# AI Code Rating – Three Characters That Say How AI Was Used in Your Project > Source: > Published: 2026-11-11 18:04:59+00:00 When I'm deciding whether to depend on a project, I'd like to know how it was built. Did a person write this? Did AI write it, with someone reading every change? Or did it go from a prompt to the main branch without anyone looking? Today you have to guess from the commit history. So I made [AI Code Rating](https://aicoderating.com), a three-character rating that a project publishes in an `ACR.md` file at the root of its repository. | Position | Range | What it says | |---|---|---| | Maintainer Expertise | `A` –`E` | How well the person responsible for the code knows this language and domain (in a solo project that's you; on a team, whoever approves changes), from Expert to Non-programmer | | AI Share | `0` –`4` | How much of the code AI wrote, from none to 1–25%, 26–50%, 51–75%, and 76–100% | | Oversight | `a` –`e` | How carefully AI changes were checked, from Verified (read line by line and tested) to Unchecked (merged as generated) | Here are some examples. `A2b`` B0a``0`, Oversight is always `a`.` D4e` Every rating has its own page that explains it, such as [aicoderating.com/A2b/](https://aicoderating.com/A2b/). The [examples page](https://aicoderating.com/examples/) works through common setups, with the reasoning for each position. AI Share describes how the code was made, not how good it is. An `A3a` project, where an expert wrote most of the code with AI and checked every change, can be stronger than a `C1e` project with a little AI code that nobody checked. The rating gives readers the facts and lets them decide what matters to them. Most AI disclosure standards use a single level, such as "none", "assisted", or "generated". That folds several questions into one answer. The question I think gets missed is who's doing the checking. The same AI-written code means something different when someone who could have written it reviews it than when nobody on the project can read it. The level is self-assessed against two practical questions. Could the person responsible for the code have written it without AI, and can they explain every line? The [Related Work](https://aicoderating.com/related-work/) page compares AI Code Rating with AI-DECLARATION.md and other standards. ``` --- rating: B4b spec: "0.1" updated: 2026-11-07 --- # AI Code Rating **[ACR B4b](https://aicoderating.com/B4b/)** (Maintainer: Experienced · AI Share: 76–100% · Oversight: Reviewed) This project is rated with [AI Code Rating](https://aicoderating.com/spec/0.1/), spec version 0.1. ## How AI Was Used A few sentences in your own words. ``` That's the actual rating of the AI Code Rating site. Claude Code wrote nearly all of its code and drafted most of its text, and I directed every change and read all of it before it went in. My older project [DomainMOD](https://github.com/domainmod/domainmod), written almost entirely by hand since 2010, is `B1b`. `ACR.md` file, with the rating linked to its page, and a README badge (Markdown, HTML, or reStructuredText) for you. The badge comes from shields.io, so nothing about your project depends on the AI Code Rating site. Yes, ratings are self-reported. But an inflated rating is easy to contradict, since commits, pull requests, and issues show how a project really works, and a rating that doesn't match them costs trust. An honest `D4c` tells readers more than a doubtful `B4b`. Once your `ACR.md` passes the validator, [ask to be added](https://github.com/ai-code-rating/aicoderating.com/issues/new?template=add-project.yml) to the [Rated Projects](https://aicoderating.com/#rated) list on the home page. It's a short form that asks for the project's name, repository, rating, and a one-sentence description. The [spec](https://aicoderating.com/spec/) is version 0.1, under CC BY 4.0, and the site code is MIT. Issues and suggestions are welcome [on GitHub](https://github.com/ai-code-rating/aicoderating.com/issues/new/choose).