What are the five layers of AI search visibility?

Extractable content structure (making facts and answers easy for AI to pull), citation worthiness (the signals that make AI consider a source authoritative), structured-data depth (schema that speaks machine-readable language), AI-readable accessibility (llms.txt, crawler decisions, clean semantics), and real-world entity signals (the consistent identity AI cross-references across the open web). Each layer compounds on the one below it, and the first three are where most of the practical wins are.

How do I make content extractable by AI?

Open each major section with a definitive one-to-three-sentence answer to the question it addresses, because AI extracts the first answer it sees. Phrase H2s and H3s as questions when natural (it mirrors how people prompt AI), state each fact once with its supporting context right beside it rather than spread across paragraphs, define concepts inline rather than assuming a definition three pages back, and use tables, lists, and numbered steps, which AI parses reliably for comparisons, specs, and procedures.

A markdown file at the site root (/llms.txt) describing the site's content, key URLs, and the topics it covers, aimed at AI crawlers. It sits in the AI-readable-accessibility layer alongside an explicit decision about which AI crawlers to allow (GPTBot, ClaudeBot, Google-Extended, PerplexityBot, and others), clean semantic HTML, and keeping critical content out of client-side-only rendering. It is one signal among many and helps, but treating it as a magic bullet is a failure mode; it does not substitute for the content and authority underneath.

Why does AEO require fundamental SEO?

Because AI cites pages, not magic. Strong AEO and GEO stack on strong on-page, technical, and off-page SEO, and a site that cannot be crawled, has thin content, or carries no authority will not be cited however good its structure or llms.txt. Treating AEO and GEO as separate from SEO is the most common failure: the AI layer compounds the fundamentals rather than replacing them.

How often should I re-audit for AI search?

At least quarterly. AI products evolve faster than search algorithms historically did, so optimizing once and walking away leaves visibility to drift. List the 10 to 20 priority questions the site should be cited for, query the major AI products relevant to the audience with each, note which sources they cite and why the site loses to them, then re-test those queries every quarter as the products and their citation logic change.

Skill · AI search optimization

AI search optimization.

Make content discoverable, extractable, and citable by AI search.

Answer Engine Optimization and Generative Engine Optimization: make content discoverable, extractable, and citable by AI overviews, answer engines, and assistants. AI search visibility comes from five stacked layers, each compounding: extractable structure, citation worthiness, structured-data depth, AI-readable accessibility, and real-world entity signals.

The skill encodes principles, because AI products evolve faster than the principles do. It stacks on top of fundamental SEO rather than replacing it: AI cites pages, not magic.

Audience: SEO and content teams optimizing for AI overviews and answer engines, building llms.txt, or worried about staying visible as search shifts from blue links to AI answers.

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The framework

Five layers, each compounding.

AI search visibility comes from five stacked layers. Each compounds on the one below it.

01Extractable content structure: open sections with a definitive 1-to-3 sentence answer, phrase headers as questions, state facts atomically with their context, and use tables, lists, and numbered steps that parse reliably.
02Citation worthiness: original data, specific numbers with sources, named experts with credentials, visible publication and last-updated dates, methodology disclosure, and citations of the sources you used.
03Structured data depth: Schema.org types with required and recommended properties filled, Person schema with sameAs links, Organization on the homepage, and FAQPage and HowTo where genuine.
04AI-readable accessibility: an llms.txt at the root, an explicit decision on which AI crawlers to allow, clean semantic HTML, and critical content kept out of client-side-only rendering.
05Real-world entity signals: consistent identity across sources, Wikidata where genuinely notable, social profiles linked via sameAs, and reviews and reputation signals AI cross-references across the open web.

The discipline

AI cites pages, not magic.

AEO and GEO stack on top of fundamental SEO; they do not replace it. AI cites pages, not magic, so a site with weak crawlability, thin content, or no authority will not be cited however clean its llms.txt. Treat this as a layer that compounds strong on-page, technical, and off-page work, not as a substitute for any of it.

Make extraction easy, because AI systems pull the first definitive answer they see. Open major sections with a one-to-three-sentence answer to the question the section addresses, phrase headers as questions where natural, state each fact once with its supporting context beside it rather than spread across paragraphs, and lean on tables, lists, and numbered steps that parse cleanly.

Entity signals are what get a brand cited for branded and expertise queries. Content alone, with no real-world entity strength (consistent identity across authoritative sources, schema-marked authors with verifiable profiles, reviews and reputation), will not be cited reliably. And AI products update faster than search algorithms historically did, so re-test the 10 to 20 priority queries quarterly rather than optimizing once.

Reference files

Two references that go alongside the SKILL.md.

references/llms-txt-guide.md
How to write a useful llms.txt, with examples.
references/extraction-friendly-patterns.md
Content patterns that AI extracts cleanly, with before-and-after examples.

Browse all reference files on GitHub

Bridges to other skills

The fundamentals AEO stacks on.

Strong AI search visibility requires strong SEO underneath. These are the layers it builds on, plus the scaled-content lane it applies to.

The page itself
seo-onpage
On-page schema and content structure overlap directly with the extraction and structured-data layers here. Strong on-page work is part of what makes a page citable.
Crawlable and rendered
seo-technical
AI crawlers render less reliably than Googlebot, so the technical layer's rendering and crawlability work decides whether AI can read the content at all.
Authority and entity
seo-offpage
Brand mentions and authority across the open web feed the entity signals AI cross-references. Off-page work builds the reputation citation worthiness depends on.
The priority queries
seo-keyword
Naming the questions AI should cite the site for is research work. Keyword research supplies the query space; this skill structures content to be cited for it.
At scale
programmatic-seo
Applies the same extraction-friendly structure and schema across programmatically generated pages, so a large set earns AI citations on factual queries.

Open source under MIT

Read the SKILL.md on GitHub.

The skill source lives in the rampstackco/claude-skills repository alongside dozens of other skills covering the full lifecycle of brand and product work. This page is a structured overview; the SKILL.md is the source. MIT licensed.

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Frequently asked questions.

What are the five layers of AI search visibility?: Extractable content structure (making facts and answers easy for AI to pull), citation worthiness (the signals that make AI consider a source authoritative), structured-data depth (schema that speaks machine-readable language), AI-readable accessibility (llms.txt, crawler decisions, clean semantics), and real-world entity signals (the consistent identity AI cross-references across the open web). Each layer compounds on the one below it, and the first three are where most of the practical wins are.
How do I make content extractable by AI?: Open each major section with a definitive one-to-three-sentence answer to the question it addresses, because AI extracts the first answer it sees. Phrase H2s and H3s as questions when natural (it mirrors how people prompt AI), state each fact once with its supporting context right beside it rather than spread across paragraphs, define concepts inline rather than assuming a definition three pages back, and use tables, lists, and numbered steps, which AI parses reliably for comparisons, specs, and procedures.
What is llms.txt?: A markdown file at the site root (/llms.txt) describing the site's content, key URLs, and the topics it covers, aimed at AI crawlers. It sits in the AI-readable-accessibility layer alongside an explicit decision about which AI crawlers to allow (GPTBot, ClaudeBot, Google-Extended, PerplexityBot, and others), clean semantic HTML, and keeping critical content out of client-side-only rendering. It is one signal among many and helps, but treating it as a magic bullet is a failure mode; it does not substitute for the content and authority underneath.
Why does AEO require fundamental SEO?: Because AI cites pages, not magic. Strong AEO and GEO stack on strong on-page, technical, and off-page SEO, and a site that cannot be crawled, has thin content, or carries no authority will not be cited however good its structure or llms.txt. Treating AEO and GEO as separate from SEO is the most common failure: the AI layer compounds the fundamentals rather than replacing them.
How often should I re-audit for AI search?: At least quarterly. AI products evolve faster than search algorithms historically did, so optimizing once and walking away leaves visibility to drift. List the 10 to 20 priority questions the site should be cited for, query the major AI products relevant to the audience with each, note which sources they cite and why the site loses to them, then re-test those queries every quarter as the products and their citation logic change.