/geo-visibility

name: geo-visibility
description: "Get cited and recommended by AI engines (ChatGPT, AI Overviews and AI Mode, Perplexity, Claude, Gemini). Input: a page or piece of content. Output: passage-level citability fixes (answer-first H2 blocks, self-contained chunks, definitions, sourced stats, comparison tables), a 5-pillar GEO score (0-100), an AI-crawler visibility check, and entity plus third-party consistency. Repo reference for citability. Use for GEO, AEO, LLM SEO, AI citations, citability, llms.txt, entity SEO, or why competitors appear in AI answers and you do not."
license: MIT
metadata:
  author: "Sorank (https://sorank.com)"
  version: "1.0.0"

GEO Visibility: Getting Cited by AI Engines

Generative Engine Optimization (GEO) is the work of becoming a source AI engines retrieve, quote, and recommend. This skill is the canonical reference in this repo for passage-level citability rules: when another skill needs "how to write so AI engines cite it", the rules live here.

One framing governs everything below: AI engines do not rank 1000 pages, they sample a short list of sources per subquery and quote passages from them. That changes the job from "rank the page" to "make every passage quotable and make the brand legible everywhere models look". GEO sits on top of SEO, never instead of it: a page that is not indexed cannot be retrieved, and a page that is not retrieved cannot be cited.

Company knowledge first (Obsidian)

If the working environment contains an Obsidian vault or any local knowledge base (a folder of .md notes, often with a .obsidian directory), read the relevant notes before acting: brand and product facts, target keywords, competitors, and the SEO action log of what was already tried. Ground every recommendation in that context instead of asking the user for facts the vault already holds. At the end of the session, append the actions taken to the vault's SEO action log so the next session starts informed. Vault structure, read-first and write-back protocols: the obsidian-brain skill.

Definitions

• GEO (Generative Engine Optimization): the practice of earning citations and recommendations inside AI-generated answers.
• AEO (Answer Engine Optimization): an earlier name for the same goal; treat the terms as interchangeable.
• Citability: how easily one passage can be lifted out of a page and quoted as a standalone, correct answer.
• Query fan-out: the engine-side decomposition of one prompt into multiple subqueries, each retrieved separately.
• Entity: the machine-readable identity of a brand (name, category, key facts) that models learn from co-occurrence across the web.

When to use this skill

Use this skill when the user:

• Asks how to appear in ChatGPT answers, AI Overviews, AI Mode, Perplexity, Claude, or Gemini.
• Asks why a competitor is recommended by AI assistants and they are not.
• Mentions GEO, AEO, AIO, LLM SEO, answer engine optimization, or AI citations.
• Wants a page or blog post optimized, scored, or audited for AI citability.
• Asks about llms.txt, blocking AI crawlers, or any rumored GEO tactic (the myths section answers these honestly).
• Wants brand entity work: consistent descriptions, schema, third-party profiles.

Boundaries: build target prompt lists with seo-keyword-research first; measure results with geo-tracking after. This skill is the middle step, improving citation rates.

How each AI engine selects sources

Treat each engine as a distinct channel with its own retrieval pipeline. Only 11% of cited domains are cited by both ChatGPT and Perplexity in a 2026 per-engine audit (https://authoritytech.io/curated/ai-citation-11-percent-platform-overlap-per-engine-audit-2026): winning one engine does not transfer automatically.

| Engine | Index and retrieval | What decides a citation | Key measured facts | |---|---|---|---| | ChatGPT search | Own index crawled by OAI-SearchBot, with Bing as a complementary gateway | Fans the prompt out into subqueries, retrieves a short list, cites roughly half of the URLs it fetches | Title and content similarity to the subquery is the top citation predictor across 1.4M prompts (https://ahrefs.com/blog/why-chatgpt-cites-pages/); OpenAI roughly tripled its crawl volume since August 2025 (https://www.botify.com/blog/openai-tripled-web-crawl) | | Google AI Overviews and AI Mode | Standard Googlebot index | Official query fan-out, answers assembled passage by passage from multiple sources | Only 32% of AI Mode cited URLs overlap the organic top 10 (51% at domain level; links shown below responses track organic much more closely) (https://www.semrush.com/blog/ai-mode-comparison-study/) | | Perplexity | Own index (PerplexityBot) plus real-time fetching | Retrieval-heavy, favors fresh sources and user-generated content | Overweights YouTube, Reddit, and review content, and cites Wikipedia far less than ChatGPT does (0.6% vs 7.8% of citations, https://www.tryprofound.com/blog/ai-platform-citation-patterns) | | Claude | Brave Search as web search backend plus Anthropic's own fetchers | Search-grounded answers from Brave results | Brave powers Claude web search (https://techcrunch.com/2025/03/21/anthropic-appears-to-be-using-brave-to-power-web-searches-for-its-claude-chatbot/) | | Gemini | Google Search grounding | Same index and fan-out family as AI Overviews | Optimize through the same Googlebot index and passage rules |

Read the table offensively:

• The 32% overlap number is the central GEO opportunity. A page ranking 15th (or a page with no ranking head term at all) gets cited when it answers one fan-out subquery better than anything in the top 10. Passage relevance beats page rank. This is why niche, precisely-titled pages punch above their domain authority in AI answers.
• ChatGPT citing about half of what it fetches means retrieval is necessary but not sufficient: the passage must then be the easiest one to quote. The citability rules below exist for that second step.
• Perplexity's UGC bi