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Voice Extractor

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BrianRWagner
voice-extractor

Extract and document someone's authentic writing voice from samples. Use when someone needs a "voice guide," wants to capture their writing DNA, or needs to train AI to write in their style. Also useful for ghostwriting, brand voice documentation, or onboarding writers.

Overview

PublisherBrianRWagner
Repositoryai-marketing-claude-code-skills
Skill namevoice-extractor
Stars
449
Forks
103
Bundled files
2
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by BrianRWagner on GitHub. Read the source before you install it.

Installation

Install the Voice Extractor AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/BrianRWagner/ai-marketing-claude-code-skills/tree/main/voice-extractor
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/BrianRWagner/ai-marketing-claude-code-skills.git /tmp/ai-marketing-claude-code-skills
mkdir -p .claude/skills
cp -r /tmp/ai-marketing-claude-code-skills/voice-extractor .claude/skills/voice-extractor
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Voice Extractor in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Voice Extractor on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Voice Extractor is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Voice Extractor

AI-generated content all sounds the same. The fix isn't better prompts — it's teaching the AI how you actually communicate.

This skill extracts your communication DNA from writing samples and produces a Voice Guide: documented, tested, and ready to use.


Mode

Detect from context or ask: "Quick voice snapshot, full Voice Guide, or full guide with examples?"

ModeWhat you getBest for
quickTop 5 voice characteristics + 3 do/don't rulesFast style reference, single piece
standardFull Voice Guide: tone, vocabulary, rhythm, structureAI training, ghostwriting, brand documentation
deepFull Voice Guide + 10 sample rewrites + writing rules checklist + AI training examplesOnboarding writers, building a brand voice system

Default: standard — use quick if they just need a fast reference. Use deep if they're onboarding a ghostwriter or building a content team.


Context Loading Gates

Before extracting, collect:

  • Writing samples — minimum 3 samples OR 500 total words (see priority list below)
  • Purpose of voice guide — AI training? Ghostwriter onboarding? Team alignment?
  • Confidence zones — Any topics where they want to sound more/less authoritative?
  • Known anti-patterns — Any words or phrases they already know they want to avoid?

Sample priority (most → least authentic):

  1. Casual Slack or email (raw, unedited voice)
  2. Podcast or call transcript
  3. LinkedIn posts or articles
  4. Website copy (often edited, less authentic)

Minimum sample gate: If samples total under 500 words, stop:

"These samples are too short to extract reliable patterns. Please add 2-3 more — emails, Slack messages, or transcripts work best. The messier and more casual, the better."

Do not attempt full extraction from under 500 words. Offer quick mode instead.


Phase 1: Sample Quality Assessment

Before extracting, reason through:

  1. Sample authenticity: Are these samples from edited/polished contexts (website, press) or raw contexts (Slack, email)? More polish = less authentic voice.
  2. Sample variety: Do the samples cover different contexts (professional, casual, educational)? Single-context samples produce single-dimension voice guides.
  3. Exclusion check: Identify and flag patterns that are NOT the authentic voice:
    • Platform formatting tics (LinkedIn line breaks, Twitter brevity forcing)
    • Typos and autocorrect errors
    • Phrases borrowed from others (quotes, retweets)
    • Unusually formal writing (legal docs, press releases)
  4. Sample size adequacy: Is there enough material for full mode, or should I use quick mode?

Output a sample assessment:

"I have [X samples / Y words] to work with. Quality: [high/medium — why]. I'll use [full/quick] mode. Excluding: [any patterns and why]."


Phase 2: Core Energy Extraction

Identify the fundamental communication mode:

Role:

  • Teacher (breaks things down systematically)
  • Challenger (pushes back on assumptions)
  • Cheerleader (builds confidence and momentum)
  • Straight-shooter (cuts through BS efficiently)

Default energy:

  • Calm authority ("Here's what works.")
  • High enthusiasm ("This is exciting — let me show you.")
  • Understated confidence ("I've seen this a hundred times.")

Recurring themes: What topics appear unprompted across samples? These are the things they actually care about.


Phase 3: Phrase Extraction (Systematic)

Scan all samples and extract:

Transition phrases (how they shift topics):

  • Quote exact examples from samples
  • Pattern: "Here's the thing...", "What I've learned...", "Let me put it differently..."

Emphasis phrases (how they land a point):

  • Quote exact examples
  • Pattern: "The reality is...", "This is the part people miss...", "Here's the actual problem..."

Closers (how they wrap up):

  • Quote exact examples
  • Pattern: "That's the move.", "Start there.", "You've got this."

Phase 4: Confidence Zone Mapping

ZoneDescriptionLanguage Markers
Full authorityTopics they're an expert inNo hedging, definitive statements, "here's what works"
Earned perspectiveTopics with experience but not mastery"In my experience...", "What I've found..."
Active explorationTopics they're learning now"I'm testing this...", "What I'm seeing..."

Map their stated expertise areas to each zone. This calibration is what makes the voice feel real vs. one-dimensional.


Phase 5: Anti-Pattern Documentation

Extract what they'd NEVER say:

  • Words that would feel wrong in their voice
  • Phrases that make them cringe
  • Tones they naturally avoid
  • Industry jargon they hate

Source these from sample evidence where possible: "You never used [word] across [X samples] — it doesn't fit your voice."


Phase 6: Validation Test (REQUIRED)

After extracting the full profile, generate 2 test sentences on the same topic:

Version A (using the extracted voice profile):

"[Sample sentence in their voice]"

Version B (wrong voice — contrasting example):

"[Same content, different voice — shows what to avoid]"

Ask the user: "Does Version A actually sound like you when you're not overthinking it? What feels off?"

This validation catches extraction errors before the guide is put into production.


Quick Mode (--quick)

When samples are thin (300–500 words) or time is short:

  1. Read 3 samples fast
  2. Pull 10 signature phrases
  3. Note 3 things they'd never say
  4. Write 1 sentence describing their energy

Output: Minimum viable voice guide.

Difference from full mode:

  • Quick: ~10 phrases, 3 anti-patterns, 1-sentence energy descriptor
  • Full: Complete profile with confidence calibration, validated test sentences, and source-cited examples

Phase 7: Self-Critique Pass (REQUIRED)

After generating the Voice Guide:

  • Are the extracted phrases actually from the samples, or am I inferring them?
  • Does the anti-pattern list include specific words/phrases, or just vague categories?
  • Do the validation test sentences demonstrate a real difference between in-voice and out-of-voice?
  • Is the confidence zone mapping specific to named topics, or just generic?
  • Would a ghostwriter be able to use this guide without asking follow-up questions?

Flag any issues: "The anti-pattern section only has 2 entries — not enough for a usable guide. I need more samples or direct input from the user."


Output Structure

markdown
## Voice Guide: [Name] — [Date]

### Sample Assessment
- Samples: [count, types]
- Total words: [count]
- Quality: [high/medium — reason]
- Mode: [quick/full]
- Excluded: [patterns excluded + why]

---

### Core Energy
- Role: [teacher/challenger/cheerleader/straight-shooter]
- Default energy: [description]
- Recurring themes: [list]

### Signature Phrases
**Transitions:**
- "[Phrase]" (source: [email/post])
- "[Phrase]"

**Emphasis:**
- "[Phrase]" (source: [email/post])

**Closers:**
- "[Phrase]"

### Confidence Calibration
**Full authority (no hedging):**
Topics: [list]
Sounds like: "[example sentence]"

**Earned perspective:**
Topics: [list]
Sounds like: "[example sentence]"

**Active exploration:**
Topics: [list]
Sounds like: "[example sentence]"

### Anti-Patterns (Never Use)
- [Word/phrase] — why: [evidence from samples]
- [Word/phrase] — why: [evidence]

### Validation Test
**This sounds like you:**
"[Version A]"

**This doesn't:**
"[Version B — contrast]"

### Self-Critique Notes
[Any gaps, things to validate with user]

### Usage Instructions
- For AI: Paste this guide into your system prompt
- For ghostwriter: Share on day 1 — cuts revision cycles in half
- For team: This is the benchmark for "on brand"

Skill by Brian Wagner | AI Marketing Architect | brianrwagner.com

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Voice Extractor AI skill do?

Extract and document someone's authentic writing voice from samples. Use when someone needs a "voice guide," wants to capture their writing DNA, or needs to train AI to write in their style. Also useful for ghostwriting, brand voice documentation, or onboarding writers.

Why use Voice Extractor on TypingMind?

Because you install it once and use it with any model. Voice Extractor is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Voice Extractor in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/BrianRWagner/ai-marketing-claude-code-skills/tree/main/voice-extractor. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Voice Extractor?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Voice Extractor?

As many as you like. As long as a model supports skills, you can use Voice Extractor with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Voice Extractor AI skill free?

It is published on GitHub by BrianRWagner. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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