What does this skill do, and when should you use it?
AI Search & GEO Optimizer is the seo-geo skill inside the claude-seo repository. It evaluates how well a URL is prepared for AI-powered search while framing GEO and AEO as SEO fundamentals applied to new search surfaces. Its checks cover passage citability, structure, authority signals, crawler access, rendering, and platform-specific considerations. It is a good fit for evidence-led AI search audits, but it is not presented as a standalone ranking guarantee or a replacement for the repository’s other SEO skills.
Accepts a URL, reads the relevant reference material, and evaluates passage-level citability, heading structure, question-and-answer formatting, sourced claims, authorship, dates, multimedia, brand signals across Wikipedia, Reddit, YouTube, and LinkedIn, and server-side rendering. It checks robots.txt for GPTBot, OAI-SearchBot, ChatGPT-User, ClaudeBot, PerplexityBot, and other named crawlers, along with llms.txt and RSL 1.0. It provides platform-oriented analysis for Google AI Overviews, Google AI Mode, ChatGPT, and Perplexity, then produces GEO-ANALYSIS.md with a readiness score, platform scores, crawler status, brand-mention findings, citation-ready passage guidance, schema recommendations, and prioritized changes. When available, optional DataForSEO MCP tools can be used to inspect ChatGPT web-search results and LLM mentions.
- An SEO consultant audits whether a client page contains self-contained 134–167-word answer passages and needs concrete rewrite recommendations.
- A content team is preparing an article for AI Overviews, ChatGPT, or Perplexity and wants checks for sourcing, question-led headings, and entity signals.
- A technical SEO specialist needs to assess client-side JavaScript dependence and verify whether important AI crawlers can access the site.
- A brand team wants to review brand presence on Wikipedia, Reddit, YouTube, and LinkedIn as part of an AI-visibility plan.
How do you install this skill?
- Many dated statistics, platform behaviors, model names, and policy conclusions require revalidation before use; no network access or scripts were executed in this review.
- The skill recommends checking llms.txt and RSL, but adoption, crawler behavior, and major-platform support can change; file presence must not be treated as a ranking guarantee.
- The workflow depends on the target URL, robots.txt, and reachable external search services; mainland-China network restrictions may require an explicitly limited static-audit fallback.
- Repository-wide SSRF and credential controls do not prove that every invocation path of this selected skill enforces those controls.
- Network access
- Local filesystem
- MCP Server
This skill is part of the 33-skill claude-seo repository. The README documents installation for the full collection: in Claude Code, run /plugin marketplace add AgriciDaniel/claude-seo, then /plugin install claude-seo@agricidaniel-claude-seo; alternatively, clone the repository and run bash claude-seo/install.sh. The source does not document a standalone installation procedure for seo-geo. The repository is MIT licensed.
tmp="$(mktemp -d)"
git clone --depth 1 https://github.com/AgriciDaniel/claude-seo.git "$tmp"
mkdir -p ~/.claude/skills
cp -R "$tmp/skills/seo-geo" ~/.claude/skills/
rm -rf "$tmp"Generated from the source repository and skill path; it copies only this skill's folder. If the author's install steps above differ, follow those first. To scope it to one project, replace ~/.claude/skills with that project's .claude/skills.
How do you use this skill?
In an installed Claude Code environment, run /seo geo https://example.com, or invoke the skill with its URL argument. It produces GEO-ANALYSIS.md. If the optional DataForSEO MCP tools are available, they can also inspect ChatGPT search results and LLM brand mentions; this extension is not required by the core workflow.
What are this skill's strengths and limitations?
- Covers passage citability, page structure, brand mentions, and technical access in one AI-search-oriented audit.
- Separates the analysis focus for Google AI Overviews, Google AI Mode, ChatGPT, and Perplexity.
- Uses concrete checks for attribution, author information, freshness, and server-side rendering.
- Defines a report containing scores, prioritized changes, and specific content-reformatting guidance.
- The skill is specifically for GEO and AI-search analysis; capabilities from the repository’s sibling skills must not be attributed to it.
- Several metrics and platform details are dated 2025 or 2026, so their current validity should be verified before relying on them.
- Referenced evidence files are not included in the supplied source text, so some analysis details may depend on files present in the repository.
- DataForSEO inspection is optional and requires the relevant MCP tools; the source does not establish that every named platform or API is available in every environment.
How does this skill compare with similar options?
Side by side with related skills; every score comes from the same FSRS standard.
| Skill | FS score | Stars | Last updated | License |
|---|---|---|---|---|
| AI Search & GEO Optimizer this page | 52 · Use with care | ★ 19k | 23d ago | MIT |
| Claude SEO Site Analysis | 46 · Use with care | ★ 19k | 23d ago | MIT |
| GEO Full-Site Audit | 48 · Use with care | ★ 11k | 22d ago | MIT |
| GEO Crawlers Access Analysis Skill | 51 · Use with care | ★ 11k | 22d ago | MIT |
| Single-Page SEO Inspector | 51 · Use with care | ★ 19k | 23d ago | MIT |
The source does not provide a direct comparison for seo-geo alone. README comparisons involving Screaming Frog and Ahrefs apply to the broader Claude SEO collection and should not be attributed specifically to this skill.
How did FollowSkills review this skill?
The skill explicitly defines handling for unreachable URLs and crawler blocks, while repository context documents SSRF defenses, credential permissions, isolated dependencies, and security reporting. Deducted 11 points because the skill itself does not specify least privilege, user confirmation, external-request data flows, third-party MCP permissions, revocation, or rollback/recovery.
The skill defines inputs, persistent output artifacts, audit checks, and several abnormal-input responses; repository CI and tests provide partial static consistency and security evidence. Deducted 12 points because there are no dedicated seo-geo key-path tests, edge-case coverage, script contracts, or failure-output examples, and optional references/extensions may be unavailable. Static review cannot reproduce execution.
The audience, trigger phrases, URL-oriented scenario, and expected report sections are clear. Deducted 5 points because non-fit boundaries, URL assumptions, and no-URL behavior are underspecified; core checks depend on reachable external sites, search surfaces, or optional services, with no mainland-China connectivity, proxy, or offline fallback guidance.
Front matter provides name, description, argument hint, MIT license, author, and version. The material is organized into criteria, platform differences, quick wins, error handling, and linked references. Deducted 5 points because installation/dependency notes, changelog, maintenance ownership, and update path are not defined in the selected skill; several claims and standards are strongly date-dependent and rely on separately loaded reference files.
The skill specifies a directly usable GEO report containing platform scores, crawler status, llms.txt review, brand signals, passage citability, SSR analysis, prioritized changes, schema guidance, and rewrite suggestions. Deducted 9 points because no representative seo-geo output, result validation, or key-path execution evidence is supplied; many statistics and thresholds require manual fact checking. Static calibration limits effectiveness to 7.
The skill cites a Google guide and an evidence-focused llms.txt reference and instructs auditors to prefer primary sources; repository CI, tests, and verification dates add supporting context. Deducted 6 points because the selected skill has no dedicated tests, fixed fixtures, independent reproduction records, or corroborated result set, and third-party statistics are mainly asserted in documentation. Static calibration limits verifiability to 5.
Open a dimension to read why it scored that way
Evidence confidence：Low — Mostly static review, author material or a limited demo; useful for discovery, not high-risk decisions.
See the full review method →