Voice profile — write toward something, not just away from tells

`/humanize` is the negative direction: it finds AI tells and says
what to remove. That leaves a draft that is merely less bad.

This is the positive direction: a written description of how you actually write,
extracted from your own published work, so a draft can be measured against a target instead of
a taboo list.

What this does not do. A voice profile makes prose sound like your prose. It does not
make model-generated text stop reading as model-generated to a neural detector — nothing an
LLM applies to its own output does. See `writing-with-ai.md`.
Use this to write well in your own register; write the load-bearing sentences yourself.

Building the profile

1. Assemble the corpus, and count it

Three to twelve of your own pieces where you were the primary writer. Published papers
are best — they survived editing. Mix genres if you write in several (paper, referee report,
grant, teaching notes); the profile should note where your register changes.

find <corpus-dir> -maxdepth 1 \( -name '*.pdf' -o -name '*.tex' \) | wc -l

(find, not a glob — in zsh an unmatched glob aborts the whole command, which
reports 0 and defeats the count this step exists for.)

Count before starting. A corpus of eleven is a different task from four, and discovering
that halfway through is how a session gets reset.

2. One subagent per document — never load the corpus into one context

Per `pdf-processing.md`: spawn one subagent per document
with context: fork. Each reads only its own file, writes a ~300-word note to
notes/voice/<name>.md against the fixed schema below, and returns only the filename.

The main session then reads only the notes. Loading a whole corpus at once has repeatedly
forced a session reset after partial work was already lost.

Per-document note schema — the same six headings every time, so the synthesis can compare:

## Lexicon      words and phrases used repeatedly; words conspicuously avoided
## Rhythm       typical sentence length; variance; where long sentences appear
## Openings     how sections and paragraphs begin; how the paper opens
## Transitions  the actual connectives used, verbatim, with rough frequency
## Hedging      how uncertainty is expressed; how strong claims are made
## Quirks       anything distinctive — punctuation habits, first person, humour, footnotes

3. Synthesize, and mark what is stable

Read only the notes. A trait belongs in the profile if it appears across most of the
corpus, not because one paper did it once. Record frequencies where you can: "'note that'
appears in 7 of 9 papers; 'delve' appears in none."

Write to voice-profile.md at the repo root (allowlisted in the repo-hygiene gate). Include:

• Signature vocabulary and the avoid list — words the author demonstrably does not use.
• Sentence rhythm, with a number: median length, and where the long ones land.
• Structural habits — how an introduction is built, where the contribution paragraph sits,
  how results are framed.
• Hedging register — the author's actual calibration language, which is usually narrower
  than a model's default.
• Deliberate quirks, labelled as deliberate. "Uses em-dashes frequently and on purpose"
  stops /humanize from flagging a habit as an AI tell.
• Where the register shifts by genre.

4. Wire it in

/humanize reads voice-profile.md when present and respects documented preferences — a
quirk you have declared deliberate is no longer a finding. Point drafting work at the profile
before it writes, not after.

Auditing a draft

Pass --audit followed by a filename to compare an existing draft against the profile instead of building one:

/voice-profile --audit main.tex

Report, per section: distance from the profile, with concrete evidence — vocabulary outside
your range, hedging denser than your baseline, transitions you do not use, sentence rhythm
that has flattened. Every finding cites the profile line it violates, so it is a deduction
rather than taste.

Read-only. Auto-rewriting prose degrades it and introduces new tells, and it cannot change
what a detector sees. The report says where and why; the author edits.

Anti-patterns

• Profiling coauthored work you did not draft. You will extract someone else's voice.
• Treating the profile as a rulebook. It describes what you have done, not what you must
  do. Voices change; re-profile after a few new papers.
• Building it from AI-assisted drafts. The profile will encode the model's register as
  yours — the corpus must be work you wrote.
• Using it to pass a detector. It is a writing aid, not a laundering step. If a venue wants
  an AI-use statement, make one (`/submission-disclosures`).

Cross-references

• `writing-with-ai.md` — readability vs provenance, and the human-readable standard
• `/humanize` — the negative direction; reads this profile when it exists
• `/proofread` — grammar and consistency, a separate lens
• `pdf-processing.md` — the one-subagent-per-document pattern