- deepen-taskSkill
- deliver-changeSkill
- design-dnaSkill
- gloss-drift-auditSkill
- image-to-sceneSkill
- marketing-claritySkill
- market-pulseSkill
- media-kitSkill
- motion-craftSkill
- nextjs-i18n-seoSkill
- oss-licence-auditSkill
- plan-review-loopSkill
- shaughnessy-ichingSkill
- swapping-pptx-templatesSkill
- ui-ux-architectSkill
- unslopSkill
- upgrade-siteSkill
- vercel-build-speedSkill
- verify-changeSkill
- verse-to-promptSkill
- video-to-shortsSkill
The file
We split this skill into the rules it gives an agent
10 rules. About 0 are common, 9 are distinctive, and 1 is very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈9
Distinctive · estimate
This author's own choices. Worth a look.
1
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 31
Ask these one at a time using AskUserQuestion.
- Very specific: line 53
If --domain was passed, use that topic.
- Distinctive: line 79
After collecting all 8 samples, analyze them silently.
- Distinctive: line 90
Use of contrast / juxtaposition
- Distinctive: line 91
Use of lists or parallel structure
- Distinctive: line 92
Use of direct address ("you")
- Distinctive: line 167
Ask: "Do these sample sentences sound like you?
- Distinctive: line 178
Read the voice profile
- Distinctive: line 180
Check output against "Anti-Patterns" before delivering
- Distinctive: line 181
If in doubt, re-read the user's original samples for calibration
≈ “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 Calibration
Build a voice profile from live writing samples typed in the terminal. The profile captures sentence rhythm, rhetorical habits, vocabulary tendencies, and tonal signatures — then saves it as a reusable reference for future content generation.
Why Live Samples
AI-assisted writing blends the author's voice with the model's defaults. To capture authentic voice, every sample must be typed fresh by the user in the terminal with no AI drafting or editing. Quantity matters less than authenticity — 6-8 raw responses are enough.
Workflow
Round 1 (General) → 4 prompts — capture base voice
Round 2 (Domain) → 4 prompts — capture subject-matter tone
Analysis → extract patterns
Voice profile → save to fileRound 1: General Voice (4 prompts)
Ask these one at a time using AskUserQuestion. Wait for each response before asking the next. Do NOT paraphrase or suggest answers.
Prompt 1 — Opinion
You have 3-4 sentences. What's something most people in your field get wrong?
Prompt 2 — Explanation
Explain something you understand deeply to someone who knows nothing about it. 3-5 sentences.
Prompt 3 — Reaction
Think of something you read recently that made you stop and think. What was it, and what was your reaction? Write it as if you're telling a friend.
Prompt 4 — Contrast
Describe two things that most people think are similar but you think are fundamentally different. 2-4 sentences.
Round 2: Domain Voice (4 prompts)
If --domain was passed, use that topic. Otherwise ask:
What domain will this voice profile be used for? (e.g., I-Ching commentary, product marketing, technical writing)
Then ask these one at a time, tailored to the stated domain:
Prompt 5 — Hook
Write the opening 2-3 sentences of a post about [domain topic]. Assume the reader doesn't care yet — make them care.
Prompt 6 — Corrective
What's a common misconception in [domain]? Correct it in 3-4 sentences the way you'd actually say it.
Prompt 7 — Compression
Take something complex in [domain] and compress it to 1-2 sentences. Make it land.
Prompt 8 — Closing
Write the last 2-3 sentences of a post. Leave the reader with something that stays.
Analysis
After collecting all 8 samples, analyze them silently. Do NOT show intermediate analysis to the user. Extract:
Sentence Mechanics
- Average sentence length (words)
- Sentence length variance (does the writer alternate short/long or stay uniform?)
- Punctuation habits (em dashes, semicolons, colons, ellipses, periods-as-fragments)
- Paragraph rhythm (single punchy lines vs. flowing multi-sentence blocks)
Rhetorical Patterns
- Opening moves (declarative? interrogative? scene-setting? provocative claim?)
- Closing moves (callback? reframe? open question? decisive statement?)
- Use of contrast / juxtaposition
- Use of lists or parallel structure
- Use of direct address ("you")
- Use of imperatives
- Second-person vs. third-person vs. impersonal framing
Vocabulary & Register
- Latinate vs. Anglo-Saxon word preference
- Technical jargon frequency (high/low, when deployed)
- Concrete vs. abstract imagery
- Metaphor density (heavy/light/none)
- Register (formal, conversational, elevated-casual, academic)
Tonal Signature
- Confidence level (assertive, hedged, qualified)
- Humor presence and type (dry, none, self-deprecating, ironic)
- Emotional distance (intimate, arm's-length, detached)
- Attitude toward the reader (peer, teacher, co-conspirator, narrator)
Voice Profile Output
Generate a voice profile document with this structure:
# Voice Profile: [User Name or Handle]
Generated: [date]
Domain: [domain]
Samples: [count]
## Summary
[2-3 sentence distillation of the voice — what makes it recognizable]
## Sentence Rhythm
[Findings from sentence mechanics — concrete observations with examples from samples]
## Rhetorical Habits
[Patterns with direct quotes from samples as evidence]
## Vocabulary & Register
[Word-level observations]
## Tonal Signature
[The overall feel — how this writer comes across]
## Rules for Matching This Voice
1. [Concrete, actionable rule]
2. [Concrete, actionable rule]
3. [Concrete, actionable rule]
4. [Concrete, actionable rule]
5. [Concrete, actionable rule]
6. [Concrete, actionable rule]
## Anti-Patterns (Do NOT)
1. [Thing to avoid that would break the voice]
2. [Thing to avoid that would break the voice]
3. [Thing to avoid that would break the voice]
## Sample Sentences in This Voice
[3 original sentences written BY THE MODEL that demonstrate the captured voice — the user should read these and confirm they sound right]Saving the Profile
- Show the voice profile to the user
- Ask: "Do these sample sentences sound like you? Anything to adjust?"
- Incorporate feedback if given
- Save the final profile to
{skill-base-dir}/profiles/[handle-or-name].md - Also offer to save a copy to the user's project (e.g.,
docs/voice-profile.md)
Using the Profile Later
Other skills and prompts can reference the saved profile. When generating content in the user's voice:
- Read the voice profile
- Apply the "Rules for Matching This Voice" as hard constraints
- Check output against "Anti-Patterns" before delivering
- If in doubt, re-read the user's original samples for calibration
The profile is a living document — the user can re-run this skill anytime to update it with fresh samples.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
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
Files you open will collect here, so you can find them again.
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.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

