my-voice

name	my-voice
description	Use when writing or rewriting text that must sound like a specific real person wrote it (their Reddit post, email, blog, bio, social copy) — the user offers a writing sample, asks to "put this in my voice", "make it sound like me", "de-slop this", or complains that a draft reads like AI. Distills a reusable voice profile from the sample and rewrites against it.

My Voice

Overview

Rewriting "in someone's voice" fails two ways at once: the agent applies generic anti-AI de-slopping that makes text clean but anonymous, and it leaves its OWN tells in (em-dashes, "not just X, it's Y", rule-of-three). The fix is to stop working from vibes: distill a concrete, checkable profile from a real sample, then verify the output against it mechanically. A voice is a set of observable habits, not a mood.

Method

1. Get a real sample

Ask for one if none given. Rank by fidelity:

1. Unedited chat/DM/message history (best — natural, unperformed)
2. Emails, comments, forum/Reddit posts
3. Published blog/marketing (worst — already edited, may not be their own hand)

Aim for ~400+ words across 2–3 pieces. Prefer the genre being written (a Reddit voice ≠ a bio voice). If the only sample is one polished paragraph, say so — the profile will be thin.

2. Distill the profile (write it down, don't hold it in your head)

Fill every dimension from the sample with a specific observation + a quoted example. Guessing = leave it "unknown", never invent:

• Sentence length & rhythm — short/medium/long; uniform or varied? run-ons?
• Punctuation habits — em-dashes? semicolons? parentheticals? ellipses? Note what they use to join clauses instead (comma splices? periods? "So"/"Then"?).
• POV & address — first person? second-person "you"? name the reader?
• Openers & connectives — how sentences start ("So", "The problem was", "Look,").
• Register — dry / wry / warm / blunt / profane / formal. Hedged or flat-assertive?
• Concreteness — numbers-first? named examples? or abstract?
• Honesty tics — do they admit dead ends, uncertainty, self-deprecation?
• Signature quirks — lowercase, specific words, a recurring move.
• Never-does — the strongest signal. What's absent from the sample that AI defaults to? (usually: em-dashes, antithesis, exclamation points, marketing words.)

3. Interview for stated preferences (the sample can't tell you these)

A sample shows what the person did, not what they want or consciously avoid. Ask a short round (one question at a time, skip any the sample already answers clearly):

• Aspirations vs habits — "Anything in these samples you'd change? Words or moves you fall into but don't like?" (Habits you'd otherwise faithfully reproduce as tells.)
• Hard bans — "Words, phrases, or punctuation you refuse to use?" (People often have a personal blocklist beyond the universal AI tells — e.g. never "utilize", never emoji, never exclamation points.)
• Register range — "Same voice everywhere, or more formal for X and looser for Y?" Capture per-context variants if so.
• Contractions / formality / profanity — confirm the defaults, since these swing by audience and the sample may be one point on that range.
• The tell test — "What makes writing instantly read as AI-generated to you?" Their answer goes straight into the kill-list; it's the most personal de-slop signal there is.

Record stated preferences in the profile and mark them as stated (vs observed from the sample). When they conflict, stated preference wins — it's the conscious choice.

4. Build the de-slop kill-list

Two layers:

• Universal AI tells (strip always): em-dash-as-connector, "not just X, it's Y" / "it's not A, it's B" antithesis, rule-of-three cadence, marketing register (seamless, powerful, robust, leverage, unlock, delve, elevate, game-changing, transformative, thrilled, dive in, testament to, "in today's ... world"), exclamation points, "Whether you're X or Y", a summarizing "In conclusion/Ultimately" outro, emoji-as-decoration, every-paragraph-perfectly-balanced.
• Person-specific — anything the sample proves they never do (e.g. "sample has 0 em-dashes across 400 words → em-dashes are banned for this voice specifically"), plus the stated bans from the interview.

5. Persist the profile

Save it to .claude/voice/<name>.md (or the user's memory dir) so future rewrites reuse it instead of re-deriving. Keep the observed / stated tags. Reuse and refine it when more samples or corrections appear.

6. Rewrite, then run the checklist

Draft in the voice, then verify against the profile before returning. This step is the skill — an unverified rewrite is where the tells survive:

• Em-dash count matches the sample's rate (usually 0). Grep the output.
• Zero "not X, it's Y" antithesis constructions.
• No word from the marketing kill-list or the person's stated bans.
• Sentence openers and connectives match the profile's list.
• Sentence-length distribution roughly matches the sample.
• Honesty tics present if the person uses them (kept the dead ends, the "here's what's broken").
• Read one paragraph aloud against a sample paragraph — same person?

If any box fails, fix that specific thing and re-check. Do not return on first draft.

Common mistakes

• Generic-casual ≠ their voice. "Made it conversational" is not the job; matching their specific habits is. A dry numbers-first writer rendered as a breezy blogger fails even with every AI word gone.
• Injecting your own tells while removing theirs. The em-dash and the antithesis are the two that survive most often — they feel like "good writing", so agents add them back. The checklist exists because self-assessment misses them.
• Over-applying de-slop. If the person genuinely uses semicolons or the occasional em-dash, keep them at their rate. De-slopping means matching the human, not stripping to a lowest-common-denominator plainness.
• Inventing quirks from a thin sample. One paragraph can't tell you their opener habits. Mark unknowns; don't fabricate a persona. The interview (step 3) is how you fill gaps the sample leaves — ask, don't guess.

Profile template

# Voice profile — <name>
Distilled from: <sources>. Tags: observed = seen in samples; stated = told directly.

## Dimensions
- Sentence length & rhythm (observed): ...
- Punctuation (observed): ...
- POV & address (observed): ...
- Openers & connectives (observed): ...
- Register (observed): ...
- Concreteness (observed): ...
- Honesty tics (observed): ...
- Signature move (observed): ...

## Kill-list (never, for this voice)
- Person-specific (observed absent): ...
- Stated bans (interview): ...
- Universal AI tells: ...

## Register range (stated): ...
## Open (ask next time): ...