GEO skills
Your GEO audit says 87 out of 100. Can ChatGPT's crawler even reach the page?
A score cannot tell you. These nine free skills can: they list what blocks a page from AI search, count how often AI engines actually name you, and say when the numbers are too thin to mean anything. For Claude and other AI assistants, and every script also runs on its own from the command line.
Built on the published method of the AI Visibility Census.
Why use this instead of a GEO score
| What a score gives you | What these skills give you |
|---|
| One number out of 100 | Findings in order of what blocks you, each with what to do |
| A number nobody has tested against AI answers. We searched nine open-source GEO audit repositories on 10 November 2026 and found no published test | No score. And we are testing in public whether scores go with being named at all, with the result published either way |
| "Up 17 points this month", with no way to tell a real change from chance | "Up 17 points, and that is inside the noise": every rate comes with a 95% range and the number of answers behind it |
Also here, and not common:
- Every tactic is graded by its evidence (tactics/EVIDENCE.md): the platform documents it, a study found it, or nobody has tested it. llms.txt is graded untested, and we say so.
- Crawler visits are verified against the IP ranges the companies publish, so a fake "GPTBot" is not counted.
- Experiments are registered before the data exists, and null results get published.
- Nothing to install beyond Python 3. No account for the checks, and your data stays on your machine. Only the answer collector needs a key, and it is your own.
- Tests run on every change.
What you get is less flattering than a score and more useful: the three things to fix, the count you can defend to a client, and a plain "we can't tell yet" when that is the truth.
When another tool is the better choice: if you want a full SEO suite, client-ready PDF reports or content generation, the larger repositories do far more than this one, and several are very good at it. This repo does one job: honest measurement.
Start here
Check one page, no account and nothing to install beyond Python 3:
git clone https://github.com/aivisibilitycensus/geo-skills
python3 geo-skills/geo-check/scripts/geo_check.py https://example.com/your-page
You get a short list of what stops AI search engines from reading that page, each item with the evidence behind it. Example output. There is no score out of 100, on purpose: whether audit scores go with being named by AI is an open question, and we are testing it in public.
Run it on every deploy
As a GitHub Action (on GitHub Marketplace), with the findings in the job summary:
- uses: aivisibilitycensus/[email protected]
with:
urls: |
https://example.com/
https://example.com/pricing
The job fails if a page has a blocking finding, such as a noindex that shipped from staging or a robots.txt rule against a search crawler. When nothing blocks, --badge prints a README badge that states the count and the date, not a score:
See it work
The answer log analyzer on its worked example (a fictional Dublin accounting firm, 108 logged answers a month on three engines):
| Engine | Named, September | Named, October |
|---|
| chatgpt | 28% (16-44%), n=36 | 33% (20-50%), n=36 |
| copilot | 14% (6-29%), n=36 | 19% (10-35%), n=36 |
| perplexity | 31% (18-47%), n=36 | 47% (32-63%), n=36 |
A nearly 17-point jump on one engine, and the report says it is not outside the noise. That is the point of these skills: counts with ranges, not a score. Full output: answer-log-analyzer/examples/example-report.md.
以上为 README 的开头部分。其余内容请阅读原文。 查看完整 README ↗
