voice-analyzer

name	voice-analyzer
version	1.0.0
description	Analyze representative writing samples into a portable personal voice profile and style guide. Use when establishing voice-matched editing for a new project or refreshing an outdated profile. Not for a target journal's editorial style; use $journal-voice.
user-invocable	true
allowed-tools	["Read","Write","Edit","Glob","Grep","AskUserQuestion"]

Voice Analyzer: Create a Voice Profile from Writing Samples

Extract voice patterns from writing samples and generate a comprehensive, portable style guide (VOICE.md). The output becomes infrastructure — a reference document used every time you work with AI to maintain your authentic voice instead of producing generic content.

Quick Start

Provide 3-5 writing samples where your voice feels strongest (500-2000 words each). The skill will:

1. Analyze patterns across all samples
2. Identify your distinctive voice markers
3. Generate a VOICE.md style guide
4. Create a forbidden phrases list specific to your anti-patterns
5. Provide testing prompts to validate the guide

Ideal samples: Published papers, proposals, emails you're proud of, blog posts, referee responses, teaching materials

Avoid: Heavily edited collaborative pieces, boilerplate text, anything that felt forced

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Sample Gathering Guidance

What Makes Good Samples

Include samples that:

• You wrote when feeling confident and natural
• Received feedback like "this sounds just like you"
• You'd be happy to write again
• Show your voice across different contexts (casual, professional, explanatory)
• Are at least 500 words (longer is better for pattern detection)

Avoid samples that:

• Were heavily edited by others
• Feel generic or corporate even to you
• Were written under heavy constraints
• Don't represent how you want to sound going forward

Minimum Requirements

• Minimum: 3 samples, 500+ words each
• Ideal: 5-7 samples, 1000+ words each
• Advanced: 10+ samples including different formats (email, long-form, paper sections)

More samples = more accurate pattern detection, but diminishing returns after 10.

Academic Sample Sources

If you're building an academic voice profile:

• Paper drafts — introduction and discussion sections show the most voice
• Referee responses — often reveal how you argue and handle criticism
• Proposals and abstracts — show how you frame contributions
• Teaching materials — lecture notes, assignment descriptions
• Emails to collaborators — longer substantive emails, not one-liners
• Blog posts or public writing — if you have any

Avoid for academic profiles:

• Methods sections (too formulaic to show voice)
• Literature review sections (mostly paraphrasing others)
• Co-authored sections where your voice was diluted

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Analysis Framework

When analyzing samples, examine these six dimensions:

1. Sentence Patterns

• Average sentence length (short/punchy vs. long/flowing)
• Length variation (uniform vs. high variance)
• Sentence starters (do you vary, or repeat patterns?)
• Use of fragments for emphasis
• Complex vs. simple sentence construction

2. Vocabulary Fingerprint

• Formality level (casual/conversational vs. professional/technical)
• Jargon usage (field-specific terms, insider language)
• Characteristic phrases you repeat
• Filler words ("actually", "basically", "honestly")
• Intensifiers you favor ("really", "quite", "particularly")
• Academic-specific: hedging vocabulary, contribution framing

3. Rhythm and Flow

• Typical paragraph length
• How you transition between ideas
• Use of one-sentence paragraphs for emphasis
• Section structure and pacing
• How you open and close pieces

4. Tone Markers

• Humor style (dry, self-deprecating, none)
• Level of directness
• How you handle uncertainty (hedge vs. commit)
• Personal disclosure level
• Relationship with reader (peer, teacher, mentor, collaborator)

5. Structural Habits

• How you use lists vs. prose
• Header/subheader patterns
• Use of examples and analogies
• How you introduce and conclude topics
• Formatting preferences (bold, italics, em-dashes, parentheticals)

6. Opinion Expression

• How strongly you state opinions
• How you qualify claims
• Use of "I" vs. "we" vs. "you" vs. passive
• How you handle disagreement or controversy
• Confidence level in assertions

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Output Format

Generate a VOICE.md file with this structure:

# Voice Profile: [Name]
Generated: [Date]
Based on: [X] writing samples ([total word count] words)

## Voice Summary
[2-3 sentence description of overall voice character]

## Core Voice Characteristics

### Sentence Patterns
- Average length: [X] words
- Variation: [Low/Medium/High]
- Notable patterns: [specific observations]

### Vocabulary Fingerprint
- Formality: [Casual/Conversational/Professional/Formal]
- Characteristic phrases: [list]
- Words to use freely: [list]

### Rhythm and Flow
- Paragraph style: [description]
- Transition patterns: [description]
- Pacing notes: [description]

### Tone Markers
- Primary tone: [description]
- Humor style: [description]
- Reader relationship: [description]

### Structural Habits
- List vs. prose preference: [description]
- Formatting patterns: [description]

### Opinion Expression
- Directness level: [1-10]
- Qualification style: [description]
- Authority stance: [description]

## The Forbidden List

### Never Use (These kill your voice)
- [phrase 1]
- [phrase 2]
- [etc.]

### Use Sparingly (Context-dependent)
- [phrase 1] - only when [context]
- [etc.]

### Watch for Clusters (OK alone, problematic together)
- [pattern description]

## Academic Mode Notes
[If academic samples were analyzed]
- Paper voice vs. email voice differences
- Hedging conventions to preserve
 Contribution framing patterns
 How formality shifts by audience (journal vs. collaborator vs. student)




 Read 3 recent pieces aloud
 Do they still sound like you?
 Update this guide if voice has evolved


 Gather new strong samples
 Re-run analysis
 Compare to this guide
 Update patterns that have changed



Use these to validate the guide works:


"Using my voice profile, write a 3-sentence response to [common scenario in your field]"


"Using my voice profile, write the opening 2 paragraphs for a piece about [topic you know well]"


"Using my voice profile, write about [topic outside your usual content]"

Compare outputs to your natural writing. If they feel off, update the guide.

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Analysis Process

Step 1: Initial Read

Read all samples without analyzing. Get a feel for the overall voice impression. Note your gut reaction: what makes this writing distinctive?

Step 2: Pattern Extraction

Go through each dimension in the analysis framework. Pull specific examples from the samples. Look for patterns that appear across multiple samples (not one-offs).

Step 3: Contrast Analysis

Compare to generic AI output patterns. What does this writer do that AI typically doesn't? What AI patterns are absent from these samples?

Step 4: Forbidden List Generation

Based on the contrast analysis, identify phrases and patterns that would destroy this voice. These become the "never use" list.

Step 5: Guide Assembly

Compile findings into the VOICE.md format. Include specific examples from the samples to illustrate each pattern.

Step 6: Validation Prompts

Generate 3 test prompts tailored to this person's typical writing contexts. These will be used to verify the guide works.

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Where to Save VOICE.md

• Default: .context/voice/voice.md (follows the .context/ pattern)
• Project-specific: <project>/.context/voice/voice.md (for project-specific voice)
• Multiple profiles: .context/voice/[context]-voice.md (academic, casual, teaching)

Add a pointer in the project's CLAUDE.md so it auto-loads:

## Voice Profile
See [`.context/voice/voice.md`](.context/voice/voice.md)

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Integration

• voice-editor uses VOICE.md to guide rewrites

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Success Criteria

The voice analysis is complete when:

• All provided samples have been analyzed
• Each dimension in the analysis framework has findings
• VOICE.md file is generated with all sections
• Forbidden list contains at least 10 specific items
• 3 validation prompts are tailored to the user's context
• User has been shown where to save the file
• Testing process has been explained

Quality check: The generated guide should allow someone unfamiliar with the writer to produce content that readers would recognize as authentic.

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Core Principle

You're not trying to achieve perfection on attempt one. You're building infrastructure that improves with use. The first guide will be good but not perfect — that's normal. Each piece written with this guide makes it more precise.

Voice doesn't live in first drafts. It lives in editing choices. The guide gives AI direction; your editing gives the work your actual voice.