voice-profile

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: