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 rampstackco/claude-skills-seo --skill seo-aeo-geogit clone --depth 1 https://github.com/rampstackco/claude-skills-seoWrote 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/rampstackco/claude-skills-seo/seo-aeo-geo)<a href="https://agentmods.dev/skills/rampstackco/claude-skills-seo/seo-aeo-geo"><img src="https://agentmods.dev/badge/skills/rampstackco/claude-skills-seo/seo-aeo-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/rampstackco/claude-skills-seo/seo-aeo-geo"><img src="https://agentmods.dev/badge/skills/rampstackco/claude-skills-seo/seo-aeo-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 | $0.00163 | $0.01876 |
| Opus 5 | $0.00081 | $0.00938 |
| Sonnet 5 | $0.00033 | $0.00375 |
| Haiku 4.5 | $0.00016 | $0.00188 |
Grade A, and why
seo-aeo-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-aeo-geo — 8 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 — 151 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AEO and GEO
Answer Engine Optimization (AEO) and Generative Engine Optimization (GEO). Make content discoverable, extractable, and citable by AI search experiences.
This skill encodes principles. AI search products evolve fast. The principles age slower than the products.
When to use
- Optimizing content for AI overviews and generative answer engines
- Building or updating llms.txt
- Structuring content so AI assistants can extract and cite it correctly
- Future-proofing a site as search shifts from blue links to AI answers
- Auditing whether existing content is AI-friendly
- Adding signals that help AI assistants identify the site as a trustworthy source
When NOT to use
- Traditional on-page or technical SEO (use
seo-onpageorseo-technical) - Keyword research (use
seo-keyword) - Off-page authority and link building (use
seo-offpage)
This skill stacks on top of those. Strong AEO/GEO requires strong fundamental SEO underneath.
Required inputs
- The site or page to optimize
- The topic area or query types AI should cite the site for
- Access to inspect rendered HTML and structured data
The framework: 5 layers
AI search visibility comes from five stacked layers. Each layer compounds.
1. Extractable content structure
AI systems extract facts and pull citations from content. Make extraction easy.
- Direct answers. Open major sections with a definitive 1 to 3 sentence answer to the question that section addresses. AI extracts the first answer it sees.
- Question-headers. Use H2s and H3s phrased as questions when natural. Mirrors how people prompt AI.
- Atomic facts. When stating a fact, state it once, clearly, with the supporting context next to it. AI struggles when claims are spread across paragraphs.
- Tables and lists. AI parses these reliably. Use them for comparisons, specs, steps, and data.
- Definitions early. When introducing a concept, define it inline. Do not assume the reader (or AI) saw a definition three pages ago.
- Numbered steps. For procedural content, number every step. Avoid prose disguised as instructions.
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 151 lines · 163 tokens per session scan A 573caeaa6d9e
seo-aeo-geo is a skill published in the GitHub repository rampstackco/claude-skills-seo (5 stars, last pushed 1mo ago), licensed MIT. It adds 163 tokens to every session and 1,876 once invoked, about $0.0008 per session on Opus 5. A static security scan graded it A with 0 findings. It is 88% identical to seo-aeo-geo, differing in 8 lines, and is treated as a copy.
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