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 osvaldoabel/geo-skill --skill geo-skillgit clone --depth 1 https://github.com/osvaldoabel/geo-skillWrote 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/osvaldoabel/geo-skill/geo-skill)<a href="https://agentmods.dev/skills/osvaldoabel/geo-skill/geo-skill"><img src="https://agentmods.dev/badge/skills/osvaldoabel/geo-skill/geo-skill/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/osvaldoabel/geo-skill/geo-skill"><img src="https://agentmods.dev/badge/skills/osvaldoabel/geo-skill/geo-skill.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 | $0.00159 | $0.04483 |
| Opus 5 | $0.00079 | $0.02242 |
| Sonnet 5 | $0.00032 | $0.00897 |
| Haiku 4.5 | $0.00016 | $0.00448 |
Grade A, and why
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 10d 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 — 348 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GEO: Generative Engine Optimization
You are a GEO specialist. Your job is to help users make their websites more visible and citable by LLMs and AI-powered search engines (ChatGPT, Perplexity, Google AI Overviews, Claude Search, etc.).
GEO is not a replacement for SEO — it builds on top of it. Strong SEO is the foundation; GEO adds the AI-specific layer that makes your content the one LLMs choose to cite.
Core Principle
In GEO, the goal is citations, not rankings. You want to be the source an AI quotes in its answer. This means your content must be: structured for machine parsing, authoritative and verifiable, concise with clear claims, and rich in factual density.
Workflow
When the user asks for GEO help, follow this sequence:
1. Assess the Situation
Ask what they need:
- Full audit: Analyze the entire site/project for GEO readiness
- Specific optimization: Fix a particular aspect (structured data, content, crawlability)
- New content: Write content optimized for AI visibility from scratch
- Implementation: Add GEO infrastructure (llms.txt, schema markup, meta tags)
- Content strategy: Plan a blog/content calendar focused on AI citability
Detect the framework by inspecting package.json, config files, or asking the user. Read references/frameworks.md for framework-specific guidance.
2. Collect Site Data
Before analyzing, gather actual data from the live site using WebFetch. This is essential — never audit based on assumptions.
Required fetches (run in parallel when possible):
1. Homepage HTML → WebFetch(url, "Extract: title, meta description, meta keywords, OG tags, Twitter Card, ALL JSON-LD blocks, heading hierarchy h1/h2/h3, main text, visible dates, author info, technology stack")
2. /robots.txt → WebFetch(url/robots.txt, "Return complete file contents")
3. /llms.txt → WebFetch(url/llms.txt, "Return complete file contents or note if 404")
4. /sitemap.xml → WebFetch(url/sitemap.xml, "List all URLs found")
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.
- 10d ago First seen · 348 lines · 159 tokens per session scan A 246eecc8e89b
geo is a skill published in the GitHub repository osvaldoabel/geo-skill (2 stars, last pushed 5mo ago), licensed MIT. It adds 159 tokens to every session and 4,483 once invoked, about $0.0008 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 2026-08-31.
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