Writing Tells
Paste text. Get a score from 0 to 100 and every signal behind it, highlighted in place.
The score measures how much the writing leans on the habits language models overuse. It is not proof of anything. How it works and where it fails.
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0 wordsWhat the score means
A high score means the writing leans hard on habits that language models overuse. It doesn't mean a machine wrote it. Plenty of careful writers love em dashes and lists of three.
We count fifteen things. Twelve push the score up, like em dashes and words such as "delve". Three pull it down, like contractions and specific numbers, because those are hard for a model to fake without being asked.
Nothing here is trained on a corpus. We picked the thresholds by hand, which is why you can see every count. If you think we're wrong about em dashes, the number is right there and you can ignore it.
Short text is unreliable. Under 80 words we say so on the page. Give it 200 words and the score settles down.
The tells, biggest first
| Tell | What it looks like | Weight |
|---|---|---|
| Model lexicon | delve, tapestry, testament, realm, robust, seamless, crucial, foster, navigate, underscore, showcase, journey | 16 |
| Em and en dashes | A dash where a comma or a full stop would do | 12 |
| Even sentence length | Every sentence about the same length | 12 |
| "Not X, but Y" | "It isn't about speed, it's about trust" | 10 |
| Stock phrases | "in today's", "it's worth noting", "plays a crucial role" | 8 |
| Lists of three | "clarity, precision, and impact" | 8 |
| Label-colon lines | Bullets that open with a bold word and a colon | 8 |
| Transition openers | Additionally, Furthermore, Moreover, Ultimately | 8 |
| Emoji or heading openers | A rocket at the start of a line that isn't a document | 5 |
| Assistant sign-offs | "I hope this helps", "let me know if" | 5 |
| "Not only … but also" | The paired intensifier | 4 |
| Question then answer | "Why does this matter? Because…" | 4 |
| Contractions subtracts | don't, it's, we've | 5 |
| Numbers and dates subtracts | Prices, counts, years, measurements | 6 |
| First person subtracts | I, my, we, our | 4 |
Why there's no verdict
Detectors that tell you "97% AI" get it wrong often enough to ruin someone's week, and they're worse on people writing in a second language, who lean on the same formal constructions models do. We're not doing that.
This page tells you which habits your text uses and where. You decide what it means. The highlighted text is the useful part; the number is just a way in.
Common tells, explained
The em dash. Models produce them far above the human rate, partly because typing one costs you nothing if you're a model. One or two is normal. One in every second sentence isn't.
The lexicon. "Delve" became the joke in 2024, so models moved on. The current crop is quieter: robust, seamless, crucial, foster, navigate, showcase. Any one of them is fine. Six in a paragraph isn't.
Not X, but Y. It sounds like insight and costs nothing to produce. Once you notice it you can't stop noticing it.
Even sentence length. People write a long tangled sentence and then a short one. Models settle into a comfortable length and stay there. This is the tell that survives a vocabulary edit, which makes it the most useful one here.
The label-colon bullet. Bold word, colon, explanation, five times over. Ask a model to explain anything and this is the shape you get back.
The sign-off. "I hope this helps" in an essay means someone pasted straight out of a chat window.
My score is high. Now what?
Read the highlights and fix what you agree with. Swap dashes for the punctuation you meant. Cut the lexicon words and say the plain thing. Turn one "not X, but Y" into a sentence that just says Y. Write a short sentence after a long one.
Then add something only you could know. A number, a name, a date, what actually happened. That's the part no model can fake, and it's what drops the score honestly.
Nothing you paste is saved or sent anywhere.