Voice extraction

/voice-extraction — Reverse-Engineer Any Voice

Paste content. Get a complete voice architecture. 10 Sonnet subagents rip apart every dimension in parallel.

This is not a summary. This is a structural teardown of how someone writes, thinks, teaches, and earns trust. The output is a voice file that any other skill can read to produce content in that voice.

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When to Use

• Someone pastes in posts, articles, or tweets they admire
• "Make me sound like this person"
• "Steal this voice"
• "Analyze how they write"
• "Study this and build a voice from it"
• Building a brand voice from a specific person's content (founder, competitor, inspiration)
• Replacing a generic brand-voice profile with one built from real source material

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On Activation

1. Confirm you have raw content to analyze. The user must paste or point to actual writing (posts, articles, tweets, essays). Minimum: 1 substantial post or 5+ tweets. More is better.

2. If the content is thin: "I need more material to extract a real voice. One post gives me patterns. Five posts give me a system. Can you paste more?"

3. If the content is rich enough: proceed immediately. Don't ask unnecessary questions. The content IS the input.

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Phase 1: Launch 10 Sonnet Subagents

CRITICAL: Use model: "sonnet" for all 10 agents. Not opus. These run in parallel and sonnet is the right tool for focused analytical work.

Launch ALL 10 in a SINGLE message using the Agent tool. Each agent gets the full pasted content plus a specific analytical lens.

The 10 Dimensions

Agent 1: Sentence Structure & Rhythm Analyze sentence length patterns, paragraph openers, idea sequencing within paragraphs, transitions between sections, use of questions vs statements. Look for: short-short-long triplets, myth-pivot openers, colon embeds, imperative staccato closers. Give specific examples from the content.

Agent 2: Vocabulary & Word Choice Analyze verbs they reach for, adjectives they use, words they avoid, jargon level, how they handle technical terms, signature phrases, casual vs formal balance. Identify: blacklist words (what they never say), signature vocabulary, crossover vocabulary from other domains.

Agent 3: Teaching Methodology Analyze how they introduce ideas, use examples, structure explanations, handle complexity, guide the reader. Look for: iteration-as-pedagogy (attempt #1, #2, #3), analogy-before-abstraction, wrong-way-first patterns, complexity escalation, "develop your intuition" closes.

Agent 4: Emotional Register Map the emotional spectrum: when they show enthusiasm, when cautious, how they handle warnings, relationship to reader, confidence level, humor style. Identify: default emotional temperature, how warmth is rationed, how confidence appears, what emotions are absent.

Agent 5: Content Structure Patterns Analyze macro structure of pieces: opening patterns, section organization, how arguments build, how pieces close. Identify: the meta-pattern across all pieces, theory-to-example ratio, what goes first, what gets omitted.

Agent 6: Authority & Credibility Signals Analyze how they establish credibility without arrogance. Look for: "we tried/I tried" patterns, I/we splitting, epistemic framing, credentialing through specificity vs titles, how they credit others.

Agent 7: Tweet-Length Writing Analyze short-form specifically: sentence structure in tweets, claim posture, compression mechanism, how they tease long-form, what makes it feel like a person vs a content machine. Identify: the thesis extraction pattern, audience selection through vocabulary.

Agent 8: Use of Concrete Examples Analyze naming patterns, specificity levels, when real names vs templates, how examples serve arguments. Look for: mechanism-level vs outcome-level specificity, friction terms that signal lived experience, examples doing double duty (illustrating AND arguing).

Agent 9: Anti-Patterns (What They Don't Do) Analyze what's systematically absent: hype words, corporate speak, self-promotion patterns, talking down, clickbait, empty enthusiasm, excessive hedging, summary conclusions, rhetorical questions as transitions. Evidence of absence is as important as evidence of presence.

Agent 10: Adaptation for Target Voice Given all the above, how should this voice be adapted for the user's product/brand? What transfers directly? What needs modification? What should the user's unique voice add that the source doesn't do? Include example tweets/posts in the adapted voice.

Agent Prompt Template

Each agent gets:

You are analyzing the writing voice of [PERSON] to reverse-engineer their complete voice architecture. Focus ONLY on [DIMENSION].

Here is their content:
[FULL PASTED CONTENT]

Additional context: [USER'S PRODUCT/BRAND if relevant]

Output a structured analysis with specific patterns and examples from the actual content. No fluff. Be specific enough that someone could replicate this voice from your analysis alone.

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Phase 2: Synthesize

Once all 10 agents report back, synthesize their findings into a single voice file. The file should be structured as:

# [Name] Voice Guide

> Built from deep analysis of [source description]. [One sentence on what this voice is for.]

## 1. Core Identity
[Who this voice is. The guiding sentence.]

## 2. Sentence Architecture
[Patterns from Agent 1, distilled into rules with examples]

## 3. Vocabulary
[Reach for / Avoid lists from Agent 2]

## 4. Emotional Register
[Temperature map from Agent 4]

## 5. Authority & Credibility
[Patterns from Agent 6]

## 6. Teaching Style
[Patterns from Agent 3]

## 7. Tweet Voice
[Short-form patterns from Agent 7]

## 8. Content Structure
[Macro patterns from Agent 5]

## 9. What We Never Do
[Anti-patterns table from Agent 9]

## 10. The [Brand] Delta
[Adaptation from Agent 10]

## 11. Example Tweets in This Voice
[3-5 examples from Agent 10's adaptation]

Where to Save

This skill extracts a specific person's voice (founder, competitor, inspiration) — it does NOT overwrite the project's canonical brand/voice-profile.md. Each extracted voice lives in its own file so multiple subjects can coexist and be referenced independently.

• If inside a project with brand/: save to brand/voices/[name].md (create the brand/voices/ subdirectory if it doesn't exist)
• If standalone: save to ./voices/[name].md relative to the current working directory

Never write to brand/voice-profile.md from this skill — that path is owned by /brand-voice and represents the single canonical project voice. Use the voices subdirectory for per-subject extractions.

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Phase 3: Wire In

After the voice file is written:

1. Tell the user it's done and where it's saved
2. Offer to update /cmo or other skills to read this file instead of (or in addition to) the default voice-profile.md
3. Offer to write sample tweets in the new voice as a test

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Anti-Patterns

Anti-pattern	Why it fails	Instead
Summarizing the person's content	A summary is not a voice analysis	Analyze HOW they write, not WHAT they write about
Using fewer than 10 agents	Each dimension needs focused attention; combining them produces shallow analysis	Always launch all 10, always in parallel
Using opus for the subagents	Expensive and unnecessary; sonnet is sharp enough for focused analytical work	Always use model: "sonnet"
Launching agents sequentially	Wastes time; these are independent analyses	Launch all 10 in a SINGLE message
Skipping the adaptation agent (#10)	The raw analysis is academic without adaptation to the user's context	Agent 10 is what makes this actionable
Writing a generic "be authentic" voice guide	Useless; every voice guide says this	Every rule must have a specific pattern with a specific example
Analyzing without enough source material	Patterns need repetition to be patterns	Ask for more content if < 1 substantial post

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Related Skills

• /brand-voice — builds voice from interview or website scrape (lighter weight, less precise)
• /writing-assistant — uses the voice file to write in the extracted personal voice
• /cmo — orchestrates marketing content using the voice file
• /compound-writing — evolves the voice over time through learnings