voice-analyze

name	voice-analyze
description	Reverse-engineer voice profiles from sample content by analyzing writing patterns. Use when user pastes writing samples and wants to extract their style.
version	1.0.0
platforms	["all"]
metadata	{"openclaw":{"requires":{}}}

voice-analyze

Reverse-engineer voice profiles from sample content by analyzing writing patterns. Detects and flags AI writing tells so the resulting profile produces human-sounding output.

Triggers

Alternate expressions and non-obvious activations (primary phrases are matched automatically from the skill description):

• "voice fingerprint" -> voice profile extraction
• "what's my writing style" -> voice analysis

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. Scans for AI writing tells (see ../voice-apply/references/ai-tells.md):

   • Flags any banned vocabulary found in the sample (verbs, adjectives, nouns, adverbs from the tells list)
   • Detects banned sentence structures: "It's not X, it's Y" negation patterns, self-posed questions answered immediately, anaphora abuse, present participle trailing clauses
   • Measures em dash frequency (AI models use ~10x more than human writers)
   • Checks for uniform sentence length clustering (the "burstiness problem": AI clusters at 15-20 words, humans swing from 3-word fragments to 40-word sprawls)
   • Detects banned transitions and filler phrases (throat-clearing openers, pedagogical openers, fake-suspense transitions, hype phrases)
   • Flags formatting tells: bold-first bullet points, excessive headers, erratic bolding, emoji decoration
   • Checks for Rule of Three overuse (back-to-back tricolons are an AI fingerprint)
   • Detects uniform paragraph length (the most visually obvious tell)
   • If the sample contains AI tells, the profile's vocabulary.avoid list MUST include them and the ai_tells_report section must document what was found

4. Maps features to voice dimensions:

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

5. Generates voice profile matching the analyzed style, with AI tells filtered out

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)
- AI Tells Detected: 3 (2 banned verbs, 1 negation pattern)

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)
- AI Tells Detected: 0

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
- Flags and excludes any AI tells found in samples

Output: personal-blog-voice.yaml

Analysis Methodology

Feature Extraction

Feature	Measurement	Maps To
Sentence length	Avg words/sentence	complexity
Sentence length variance	Std deviation of word count	burstiness (human = high variance)
Contractions	Frequency per 100 words	formality (inverse)
First person ("I", "we")	Frequency	warmth
Second person ("you")	Frequency	warmth
Passive voice	Percentage of sentences	confidence (inverse)
Questions	Per paragraph	warmth, engagement
Hedging words	"might", "perhaps", "could"	confidence (inverse)
Exclamation marks	Frequency	energy
Technical terms	Domain vocabulary density	complexity
Em dash frequency	Per 1000 words	ai_tell_score (high = likely AI)
Banned AI vocabulary	Count from tells list	ai_tell_score
Paragraph length variance	Std deviation of sentence count	ai_tell_score (low variance = likely AI)

AI Tells Detection Pass

After standard feature extraction, run the AI tells scan. For each category in ../voice-apply/references/ai-tells.md:

1. Vocabulary scan: Count occurrences of every word/phrase in the banned lists (verbs, adjectives, nouns, adverbs, connectors)
2. Structure scan: Detect banned sentence patterns (negation flips, self-posed questions, anaphora, trailing participles, false ranges, hedge-stacking)
3. Punctuation scan: Count em dashes, semicolons, Oxford commas, Unicode ellipsis characters
4. Rhythm scan: Calculate sentence length standard deviation. If std dev < 4 words, flag as AI-like clustering
5. Formatting scan: Check for bold-first bullets, excessive headers, erratic bolding
6. Transition scan: Search for throat-clearing openers, pedagogical openers, fake-suspense phrases, hype phrases, banned conclusion phrases

Report findings in the output profile under ai_tells_report. Any banned vocabulary found in the sample goes into vocabulary.avoid automatically.

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 - Built from two sources:

1. Conspicuous absence of common phrases in the samples
2. Any AI writing tells found during the tells detection pass (always include these)

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:
    - "leverage"       # AI tell: banned verb
    - "delve"          # AI tell: banned verb
    - "comprehensive"  # AI tell: banned adjective
    - "landscape"      # AI tell: banned noun
    - "furthermore"    # AI tell: banned adverb
    - "patterns not found in samples"
  signature_phrases:
    - "The key point is..."
    - "This demonstrates..."
structure:
  sentence_length: medium    # avg 15-20 words
  sentence_length_variance: high  # std dev > 6 words (human-like)
  paragraph_length: medium   # avg 4-6 sentences
  paragraph_length_variance: high # varied paragraph sizes (human-like)
  sentence_variety: high     # varied structure detected
  use_lists: when-appropriate
  use_examples: frequently
  use_questions: rarely
  em_dash_usage: avoid       # always set to avoid unless user explicitly uses them naturally
perspective:
  person: third
  voice: active
  tense: present
extracted_patterns:
  opening_style: "context-first"
  closing_style: "conclusion-summary"
  transition_style: "logical-flow"
ai_tells_report:
  tells_found: 3
  em_dash_frequency: 2.1     # per 1000 words (human avg ~0.5, AI avg ~5.0)
  sentence_length_std_dev: 7.2  # words (human = 6-12, AI = 2-4)
  paragraph_length_std_dev: 2.8 # sentences (human = 2-5, AI = 0.5-1.5)
  banned_vocabulary_found:
    - "leverage (1x)"
    - "comprehensive (2x)"
  banned_structures_found:
    - "negation pattern (1x)"
  banned_transitions_found: []
  formatting_tells_found: []
  overall_human_score: 0.82  # 0 = clearly AI, 1 = clearly human

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)
• AI tells in samples: If the sample itself contains AI tells (e.g., it was partially AI-generated), the analyzer will flag them and exclude them from the profile's preferred patterns. The overall_human_score helps gauge how much of the sample is genuinely the user's voice vs AI artifacts.

References

• AI Writing Tells: ../voice-apply/references/ai-tells.md