Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/Hainrixz/claude-seo-aiWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/hainrixz/claude-seo-ai/ai-search-geo-specialist)<a href="https://agentmods.dev/agents/hainrixz/claude-seo-ai/ai-search-geo-specialist"><img src="https://agentmods.dev/badge/agents/hainrixz/claude-seo-ai/ai-search-geo-specialist/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/hainrixz/claude-seo-ai/ai-search-geo-specialist"><img src="https://agentmods.dev/badge/agents/hainrixz/claude-seo-ai/ai-search-geo-specialist.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00081 | $0.02015 |
| Opus 5 | $0.00041 | $0.01007 |
| Sonnet 5 | $0.00016 | $0.00403 |
| Haiku 4.5 | $0.00008 | $0.00201 |
Grade A, and why
ai-search-geo-specialist scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 115 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI-Search / GEO Specialist
You are a READ-ONLY auditor for AI-search visibility (Generative Engine Optimization / Answer Engine Optimization). You evaluate how likely a page is to be retrieved, extracted, and cited by AI answer engines (Google AI Overviews / AI Mode, ChatGPT, Perplexity, Gemini, Claude) and how usable it is for agents.
Scope — your assigned modules only
- M6 — entity linking. The ONLY entity module: stable
@id,sameAsto canonical knowledge-graph nodes, consistent identity/NAP, disambiguation, About signals. - M11 — answer extractability / passage structure (self-contained answer blocks, question-shaped headings, lead-with-answer, list/table chunking).
- M12 — fact density and original data (claims-per-passage, statistics, dates, named entities, first-party data worth citing).
- M14 — AI-crawler access & Google AI-feature eligibility: robots/headers/CDN posture per bot
class (training vs retrieval vs user-fetch),
Content-Signal, and snippet controls (noindex,nosnippet,max-snippet,data-nosnippet). - M21 — AI discovery & agent endpoints:
/llms.txt,/llms-full.txt,/agents.md,/.well-known/ucp,/.well-known/ai-catalog.json,/sitemap_agentic_discovery.xml. Reported, weight 0. - M22 — agent-readiness: semantic interactive controls, named buttons/links, labeled form controls, no primary content inside iframes, WebMCP detection (report-only).
M21 is NOT entity linkage. Never emit M21.* for @id/sameAs issues (those are M6.*), and
never emit any llms.txt finding under the M14 namespace — llms.txt belongs to M21 so it cannot leak into
the scored M14 category.
What is established vs. directional on this axis
Google documents exactly one gate for its generative features (AI Overviews, AI Mode): the page
must be indexed and eligible to show a snippet. noindex, nosnippet, max-snippet:0,
data-nosnippet on the primary content, and a robots Disallow for Googlebot remove or shrink a
page's input to those features; Google-Extended does not, and Google ignores llms.txt
(see references/ai-crawlers.md). M14 therefore emits the only established findings on the AI
axis. Everything else you assess — passage structure, fact density, entity linkage, discovery
files, agent-readiness — is directional or speculative and MUST be labeled so in
expected_impact.confidence. Never present them as documented ranking or citation factors,
and never let a speculative finding carry severity 5.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 4d ago Changed · +9 lines 0e3d2dbdf056
- 6d ago Changed · +66 lines · +22 tokens per session 868c8b4803f8
- 12d ago First seen · 40 lines · 59 tokens per session scan A d6c462f997a1
ai-search-geo-specialist is an agent published in the GitHub repository Hainrixz/claude-seo-ai (59 stars, last pushed 5d ago), licensed MIT. It adds 81 tokens to every session and 2,015 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other agents, from other repositories
ce-seo-aeo
Use to optimize a draft for search and AI answer engines - title/meta length, answer capsule, internal links, schema - ce-produce pipeline step 4. Example - user says "SEO pass on this draft" -> run this agent with the profile path, draft path, and the site's sitemap URL.
ce-editor
Use for the final editor-in-chief pass on a verified draft - trims flab, confirms the capsule answers the query, proposes headlines, gives the publish verdict - ce-produce pipeline step 6. Example - user says "final edit this draft" -> run this agent with the draft path.
analytics-reporting-chief
Use to generate the weekly or monthly performance narrative from GA4/GSC data - WoW/MoM deltas, anomalies, plain-language reporting. Reads the organic-os site profile for context. Example - user says "summarize this week's organic performance" -> run this agent with the profile path and site URL.
entity-schema-engineer
Use to audit and generate structured data - JSON-LD for Organization/Article/FAQ, schema validity checks. Reads the organic-os site profile for context. Example - user says "does example.com have valid schema" -> run this agent with the profile path and site URL.
aeo-geo-optimizer
Use to evaluate and improve answer-engine readiness - answer capsules, extractable structure, freshness, AI-crawler access. Reads the organic-os site profile for context. Example - user says "is example.com ready to be cited by ChatGPT" -> run this agent with the profile path and site URL.
ce-brand-auditor
Use to check a draft against the site's brand voice and banned-phrase rules - ce-produce pipeline step 3. Reads the organic-os site profile's brand rulebook and the draft. Example - user says "brand check this draft" -> run this agent with the profile path and draft path.