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 dalroot/agentic-seo --skill geogit clone --depth 1 https://github.com/dalroot/agentic-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/dalroot/agentic-seo/geo)<a href="https://agentmods.dev/skills/dalroot/agentic-seo/geo"><img src="https://agentmods.dev/badge/skills/dalroot/agentic-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/dalroot/agentic-seo/geo"><img src="https://agentmods.dev/badge/skills/dalroot/agentic-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 | $0.00177 | $0.02739 |
| Opus 5.5 | $0.00071 | $0.01096 |
| Sonnet 5.5 | $0.00035 | $0.00548 |
| Haiku 4.5 | $0.00018 | $0.00274 |
Grade A, and why
generative-engine-optimization 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 today.
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 — 151 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Strategies: GEO (Generative Engine Optimization)
Guides GEO/AEO strategy for AI search visibility. GEO optimizes content for ChatGPT, Claude, Perplexity, and AI search summaries (Google AI Overviews, Bing Copilot, Yandex Search with AI)—getting cited in AI-generated answers rather than ranking in traditional SERPs. See serp-features for AI search as SERP features; featured-snippet for snippet optimization that overlaps with AI Overviews.
When invoking: On first use, if helpful, open with 1-2 sentences on what this skill covers and why it matters, then provide the main output. On subsequent use or when the user asks to skip, go directly to the main output.
Scope
- GEO = Generative Engine Optimization
- AEO = Answer Engine Optimization
- LLMO = Large Language Model Optimization
- AIO = Artificial Intelligence Optimization
All refer to the same goal: visibility in AI assistant responses.
GEO vs. SEO
| Dimension | SEO | GEO |
|---|---|---|
| Goal | Rankings in search results | Citations in AI answers |
| User path | Click → visit → convert | Answer in-place; may not visit |
| Content | Full page optimization | Clear, citable paragraphs |
| Metrics | Clicks, traffic | Citations, brand mentions |
| Platforms | Google, Bing, Yandex (organic) | AI Overviews, Copilot, Yandex AI, ChatGPT, Perplexity |
Both matter: Create content that ranks and gets cited. AI search summaries (AI Overviews, Copilot, Yandex AI) are SERP features—see serp-features. When SERP features cause zero-click (user gets answer without clicking), citation becomes the primary value; optimize for being cited, not just ranked.
AI Search Platforms (SERP Features + Standalone)
| Platform | Type | Source Selection | Optimization Focus |
|---|---|---|---|
| Google AI Overviews | SERP feature | Top 10–12 organic; Gemini; favors older domains (49% over 15 yrs) | Traditional SEO; structured data; citable blocks |
| Bing Copilot Search | SERP feature | Bing index; GPT-4; 9.81% domain overlap with Google; favors younger domains (18.85%); LinkedIn signals for B2B | Bing optimization; LinkedIn presence; structured content |
| Yandex Search with AI / Neuro | SERP feature | Real-time Yandex search; YandexGPT; Russia-focused | Yandex indexing; Russian content; cited sources |
| Perplexity | Standalone | 200B+ URL index; independent crawl; favors recency, semantic alignment | Content freshness; semantic markup; mid-tier site opportunity |
| ChatGPT (web search) | Standalone | GPTbot; high-authority, frequently updated, LLM-friendly; favors older domains (45.8%) | Backlinks; structured data; authority signals |
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.
- today First seen · 151 lines · 177 tokens per session scan A 2ec798a15761
generative-engine-optimization is a skill published in the GitHub repository dalroot/agentic-seo (25 stars, last pushed 6d ago), licensed MIT. It adds 177 tokens to every session and 2,739 once invoked, about $0.0007 per session on Opus 5.5. 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 2026-10-26.
Other skills, from other repositories
seo
Deterministic LLM-first SEO audits for websites, blog posts, and GitHub repositories. Use this when the user asks to "perform SEO analysis", "run SEO audit", "analyze SEO", "check technical SEO", "review schema", "Core Web Vitals", "E-E-A-T", "hreflang", "GEO", "AEO", or GitHub repository SEO optimization. For…
github
When the user wants to use GitHub for SEO, parasite SEO, GEO, open source marketing, README optimization, or curated Awesome lists. Also use when the user mentions "GitHub," "GitHub SEO," "GitHub parasite SEO," "GitHub GEO," "awesome list," "GitHub README," "profile README," "pinned repositories," "Trending,"…
schema-markup
When the user wants to add or optimize structured data (Schema.org, JSON-LD). Also use when the user mentions "schema," "structured data," "JSON-LD," "rich results," "rich snippets," "Google rich snippets," "featured snippet schema," "add schema to page," "missing structured data," "schema validation error," "Schema…
grokipedia-recommendations
When the user wants to add recommendations, links, or content to Grokipedia. Also use when the user mentions "Grokipedia," "GEO wiki," "AI encyclopedia," "suggest Grokipedia article," "Grokipedia citation," "Grokipedia listing," "AI search citation," "GEO visibility," or "Grokipedia optimization." For multi-platform…
google-search-console
When the user wants to analyze Google Search Console data, use the GSC API, or interpret search performance. Also use when the user mentions "GSC," "Search Console," "indexing report," "Core Web Vitals," "Enhancements," "Insights report," "search performance," "search queries," "search performance report," "URL…
brand-visual-generator
When the user wants to define, audit, or apply visual identity (typography, colors, spacing, design tokens, frontend aesthetics). Also use when the user mentions "brand style guide," "visual identity," "design system," "typography," "color palette," "brand guidelines," "AI brand aesthetics," "brand colors," "font…