voice-baseline

An agent writing in your voice does not only sample the median of the internet. It samples the median of you, from the pieces and rules and memory files in its context, and it regresses toward your most frequent choices because those are the highest probability tokens in your corpus. A word that was a choice at one use comes back at five. Then the fifth use teaches the next draft, and the tic becomes the style.

Generic ban lists cannot catch this, because the words are yours and they are not on anyone's list. Measure instead.

Run it

python3 scripts/census.py PATH/TO/YOUR/WRITING --out voice-baseline.md
python3 scripts/census.py PATH/TO/YOUR/WRITING --check DRAFT.md

The census walks a directory of your own finished pieces and reports word counts with a per-file rate, sentence-shape counts, sentence openers, and heading first words. --check compares one draft against those rates and prints only what sits above them. Exit code 1 when something is above baseline, so it fits a pre-publish script.

Keep the output file next to your rules so the agent reads it before it writes.

What it measures

Words. A watchlist of praise and emphasis words, counted with word boundaries and reported per file. Read the per-file rate: a word used twice per file is a habit, and the same word at eight in one draft is the draft's problem. Edit WATCH in the script to add your own suspects; the point is that the list ends up specific to you.

Paragraph-ending verdict sentences. "The fix was one line." A concrete noun, a past-tense verb, a short predicate, parked at the end of a paragraph so it lands like a ruling. One per piece at most, and only when it carries a fact the paragraph has not already delivered.

Kicker closers. "That is the point." "That is all a title is for." A closing line that restates the piece as an aphorism. The cap is zero.

Endings. How many pieces close on a question to the reader. Most of them means the question has become a formula.

Sentence openers and heading first words. These are where a personal tic hides best, because each instance reads fine. If a third of your headings begin with the same word, you have a template. In one real 27-file archive the census found 53 headings starting with "The", the exact shape people name as an AI tell, learned from the author's own archive.

How to read the result

The census counts; it does not judge. For each flagged word or shape, decide whether it is your voice or your mode:

• Voice. The word carries meaning the sentence needs, or the rhythm is yours and deliberate. Keep it, and note the exception so the next check does not re-raise it.
• Mode. The word is praise, emphasis, or padding, and removing it changes nothing for the reader. Cut it, or replace it with the behaviour it was gesturing at.

Fix the shapes by cap rather than by ban: one verdict sentence, zero kickers, no default question ending, one narrative heading pattern per piece.

Re-measure

Run the census again after roughly ten new pieces. A tic you fixed should fall. A new one will have appeared, usually the word you reached for while avoiding the last one, which is the same substitution the model makes when a single word is banned.

Pair with slop-gate for the universal tells and no-ai-slop for the structural ones a regular expression cannot see.