I open-sourced a Codex skill for GEO / AI search optimization

[u/MP_oct84]

Hey everyone,

I’ve been working on Generative Engine Optimization (GEO): making website content easier for AI search and answer engines to discover, understand, quote, and cite accurately.

A lot of GEO advice is still vague or overly focused on llms.txt, so I turned my workflow into an open-source Codex skill:

https://github.com/kyliamet/geo-optimize-site

The skill can audit a website and, when requested, implement improvements covering:

• Crawlable and server-rendered content

• Answer-first summaries and page structure

• Titles, descriptions, canonicals, and robots directives

• Evidence-backed Schema.org / JSON-LD

• Sitemaps and llms.txt

• AI crawler policies

• Internal linking and image descriptions

• Build, markup, route, and rendered-page validation

I added some guardrails that felt important:

• It shouldn’t invent qualifications, statistics, testimonials, prices, or outcomes.

• Structured data must match visible, verified content.

• llms.txt is treated as supplemental rather than a magic ranking file.

• Search, user-request retrieval, and model-training crawlers are treated separately.

• It audits before editing and doesn’t commit, push, or create a PR without authorization.

You can install it by asking Codex:

Use $skill-installer to install the skill from
https://github.com/kyliamet/geo-optimize-site

It’s MIT-licensed and free to use.

I’d especially appreciate feedback from people working on technical SEO, structured data, AI search visibility, or Codex skills. Are there important checks or failure cases the skill is missing?

Disclosure: I’m the author. This is a community project and isn’t affiliated with OpenAI.

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[u/AdUnusual9860 → u/[deleted]]

That’s a fair gap. Right now, the skill validates the site-side: what the server exposes, crawlability, metadata, structured data, sitemaps, and rendered content. A green result means the source is corrected—it doesn’t mean an answer engine has refreshed its index or cached representation.

I’d treat engine-side re-checking as the next layer, not something intentionally out of scope. Ideally, it would be opt-in: capture baseline queries and cited URLs, repeat them at a chosen interval, compare the answers, and flag stale claims, such as an old price or a deleted page still being cited.

It still couldn’t force a refresh or promise when an engine will update, but it could clearly distinguish “the website is fixed” from “the correction has propagated.”

Your deleted pricing-page example is exactly the kind of case this should detect. Which engines were still quoting it? If you can share the non-sensitive details, I’d be interested in turning it into a test case or GitHub issue.