Optimize content for AI Overviews (formerly SGE), ChatGPT web search, Perplexity, and other AI-powered search experiences. Generative Engine Optimization (GEO) analysis including brand mention signals, AI crawler accessibility, llms.txt compliance, passage-level citability scoring, and platform-specific optimization. Use when user says "AI Overviews", "SGE", "GEO", "AI search", "LLM optimization", "Perplexity", "AI citations", "ChatGPT search", or "AI visibility".
Collected from public GitHub. Every version is saved exactly as it was and never changes. Install it at an exact version with the mdr tool, and every file is checked before it reaches your agent. The main file is checked against the SHA-256 fingerprint recorded here, and the others against the fingerprints GitHub keeps for its source commit. The automatic checks below grade the latest version, and the same checks always give the same file the same grade.
What the file says
# AI Search / GEO Optimization (May 2026)
## Primary Source: Google's AI Optimization Guide
Google's official position, published under Search Central docs:
> "Optimizing for generative AI search is **still SEO** from Google's
> perspective. AEO and GEO are rebranded labels for the same work."
Read `references/google-ai-optimization-guide.md` for the full synthesis,
myth-busting list (`llms.txt`, chunking, AI-rephrasing, mention-farming,
all rejected by Google as ineffective), and the Who/How/Why test for
content quality.
Audits should frame GEO findings as **SEO fundamentals applied to AI-search
surfaces**, not as a separate optimization discipline. When community
recommendations contradict Google's primary source, defer to Google and note
the contradiction in the report.
## Key Statistics
| Metric | Value | Source |
|--------|-------|--------|
| AI Overviews reach | 2.5 billion+ monthly active users, reported from Google I/O 2026 keynote coverage; not confirmed on a Google-owned source; 200+ countries | Third-party I/O reporting |
| AI Overviews query coverage | ~50% of queries (third-party measurement; varies by country) | Industry data |
…
The line at the top of this page pins the label v2.2.5, which is what most people want. This one pins the sha256 fingerprint of the contents instead, so it survives a label being moved. Either way the exact version is written to mdr.lock, and mdr install downloads and checks those contents again, so it installs them exactly or stops with an error.
A 17 of 17 checks passed. Fixed automatic checks with no AI involved, so the same checks give the same answer every run.
pass: Frontmatter block present
pass: Frontmatter declares a name
pass: Frontmatter declares a description
pass: Size between 200 bytes and 200 KB (14808 bytes)
pass: No zero-width or bidi control characters
pass: No instruction hidden inside an HTML comment
pass: No link to an exfiltration or paste host
pass: No credential-shaped string
pass: No instruction to send local credentials anywhere
pass: No text hidden with inline styles
pass: No prompt-injection phrasing
pass: No curl or wget piped into a shell
pass: No recursive delete of root, home or parent
pass: No instruction to read or print local credentials
pass: No base64 blob over 200 characters
pass: No link to a raw IP address
pass: No script tag
Source
GitHub
haniubub/seo-toolkit · 8 stars · license a license file GitHub does not identify · pushed 2026-09-11 · branch main
API
GET https://markdownregistry.com/api/v1/artifacts/art_iusmea7zsjhf6oyj
GET https://markdownregistry.com/api/v1/resolve?ref=haniubub/seo-toolkit/seo-geo
GET https://markdownregistry.com/api/v1/blob/41e3012757cd34f6d8726d807d08688ca04833bc91172ebb4118a681f16db0cc
Your AI assistant does the legwork. You hear about the deals worth your word. Hand yours the standing instructions at modelranch.com and it joins the network that reads files like this one.
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