Hand it a draft and ask it to humanize the text, or to make a README sound less like
ChatGPT. It rewrites the prose, then measures the result. You get the rewrite and a
measurement that says whether it now reads like a person wrote it.
Install
With the skills CLI, for Claude Code and every other agent it
supports, Gemini CLI and GitHub Copilot among them:
npx skills add ilien-dev/quiron
It needs no API key. It runs on the model you already use, and the scripts need nothing
beyond the Python 3 standard library.
As a Claude Code plugin
Run these two commands inside Claude Code:
/plugin marketplace add ilien-dev/quiron
/plugin install quiron@quiron
The plugin puts the skill under its own name, so you call it as /quiron:quiron.
Demo: behind the scenes
[!IMPORTANT]
Use it responsibly. Quirón is for text you write for yourself or your own team: a
how-to guide, internal docs, meeting notes, a draft nobody outside the company will
read. The point is text that feels close and easy to read, not text that pretends a
person wrote it.
Do not use it to pass AI writing off as human in public: social media, published
articles, schoolwork, reviews, job applications, anything where the reader has a right
to know who wrote it. Never use it to deceive or defraud anyone. It also does not beat
AI detectors that read token probabilities, and it was not built to.
What it catches
Ask a model for a blog post and you get a shape you learn to spot: lists of three, about
twice the headings a person would use, a summary section at the end, "it's not just X,
it's Y" and em dashes where a comma would do. Quirón gives the model a checklist of 33 of
these signs of AI writing and a meter that says in numbers whether the fix landed or went
too far.
Delve is not on the list anymore. On this repo's 2026 run it turned up in 1% of
assistant posts or fewer, roughly as often as in human ones. The words that give 2026
models away are ordinary ones you'd never flag by eye, used at many times the human rate
(the list is in references/word-choice.md). The
other half of the signal is what's missing: models rarely write very or able, and
people use both all the time.
Here is the meter on an assistant-written Stripe tutorial from eval/ai/blog/:
$ python3 scripts/aimeter.py eval/ai/blog/e2e-raw/aspittel-782713.md
feature this human band verdict
long words (7+) /1k 353.83 159.68 - 250.46 above band, AI side
nominalizations /1k 55.21 7.29 - 32.65 above band, AI side
mean word length 5.46 4.28 - 4.86 above band, AI side
lists of three /1k 11.29 0.00 - 5.51 above band, AI side
plain words /1k 33.88 43.19 - 95.24 below band, AI side
...
13/23 features inside the human band (p10-p90 of 167 human texts)
! triads: retries, cancellations, and preventing, refund, tax, and privacy
The rewrite of the same post, built from the author's own notes, scores 23 of 23.
How it differs from other humanizer skills
The best-known humanizer skills, humanizer and
stop-slop, give the model a list of AI
writing patterns to remove, and they work. So does no-ai-slop.
Stop-slop also has the model score its own draft from 1 to 10 on five questions.
Quirón has a pattern list too, but the model doesn't grade itself. A script measures 23
rates in the text and compares each one with the range found in human writing published
before ChatGPT. That catches a failure a checklist can't see. Tell a model to write like a
person and it usually overshoots: choppier and plainer than any person writes. The meter flags that as
loudly as the AI side.
What backs it
Every rule and number traces to a published study or to a run of the scripts in this
repo. Nothing ships on "this reads better".
- Human baselines. The bands come from 167 dev.to posts and 157 WritingPrompts
stories, all written before ChatGPT existed, plus a Spanish set. Since November 2026
there are also bands for pull request descriptions, commit messages, READMEs and work
email. A quarter of each is
held out and never used to pick anything.
- 2026 AI text on the same titles. Claude Opus, Sonnet, Haiku and GPT wrote the
comparison texts in
eval/ai/, from the plain prompt a user would type. They were
written in September 2026 and again in November with the models current then.
- Two failure modes. Text can sit on the AI side of a band or overshoot past the
human side. Overshoot is a tell too: models told to "write like a human" go choppier
and plainer than any person does. The meter flags both.
- Blind judges. Fresh model judges read posts one at a time and guessed which were
AI. That test produced the most important finding below. Since September 2026 every
change to the rewrite rules is also judged by Claude and GPT judges on titles it was
never tuned on, in English, Spanish and fiction; the harness is in
eval/e2e/.
The details of how each number was measured are in SKILL.md and
eval/README.md, along with the source of every rule.
Tips for text that reads human
The judges were not fooled by style edits alone. A rewrite that put every rate inside
the human band was still judged AI 12 times out of 12. What moved them was the writer's
own material. So:
This is the beginning of the README. Read the rest at the original. Read the full README ↗
