# Micro-habit detection: your voice is not your vocabulary · ScriptGrain

> Word choice is the smallest part of a writing voice. The habits that identify you are contraction rate, sentence variation, asides, how you open. Measure twelve of yours against a reference corpus, free.

Canonical: https://scriptgrain.com/micro-habits

# Micro-habit detection: your voice is not your vocabulary

*By Jack Stovell · published 2026-09-20*

Ask people what makes their writing theirs and they name words. The ones they like, the ones they avoid. Vocabulary is real, and it is the least of it. Stylometry, the discipline that attributes anonymous texts to authors, barely looks at content words. It looks at function words, punctuation habits, sentence-length distribution, the shape of clauses: things a writer produces without deciding to, and could not fake if asked.

Those are micro-habits. They are why a friend can tell your email from a colleague's before reading the signature, and why an AI draft "in your voice" still is not: the model matched your words and none of your habits.

**Measure twelve of yours.** Paste something you wrote below. Each habit is counted in your browser and placed against a reference corpus of recent public writing, so you see which ones are typical, which are distinct, and which are yours alone.

## What a micro-habit looks like in numbers

- **Contractions**, per 1,000 words. The reference corpus runs from 8 (formal) to 32 (spoken) between the tenth and ninetieth percentile. A writer at 120 is unmistakable in one paragraph.
- **Sentence variation**, standard deviation over mean. Human prose usually sits above 0.5; a machine's average sits lower, which is why detectors score it first.
- **Short sentences**, the share under eight words. Some writers never write one. Some write one in three.
- **Asides in brackets**, per 300 words. A habit of thinking that survives every editor.
- **Semicolons, exclamation marks, ellipses.** Each one is a yes or a no for most writers, which makes each one a fingerprint.
- **Who the writing addresses.** The share of "you" against "I" and "we". Teachers and marketers write to you; diarists and analysts do not.
- **How you open, how you argue, whether you hedge.** These need a judge rather than a count; the fingerprint adds them.

## Why this matters for AI

A prompt can carry adjectives. It cannot carry habits you do not know you have. That is the whole reason "write like me" produces something generic: the model got the vocabulary and guessed the rest, and its guess is the average. Measure the habits and you can hand them over as numbers, then score every draft against them. ScriptGrain's profile does that with 45 attributes; the report below does the countable dozen with no account.

## Where word choice does come in

Preferred words, signature phrases and the words a writer never uses are measured too, and they matter in a draft. They are just not what identifies you. A draft can use all your favourite words and still read as somebody else if the rhythm is wrong. Start with the habits.

## Questions

### How accurate is a report from one piece of writing?

Indicative from 120 words, steady from about 300, and better across several pieces. A single piece written for an unusual purpose (a legal notice, a eulogy) measures that purpose, not you.

### What is the reference corpus?

Two hundred and ninety-nine recent posts from forty-seven public blogs, newsletters and essay sites, measured with the same code and discarded. Percentiles say where you sit against that set; they are not a judgement.

### Can an AI reproduce these habits if I give it the numbers?

Largely, yes, and that is the point: a number is a target a model can hit, an adjective is not. The remaining gap is what the voice-match score is for.

## Related

- [Writing style analysis](https://scriptgrain.com/tools/writing-style-analysis)
- [What writing voice is](https://scriptgrain.com/writing-voice)
- [Create a personal AI writing style](https://scriptgrain.com/personal-ai-writing-style)