Quiron

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Quirón: an AI humanizer skill that measures its own work

An agent skill for Claude Code, Codex and Cursor. It strips the AI slop that marks prose as machine-written, then checks the result against measured human writing instead of a hunch.

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

The animation shows what the skill does in the background. You won't see this screen when you run it, and it doesn't start a server or open a report: the model edits your text in the chat and gives you the meter's result there.

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[!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
em dashes /1k             3.76     0.00 - 2.74    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)