# Voice Analyze > Reverse-engineer voice profiles from sample content by analyzing writing patterns. Use when user pastes writing samples and wants to extract their style. Use when this capability is needed. - Skill: `tomevault-io/voice-analyze` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/voice-analyze` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/voice-analyze/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/voice-analyze --- # 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 ```yaml 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` --- > Source: [angelarose210/ghostwriter](https://github.com/angelarose210/ghostwriter) — distributed by [TomeVault](https://tomevault.io).