| name | build-voice-vocabulary |
| description | Builds a Voice Vocabulary for an author — a dictionary of their characteristic phrases, turns of speech, fillers, metaphors, and contrarian formulas. Used as a humanization layer on top of the DIP. Collected ONLY from raw sources (transcripts, voice input, personally written Telegram posts), NEVER from the author's LinkedIn posts. |
| disable-model-invocation | true |
| argument-hint | ["Author"] |
| allowed-tools | Read, Grep, Glob, Edit, Write |
Build Voice Vocabulary
Voice Vocabulary is a layer on top of the DIP (Digital Identity Profile), specifically for humanizing the final draft of a post. The DIP describes tone and topics. Voice Vocabulary provides verbatim phrases from the author that you can use as targeted replacements for AI markers.
When to invoke
- After the author has a DIP at v0.7+ (3+ transcripts in
_Sources/).
- On the first humanization attempt — when it becomes clear that DIP quotes aren't enough.
- Every 3 months — refresh with new transcripts.
Pipeline (8 steps)
Step 1: Collect source list
Read everything available about the author:
- Transcripts of calls, podcasts, interviews where the author is a speaker
- Voice input (if audio logs exist)
- Telegram posts personally written by the author (not reposts)
- Internal meetings where the author talks a lot live (NOT a Granola summary — you need the full transcript)
- The author's CV, articles (only if written by them, without AI)
❌ Do NOT use:
- The author's LinkedIn posts (even their own) — that's OUTPUT, feedback loop = voice degradation
- AI-generated content, even if the author approved it
- Other people's posts "in the author's style"
- Transcripts where the author only reacts to others (chat comments during someone else's talk)
Step 2: Spin up the template
Copy Templates/Voice-Vocabulary-Template.md → Knowledge-Base/01-Identity-Profiles/_Voice-Vocabulary/[Author]-Voice-Vocabulary.md.
Fill the frontmatter (author, sources, language coverage, status: v0.1 initial).
Step 3: Extract Sentence Starters
Read each transcript. Find recurring sentence openings (5+ times for high, 3–5 for medium, 1–2 for low).
Record in this format:
| Phrase | Frequency | Example (with source) | When to use |
| "I think I figured out..." | high (10+) | "I think I figured out how to give agents access..." — TG 2026-02-16 | Hedged claim setup |
Start with high-frequency patterns (10+). Then medium. Skip low — it's not a pattern.
Step 4: Idiomatic Phrases
Personal turns of phrase that are recognizable as this author. Often — short 2–4-word expressions with characteristic flavor.
Test: "if I heard this phrase from behind a wall, would I know it's [Author]?"
Step 5: Filler Words / Tics
Filler words in spoken speech. Important: use them carefully in LinkedIn posts — 1 per post, no more. They work as a micro-signal of live speech, but oversaturation becomes irritating.
Step 6: Categories 4–9
Same approach for:
- Characteristic Adjectives & Verbs
- Code-Switching Patterns (if the author is multilingual)
- Numbers & Scale References (how they handle numbers)
- Recurring Metaphors (metaphors with repetition — NOT one-offs)
- Contrarian Move Formulas (formulas like "everyone thinks X, in reality Y")
- Closing Patterns (how they wrap up a thought)
Step 7: Sources section
At the end of the file — a table of all source files with type and date. This lets you:
- Update properly as new transcripts appear
- See which category is under-sampled (e.g., only 1 EN source → English coverage is weak)
Step 8: Status + What's NOT covered
In the footer, set the status (v0.1 / v0.5 / v0.7 / v1.0) and the "Not Covered / TBD" block:
- Categories with insufficient entries
- Hypotheses to verify against future transcripts
- Questions for the author
Validity checklist
After completing the first pass, walk through the checklist:
If it fails on ≥2 items — that's v0.5 (working), not v1.0.
Anti-patterns
- ❌ Rebuilding the DIP inside the Voice Vocabulary — overlapping work.
- ❌ Taking "unique" words from a single transcript as a pattern — that's noise, not signal.
- ❌ Including phrases the author used in their first-ever public talk — they may sound unnatural there.
- ❌ Auto-generating from posts — that produces a feedback loop and loss of voice.
Output
A file at Knowledge-Base/01-Identity-Profiles/_Voice-Vocabulary/[Author]-Voice-Vocabulary.md, ready for use by /detect-ai and /check-generic validators as a reference for humanization replacements.
