voice-analyze

Reverse-engineer voice profiles from sample content by analyzing writing patterns.

Triggers

• "analyze this writing style"
• "extract voice from..."
• "what voice is this?"
• "create profile from this sample"
• "match this writing style"

Behavior

When triggered, this skill:

1. Analyzes text samples for:

   • Sentence structure and length patterns
   • Vocabulary sophistication and domain
   • Tone markers (formality, confidence, warmth)
   • Structural patterns (lists, examples, questions)
   • Perspective and voice choices

2. Extracts measurable features:

   • Average sentence length
   • Vocabulary complexity (syllables, word length)
   • Contraction usage
   • Personal pronoun frequency
   • Question density
   • List/bullet usage

3. Maps features to voice dimensions:

   • Statistical analysis → tone scale values (0-1)
   • Pattern detection → structure preferences
   • Vocabulary extraction → prefer/avoid lists

4. Generates voice profile matching the analyzed style

Usage Examples

Analyze Existing Documentation

User: "Analyze this writing style" + [paste technical docs]

Analysis:
- Formality: 0.7 (no contractions, structured sentences)
- Confidence: 0.85 (direct statements, few hedges)
- Warmth: 0.25 (impersonal, third-person)
- Complexity: 0.8 (technical vocabulary, long sentences)

Output: analyzed-technical-docs.yaml

Match Brand Voice

User: "Extract voice from our marketing copy" + [paste samples]

Analysis:
- Formality: 0.3 (conversational, contractions)
- Confidence: 0.7 (benefit claims, but some hedging)
- Warmth: 0.85 (second person, friendly tone)
- Energy: 0.8 (exclamation points, action verbs)

Output: brand-marketing-voice.yaml

Capture Personal Style

User: "Create profile from my blog posts" + [paste samples]

Analysis:
- Identifies personal writing quirks
- Extracts signature phrases
- Maps to voice dimensions

Output: personal-blog-voice.yaml

Analysis Methodology

Feature Extraction

Dimension Calibration

Formality (0-1):

• 0.0-0.3: Contractions frequent, casual language, fragments okay
• 0.4-0.6: Mixed style, professional but accessible
• 0.7-1.0: No contractions, complete sentences, formal structure

Confidence (0-1):

• 0.0-0.3: Many hedges ("might", "perhaps"), questions, qualifiers
• 0.4-0.6: Balanced certainty, occasional hedges
• 0.7-1.0: Direct statements, conclusions first, few qualifiers

Warmth (0-1):

• 0.0-0.3: Third person, passive voice, clinical tone
• 0.4-0.6: Professional but personable
• 0.7-1.0: Second person, inclusive language, empathetic

Energy (0-1):

• 0.0-0.3: Calm, measured, understated
• 0.4-0.6: Balanced engagement
• 0.7-1.0: Exclamation marks, action verbs, dynamic phrasing

Complexity (0-1):

• 0.0-0.3: Short sentences, simple vocabulary, accessible
• 0.4-0.6: Moderate complexity, clear but nuanced
• 0.7-1.0: Long sentences, technical vocabulary, layered ideas

Vocabulary Extraction

Signature phrases - Identified by:

• Repeated patterns across samples
• Distinctive constructions
• Opening/closing patterns

Domain vocabulary - Extracted by:

• Technical term frequency
• Specialized jargon
• Industry-specific language

Avoid patterns - Detected by:

• Conspicuous absence of common phrases
• Consistent avoidance of certain constructions

Output Format

name: analyzed-sample-voice
version: 1.0.0
description: Voice profile extracted from sample content
analysis_source:
  sample_size: 1500  # words analyzed
  sample_count: 3    # number of samples
  confidence: 0.85   # analysis confidence score
tone:
  formality: 0.65
  confidence: 0.8
  warmth: 0.4
  energy: 0.5
  complexity: 0.7
vocabulary:
  prefer:
    - "extracted signature phrase 1"
    - "detected domain terminology"
  avoid:
    - "patterns not found in samples"
  signature_phrases:
    - "The key point is..."
    - "This demonstrates..."
structure:
  sentence_length: medium    # avg 15-20 words
  paragraph_length: medium   # avg 4-6 sentences
  sentence_variety: high     # varied structure detected
  use_lists: when-appropriate
  use_examples: frequently
  use_questions: rarely
perspective:
  person: third
  voice: active
  tense: present
extracted_patterns:
  opening_style: "context-first"
  closing_style: "conclusion-summary"
  transition_style: "logical-flow"

CLI Usage

# Analyze from file
python voice_analyzer.py --input sample.txt

# Analyze from multiple files
python voice_analyzer.py --input "sample1.txt,sample2.txt,sample3.txt"

# Analyze from stdin (pipe content)
cat sample.txt | python voice_analyzer.py --stdin

# Specify output name
python voice_analyzer.py --input sample.txt --name my-extracted-voice

# Output to specific directory
python voice_analyzer.py --input sample.txt --output .aiwg/voices/

# JSON output for inspection
python voice_analyzer.py --input sample.txt --json

Integration

• Output: Creates profiles usable by voice-apply
• Chain: voice-analyze → voice-create (to refine) → voice-apply
• Chain: voice-analyze + voice-analyze → voice-blend (combine styles)

Accuracy Considerations

• Minimum sample: 500+ words for reliable analysis
• Multiple samples: 3+ samples improve accuracy
• Consistent genre: Mixing genres reduces accuracy
• Confidence score: Output includes analysis confidence (0-1)

References

• Schema: ../../../schemas/voice-profile.schema.json
• Dimensions guide: ../voice-apply/references/voice-dimensions.md
• Generator: ../voice-create/scripts/voice_generator.py