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.
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
On Activation
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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.
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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?"
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If the content is rich enough: proceed immediately. Don't ask unnecessary questions. The content IS the input.
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.
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 tobrand/voices/[name].md(create thebrand/voices/subdirectory if it doesn't exist) - If standalone: save to
./voices/[name].mdrelative 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.
Phase 3: Wire In
After the voice file is written:
- Tell the user it's done and where it's saved
- Offer to update
/cmoor other skills to read this file instead of (or in addition to) the defaultvoice-profile.md - Offer to write sample tweets in the new voice as a test
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 |
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