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.
npx skills add amirjahfar1/automate-seo-with-claude --skill seo-geogit clone --depth 1 https://github.com/amirjahfar1/automate-seo-with-claudeWrote 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/skills/amirjahfar1/automate-seo-with-claude/seo-geo)<a href="https://agentmods.dev/skills/amirjahfar1/automate-seo-with-claude/seo-geo"><img src="https://agentmods.dev/badge/skills/amirjahfar1/automate-seo-with-claude/seo-geo/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/skills/amirjahfar1/automate-seo-with-claude/seo-geo"><img src="https://agentmods.dev/badge/skills/amirjahfar1/automate-seo-with-claude/seo-geo.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 | $0.00128 | $0.02733 |
| Opus 5 | $0.00064 | $0.01367 |
| Sonnet 5 | $0.00026 | $0.00547 |
| Haiku 4.5 | $0.00013 | $0.00273 |
Grade A, and why
seo-geo 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 12d 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.
This is a copy
88% identical to seo-geo — 22 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 164 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Example output: examples/seo-geo-notion-share-pages-20260514/GEO.md
Page-Level GEO (Generative Engine Optimization)
For one URL, surface its AI-search citation footprint and recommend the page-level changes that would improve citability across AI Overview, Perplexity, ChatGPT, and other LLM-powered search engines. Different from the domain-level brand-vs-brand share-of-voice — this is page-level diagnosis.
Prerequisites
- DataForSEO MCP server connected.
- Claude's
WebFetchtool available. - User provides: a target URL. Optional: target country (default
us), specific keywords to focus on (defaults: the URL's top-5 traffic-weighted keywords from DataForSEO).
Process
-
Validate target & preflight. See
skills/seo-firecrawl/references/preflight.mdfor the canonical 3-stage preflight (cost note, Firecrawl availability, Google APIs). Skill-specific notes:- Confirm URL is fetchable before continuing.
- DataForSEO bills per call; this run issues ~10–20 calls (URL keyword footprint, AIO presence + leaderboard for top 5 keywords). Use the documented
limit/ceilingparams to cap. - Firecrawl: optional, ~3 Firecrawl credits if available. When available, the JSON-LD parse in step 7 and the AI-protocol-files step 8 use it. Without it, those steps emit
(skipped — Firecrawl not installed; install via extensions/firecrawl/install.sh)notes inGEO.mdrather than failing the run. Pass--no-firecrawlto skip Firecrawl even when available (saves credits). - Google APIs: not used.
-
URL keyword footprint
mcp__dataforseo__dataforseo_labs_google_relevant_pagesandmcp__dataforseo__dataforseo_labs_google_ranked_keywords(withtarget= URL)- Pull URL's overview (keywords, traffic).
- Pull all keywords the URL ranks for. Sort by traffic-weighted score.
- Take the top 5 as the GEO investigation set (or use user-supplied keywords).
-
AIO presence per keyword
mcp__dataforseo__serp_organic_live_advanced(read the AIO block)- For each keyword, query the SERP and read the AI Overview block for presence + citation list.
- Flag: AIO present? Is the candidate URL cited?
- Capture the AIO answer text — it tells you what passage shape Google's models prefer.
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.
- 12d ago First seen · 164 lines · 128 tokens per session scan A 16b04d2f7b52
seo-geo is a skill published in the GitHub repository amirjahfar1/automate-seo-with-claude (2 stars, last pushed 3mo ago), licensed MIT. It adds 128 tokens to every session and 2,733 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. It is 88% identical to seo-geo, differing in 22 lines, and is treated as a copy.
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seo-geo
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Generate, validate, or audit llms.txt files for AI search visibility. Crawls site structure, generates spec-compliant Markdown index for LLMs. Use when user says "llms.txt", "llm txt", "AI crawlers", "generate llms", "LLM file", "AI readability file".