| name | voice-analyzer |
| description | "Use when you need to analyze writing samples to create a portable voice profile." |
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:
- Analyze patterns across all samples
- Identify your distinctive voice markers
- Generate a VOICE.md style guide
- Create a forbidden phrases list specific to your anti-patterns
- 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
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
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
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.
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.
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)
Integration
/voice-editor uses VOICE.md to guide rewrites
Success Criteria
The voice analysis is complete when:
Quality check: The generated guide should allow someone unfamiliar with the writer to produce content that readers would recognize as authentic.
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.
