What it does. When the user wants to optimize content for AI search engines, get cited by LLMs, or appear in AI-generated answers. Also use when the user mentions 'AI SEO,' 'AEO,' 'GEO,' 'LLMO,' 'answer engine optimization,' 'generative engine optimization,' 'LLM optimization,' 'AI Overviews,' 'optimize for ChatGPT,' 'optimize for Perplexity,' 'AI citations,' 'AI visibility,' 'zero-click search,' 'how do I show up in AI answers,' 'LLM mentions,' 'optimize for Claude/Gemini,' 'llms.txt,' 'llms-full.txt,' 'OKF,' 'Open Knowledge Format,' 'knowledge bundle,' 'agent-readable site,' 'agent readiness,' 'is my site agent-ready,' 'WebMCP,' 'do listicles still work for AI,' 'ChatGPT stopped citing comparison pages,' or 'AI citation format shift.' Use this whenever someone wants their content to be cited or surfaced by AI assistants and AI search engines. For traditional technical and on-page SEO audits, see seo-audit. For structured data implementation, see schema. Part of coreyhaines31/marketingskills (marketing skills for agents) (coreyhaines31/marketingskills).

Upstream	coreyhaines31/marketingskills
Skill file	skills/ai-seo/SKILL.md
License	MIT
Author	Corey Haines
Fetched	2026-09-10

Install

• npx skills add coreyhaines31/marketingskills --skill ai-seo, or copy the skill folder into ~/.claude/skills/ai-seo/.
• Raw file: curl -sL https://raw.githubusercontent.com/coreyhaines31/marketingskills/HEAD/skills/ai-seo/SKILL.md

SKILL.md (verbatim)

name: ai-seo
description: "When the user wants to optimize content for AI search engines, get cited by LLMs, or appear in AI-generated answers. Also use when the user mentions 'AI SEO,' 'AEO,' 'GEO,' 'LLMO,' 'answer engine optimization,' 'generative engine optimization,' 'LLM optimization,' 'AI Overviews,' 'optimize for ChatGPT,' 'optimize for Perplexity,' 'AI citations,' 'AI visibility,' 'zero-click search,' 'how do I show up in AI answers,' 'LLM mentions,' 'optimize for Claude/Gemini,' 'llms.txt,' 'llms-full.txt,' 'OKF,' 'Open Knowledge Format,' 'knowledge bundle,' 'agent-readable site,' 'agent readiness,' 'is my site agent-ready,' 'WebMCP,' 'do listicles still work for AI,' 'ChatGPT stopped citing comparison pages,' or 'AI citation format shift.' Use this whenever someone wants their content to be cited or surfaced by AI assistants and AI search engines. For traditional technical and on-page SEO audits, see seo-audit. For structured data implementation, see schema."
metadata:
  version: 2.5.0

AI SEO

You are an expert in AI search optimization — the practice of making content discoverable, extractable, and citable by AI systems including Google AI Overviews, ChatGPT, Perplexity, Claude, Gemini, and Copilot. Your goal is to help users get their content cited as a source in AI-generated answers.

Before Starting

Check for product marketing context first: If .agents/product-marketing.md exists (or .claude/product-marketing.md, or the legacy product-marketing-context.md filename, in older setups), read it before asking questions. Use that context and only ask for information not already covered or specific to this task.

Gather this context (ask if not provided):

1. Current AI Visibility

• Do you know if your brand appears in AI-generated answers today?
• Have you checked ChatGPT, Perplexity, or Google AI Overviews for your key queries?
• What queries matter most to your business?

2. Content & Domain

• What type of content do you produce? (Blog, docs, comparisons, product pages)
• What's your domain authority / traditional SEO strength?
• Do you have existing structured data (schema markup)?

3. Goals

• Get cited as a source in AI answers?
• Appear in Google AI Overviews for specific queries?
• Compete with specific brands already getting cited?
• Optimize existing content or create new AI-optimized content?

4. Competitive Landscape

• Who are your top competitors in AI search results?
• Are they being cited where you're not?

---

How AI Search Works

The AI Search Landscape

Platform	How It Works	Source Selection
Google AI Overviews	Summarizes top-ranking pages	Strong correlation with traditional rankings
ChatGPT (with search)	Searches web, cites sources	Draws from wider range, not just top-ranked
Perplexity	Always cites sources with links	Favors authoritative, recent, well-structured content
Gemini	Google's AI assistant	Pulls from Google index + Knowledge Graph
Copilot	Bing-powered AI search	Bing index + authoritative sources
Claude	Brave Search (when enabled)	Training data + Brave search results

For a deep dive on how each platform selects sources and what to optimize per platform, see references/platform-ranking-factors.md.

Key Difference from Traditional SEO

Traditional SEO gets you ranked. AI SEO gets you cited.

In traditional search, you need to rank on page 1. In AI search, a well-structured page can get cited even if it ranks on page 2 or 3 — AI systems select sources based on content quality, structure, and relevance, not just rank position.

Critical stats:

• AI Overviews appear in ~45% of Google searches
• AI Overviews reduce clicks to websites by up to 58%
• Brands are 6.5x more likely to be cited via third-party sources than their own domains
• Optimized content gets cited 3x more often than non-optimized
• Statistics and citations boost visibility by 40%+ across queries

Google's Official Stance vs. Multi-Platform Reality

This is important to read once before doing anything else.

Google's position (AI features optimization guide):

"The best practices for SEO continue to be relevant because our generative AI features on Google Search are rooted in our core Search ranking and quality systems."

Google explicitly says:

• No special markup or files are required for AI Overviews or AI Mode
• Don't chunk content for AI — write for people, organize with normal headings and paragraphs
• Don't write separate content for AI — that risks "scaled content abuse" spam policy
• Helpful, reliable, people-first content wins — same E-E-A-T standards as regular Search
• No AI-specific Search Console reporting — use standard SEO metrics

Other AI engines (ChatGPT, Claude, Perplexity, Copilot) behave differently:

• They actively reward extractable structure — passages, FAQs, comparison tables, definition blocks
• They parse llms.txt, structured pricing pages, and machine-readable files when present
• They cite third-party sources (Reddit, Wikipedia, review sites) more heavily than top-ranked pages

What this means for the work:

• The structural patterns in this skill (40–60 word answer blocks, FAQ schema, comparison tables) help non-Google AI engines materially. They also don't hurt Google — they're just normal good content organization.
• For Google AI Overviews / AI Mode specifically: optimize for people and core Search, full stop. Strong E-E-A-T, original information, semantic HTML, clean indexability.
• For ChatGPT/Claude/Perplexity: layer on the extractable structure + llms.txt + machine-readable files.

When in doubt, default to "write for people, organize for clarity" — that satisfies both camps.

Query Fan-Out (Google AI Search)

Google's AI features don't just answer the one query a user typed — they generate concurrent, related queries under the hood and retrieve results for each.

Google's own example: a user asking "how to fix lawns" triggers fan-out queries about herbicides, chemical-free removal, weed prevention, etc. The AI synthesizes across all of them.

Implications:

• Single-page-per-keyword targeting is less effective. Cover the full topical cluster so you're retrievable for the fan-out variants too.
• Long-tail intent matters less than topical authority — Google's AI systems understand synonyms and semantic equivalence.
• A page that comprehensively answers a parent topic (with sub-questions covered) will be retrieved more often than narrow per-query pages.

Action: when planning content, brainstorm the 5–10 related queries the AI is likely to fan out to and make sure your content (or your site as a whole) covers them.

ChatGPT fans out too — and you can extract its literal background queries for your niche via DevTools (method in references/format-volatility.md). Post-5.6, ChatGPT's fan-outs shifted away from "best/vs/top" modifiers toward site: and "official" searches — use the extraction to see where your category's fan-outs stand today.

---

AI Visibility Audit

Before optimizing, assess your current AI search presence.

Step 1: Check AI Answers for Your Key Queries

Test 10-20 of your most important queries across platforms:

Query	Google AI Overview	ChatGPT	Perplexity	You Cited?	Competitors Cited?
[query 1]	Yes/No	Yes/No	Yes/No	Yes/No	[who]
[query 2]	Yes/No	Yes/No	Yes/No	Yes/No	[who]

Query types to test:

• "What is [your product category]?"
• "Best [product category] for [use case]"
• "[Your brand] vs [competitor]"
• "How to [problem your product solves]"
• "[Your product category] pricing"

Step 2: Analyze Citation Patterns

When your competitors get cited and you don't, examine:

• Content structure — Is their content more extractable?
• Authority signals — Do they have more citations, stats, expert quotes?
• Freshness — Is their content more recently updated?
• Schema markup — Do they have structured data you're missing?
• Third-party presence — Are they cited via Wikipedia, Reddit, review sites?

Step 3: Content Extractability Check

For each priority page, verify:

Check	Pass/Fail
Clear definition in first paragraph?
Self-contained answer blocks (work without surrounding context)?
Statistics with sources cited?
Comparison tables for "[X] vs [Y]" queries?
FAQ section with natural-language questions?
Schema markup (FAQ, HowTo, Article, Product)?
Expert attribution (author name, credentials)?
Recently updated (within 6 months)?
Heading structure matches query patterns?
AI bots allowed in robots.txt?

Step 4: AI Bot Access Check

Verify your robots.txt allows AI crawlers. Each AI platform has its own bot, and blocking it means that platform can't cite you:

• GPTBot and ChatGPT-User — OpenAI (ChatGPT)
• PerplexityBot — Perplexity
• ClaudeBot and anthropic-ai — Anthropic (Claude)
• Google-Extended — Google Gemini and AI Overviews
• Bingbot — Microsoft Copilot (via Bing)

Check your robots.txt for Disallow rules targeting any of these. If you find them blocked, you have a business decision to make: blocking prevents AI training on your content but also prevents citation. One middle ground is blocking training-only crawlers (like CCBot from Common Crawl) while allowing the search bots listed above.

See references/platform-ranking-factors.md for the full robots.txt configuration.

---

Optimization Strategy

The Three Pillars

1. Structure (make it extractable)
2. Authority (make it citable)
3. Presence (be where AI looks)

Pillar 1: Structure — Make Content Extractable

AI systems extract passages, not pages. Every key claim should work as a standalone statement.

Content block patterns:

• Definition blocks for "What is X?" queries
• Step-by-step blocks for "How to X" queries
• Comparison tables for "X vs Y" queries
• Pros/cons blocks for evaluation queries
• FAQ blocks for common questions
• Statistic blocks with cited sources

For detailed templates for each block type, see references/content-patterns.md.

Structural rules:

• Lead every section with a direct answer (don't bury it)
• Keep key answer passages to 40-60 words (optimal for snippet extraction)
• Use H2/H3 headings that match how people phrase queries
• Tables beat prose for comparison content
• Numbered lists beat paragraphs for process content
• Each paragraph should convey one clear idea

Pillar 2: Authority — Make Content Citable

AI systems prefer sources they can trust. Build citation-worthiness.

The Princeton GEO research (KDD 2024, studied across Perplexity.ai) ranked 9 optimization methods:

Method	Visibility Boost	How to Apply
Cite sources	+40%	Add authoritative references with links
Add statistics	+37%	Include specific numbers with sources
Add quotations	+30%	Expert quotes with name and title
Authoritative tone	+25%	Write with demonstrated expertise
Improve clarity	+20%	Simplify complex concepts
Technical terms	+18%	Use domain-specific terminology
Unique vocabulary	+15%	Increase word diversity
Fluency optimization	+15-30%	Improve readability and flow
Keyword stuffing	-10%	Actively hurts AI visibility

Best combination: Fluency + Statistics = maximum boost. Low-ranking sites benefit even more — up to 115% visibility increase with citations.

Statistics and data (+37-40% citation boost)

• Include specific numbers with sources
• Cite original research, not summaries of research
• Add dates to all statistics
• Original data beats aggregated data

Expert attribution (+25-30% citation boost)

• Named authors with credentials
• Expert quotes with titles and organizations
• "According to [Source]" framing for claims
• Author bios with relevant expertise

Freshness signals

• "Last updated: [date]" prominently displayed
• Regular content refreshes (quarterly minimum for competitive topics)
• Current year references and recent statistics
• Remove or update outdated information

E-E-A-T alignment

• First-hand experience demonstrated
• Specific, detailed information (not generic)
• Transparent sourcing and methodology
• Clear author expertise for the topic

Pillar 3: Presence — Be Where AI Looks

AI systems don't just cite your website — they cite where you appear.

Third-party sources matter more than your own site:

• Wikipedia mentions (7.8% of all ChatGPT citations)
• Reddit discussions (volatile: ~1.8% of ChatGPT citations historically, but nearly wiped from ChatGPT by Aug 2026 retrieval changes — still retrieved elsewhere; see the volatility section in references/agent-readiness.md)
• Industry publications and guest posts
• LinkedIn — per LinkedIn's own AEO guide, the most-cited outlet for professional-topic searches; Articles out-cite Posts ~60/40, and a post's first words become its URL slug, so front-load the target phrase (details in references/format-volatility.md)
• Review sites (G2, Capterra, TrustRadius for B2B SaaS)
• YouTube (frequently cited by Google AI Overviews)
• Podcasts (episodes get transcribed, show notes published — both get crawled and cited)
• Quora answers

Actions:

• Ensure your Wikipedia page is accurate and current
• Participate authentically in Reddit communities — but as one surface in a portfolio, never the whole strategy (citation mixes shift overnight with retrieval updates)
• Get featured in industry roundups and comparison articles
• Maintain updated profiles on relevant review platforms
• Create YouTube content for key how-to queries — models don't watch the video, they read the text layer around it; see references/youtube-ai-citations.md for the full anatomy (transcript, captions, chapters, description, pinned comment)
• Guest on podcasts in your category (prep with the public-relations skill's podcast guest prep)
• Answer relevant Quora questions with depth

Machine-Readable Files for AI Agents

Google's stance: not required for AI Overviews or AI Mode. Their guide explicitly says you don't need new markup, AI files, or markdown to appear in generative AI search.

Why include them anyway: non-Google AI engines (ChatGPT, Claude, Perplexity) and autonomous buying agents do reward extractable structure. The files below help with those engines without harming Google.

AI agents aren't just answering questions — they're becoming buyers. When an AI agent evaluates tools on behalf of a user, it needs structured, parseable information. If your pricing is locked in a JavaScript-rendered page or a "contact sales" wall, agents will skip you and recommend competitors whose information they can actually read.

Audit this layer first: references/agent-readiness.md — the access/discovery/parseability checklist, free scoring tools (npx is-agentic, Frase's checker), Markdown content negotiation + Link headers, llms-full.txt, and the emerging agent-actionable layer (WebMCP).

Add these machine-readable files to your site root:

/pricing.md or /pricing.txt — Structured pricing data for AI agents

# Pricing — [Your Product Name]

## Free
- Price: $0/month
- Limits: 100 emails/month, 1 user
- Features: Basic templates, API access

## Pro
- Price: $29/month (billed annually) | $35/month (billed monthly)
- Limits: 10,000 emails/month, 5 users
- Features: Custom domains, analytics, priority support

## Enterprise
- Price: Custom — contact sales@example.com
- Limits: Unlimited emails, unlimited users
- Features: SSO, SLA, dedicated account manager

Why this matters now:

• AI agents increasingly compare products programmatically before a human ever visits your site
• Opaque pricing gets filtered out of AI-mediated buying journeys
• A simple markdown file is trivially parseable by any LLM — no rendering, no JavaScript, no login walls
• Same principle as robots.txt (for crawlers), llms.txt (for AI context), and AGENTS.md (for agent capabilities)

Best practices:

• Use consistent units (monthly vs. annual, per-seat vs. flat)
• Include specific limits and thresholds, not just feature names
• List what's included at each tier, not just what's different
• Keep it updated — stale pricing is worse than no file
• Link to it from your sitemap and main pricing page

/llms.txt — Context file for AI systems (see llmstxt.org)

If you don't have one yet, add an llms.txt that gives AI systems a quick overview of what your product does, who it's for, and links to key pages (including your pricing).

/okf/ — Open Knowledge Format bundle (Google-backed, v0.1)

Google introduced OKF in June 2026 — a markdown spec for representing site content as a directory of cross-linked files with YAML frontmatter, agent-readable without scraping. Built primarily for data-team catalog metadata; the site-readable-by-agents repurposing was popularized by Suganthan Mohanadasan. No confirmed AI-search ranking signal today — treat it as protocol-layer registration like early schema.org. For the full breakdown, implementation paths (free generator, WordPress plugin, by-hand), hosting guidance, and when to skip, see references/okf.md.

Schema Markup for AI

Structured data helps AI systems understand your content. Key schemas:

Content Type	Schema	Why It Helps
Articles/Blog posts	Article, BlogPosting	Author, date, topic identification
How-to content	HowTo	Step extraction for process queries
FAQs	FAQPage	Direct Q&A extraction
Products	Product	Pricing, features, reviews
Comparisons	ItemList	Structured comparison data
Reviews	Review, AggregateRating	Trust signals
Organization	Organization	Entity recognition

Content with proper schema shows 30-40% higher AI visibility on non-Google AI engines. Google's note: structured data is "not required for generative AI search" but is recommended for overall SEO strategy. For implementation, use the schema skill.

---

Agentic Experiences

Beyond AI search engines summarizing content, autonomous agents are starting to access sites directly — clicking, reading, comparing, even buying on behalf of users. Google's guide flags this as an emerging category to plan for.

How agents access your site:

• Visual rendering — they screenshot/read the page like a user would
• DOM inspection — they parse the page's HTML structure
• Accessibility tree — they rely on the same semantic information assistive tech uses (labels, roles, landmarks, headings)

What to do:

• Render meaningful content without heavy JS gymnastics — if the page is blank until 4 frameworks finish loading, agents see blank
• Semantic HTML — use <main>, <nav>, <article>, <button>, proper heading hierarchy, alt text on images
• Clean accessibility tree — every interactive element labelled; ARIA used correctly (or not at all when native HTML suffices)
• Stable selectors / predictable layouts — agents struggle with sites that re-render every interaction
• Visible pricing, specs, contact info — anything an agent would need to make a buying recommendation should be on a public, indexable page (this is where /pricing.md and similar files help)

Emerging — Universal Commerce Protocol (UCP): Google references UCP as a forthcoming protocol that will give agents standardized hooks for commerce interactions (catalog discovery, pricing, checkout). Watch for adoption; for now, the structural recommendations above are the precursor.

For ecom and local business specifically, Google highlights:

• Merchant Center feeds + Google Business Profile for product/service visibility in AI Search
• Business Agent for conversational customer engagement (where applicable)

---

Content Types That Get Cited Most

Not all content is equally citable — and the format mix is volatile. The long-standing baseline had comparison articles (~33%) and listicles (~10%) among the top citation earners, but ChatGPT 5.6 (Aug 2026) demoted the exploited formats: listicle citations fell −50.5% and comparison-page citations −32.1%, while site: and "official" retrieval surged — a shift toward primary sources and owned pages. Format strategy is now per-platform (comparisons still work on Google AIO/Gemini/Perplexity). See references/format-volatility.md for the shift data, the per-platform format table, LinkedIn's citation numbers, and the ChatGPT fan-out extraction diagnostic.

Evergreen winners across platforms: original research and data, definitive guides, and owned "official" pages — product, docs, pricing — with extractable structure.

Underperformers: generic unstructured posts, thin or gated or PDF-only content, and anything undated without author attribution.

Citation ≠ recommendation. Getting cited means your content was useful to consult; getting recommended — onto the buyer's actual shortlist — is governed by web-wide consensus (reviews, forums, analysts, press) and is largely independent of your own content. Self-promotional "best [category]" listicles can even backfire for emerging brands: in one 100-query B2B study, 69% of the AI Overview citations that self-promotional listicles earned came in answers that recommended competitors instead of the publishing brand. See references/citations-vs-recommendations.md for the visibility ladder (retrieved → cited → mentioned → recommended), stage-dependent buyer's-guide strategy, what earns recommendations, and the attribution blind spot.

---

Monitoring AI Visibility

What to Track

Metric	What It Measures	How to Check
AI Overview presence	Do AI Overviews appear for your queries?	Manual check or Semrush/Ahrefs
Brand citation rate	How often you're cited in AI answers	AI visibility tools (see below)
Share of AI voice	Your citations vs. competitors	Peec AI, Otterly, ZipTie
Citation sentiment	How AI describes your brand	Manual review + monitoring tools
Recommendation rate	Whether you're on the shortlist, not just cited (see citations-vs-recommendations.md)	Prompt tracking + mention framing
Source attribution	Which of your pages get cited	Track referral traffic from AI sources

AI Visibility Monitoring Tools

Tool	Coverage	Best For
Otterly AI	ChatGPT, Perplexity, Google AI Overviews	Share of AI voice tracking
Peec AI	ChatGPT, Gemini, Perplexity, Claude, Copilot+	Multi-platform monitoring at scale
ZipTie	Google AI Overviews, ChatGPT, Perplexity	Brand mention + sentiment tracking
LLMrefs	ChatGPT, Perplexity, AI Overviews, Gemini	SEO keyword → AI visibility mapping

DIY Monitoring (No Tools)

Monthly manual check:

1. Pick your top 20 queries
2. Run each through ChatGPT, Perplexity, and Google
3. Record: Are you cited? Who is? What page?
4. Log in a spreadsheet, track month-over-month

AI answers are non-deterministic — one run is an anecdote, not a measurement. Run each query 3–5 times per platform and track the mention rate with its sample size ("cited 3/5, n=5"), comparing rates over time rather than single runs. Full rigor checklist in references/format-volatility.md.

Search Console expectations

Google's guide is explicit: there is no AI-specific Search Console reporting. AI Overviews and AI Mode use core Search ranking, so the standard Search Console reports (Performance, Coverage, Core Web Vitals) are still what you measure with for Google. The third-party tools above are the only way to see cross-platform AI citation behavior.

---

What NOT to Do

Google's guide calls these out explicitly — they hurt across both traditional Search and AI features.

1. Write separate content "for AI". Same content should serve people and AI. Writing variants targeted at AI systems risks the scaled content abuse spam policy — Google's words.
2. Chunk pages into AI-bait fragments. Google's guide is direct: "Don't break your content into tiny pieces for AI to better understand it." Use normal paragraph + heading structure.
3. Generate at scale for ranking manipulation. AI-generated content is fine if it meets Search Essentials and spam policies. Mass-producing thin variations does not.
4. Pursue inauthentic mentions. Don't fabricate citations or bulk-spam Reddit/Wikipedia for AI visibility. Real participation only.
5. Block AI crawlers if you want citation. Blocking GPTBot, PerplexityBot, ClaudeBot, Google-Extended means those engines literally cannot cite you. Block training-only crawlers (CCBot) if you must, not the search-and-cite ones.
6. Hide your main content behind JS that doesn't render. Both core Search and AI agents need to see your content; JS-only rendering loses both audiences.
7. Skip E-E-A-T fundamentals. Author identity, first-hand experience, expertise signals, transparent sourcing — Google's guide leans heavily on these for AI features.

---

AI SEO by Content Type

For tactical guidance on SaaS product pages, blog content, comparison/alternative pages, documentation, and local/ecom (Google's emphasis on Merchant Center + Business Profile), see references/content-types.md.

---

Common Mistakes

• Ignoring AI search entirely — ~45% of Google searches now show AI Overviews, and ChatGPT/Perplexity are growing fast
• Treating AI SEO as separate from SEO — Good traditional SEO is the foundation; AI SEO adds structure and authority on top
• Writing for AI, not humans — If content reads like it was written to game an algorithm, it won't get cited or convert
• No freshness signals — Undated content loses to dated content because AI systems weight recency heavily. Show when content was last updated
• Gating all content — AI can't access gated content. Keep your most authoritative content open
• Ignoring third-party presence — You may get more AI citations from a Wikipedia mention than from your own blog
• No structured data — Schema markup gives AI systems structured context about your content
• Keyword stuffing — Unlike traditional SEO where it's just ineffective, keyword stuffing actively reduces AI visibility by 10% (Princeton GEO study)
• Hiding pricing behind "contact sales" or JS-rendered pages — AI agents evaluating your product on behalf of buyers can't parse what they can't read. Add a /pricing.md file
• Blocking AI bots — If GPTBot, PerplexityBot, or ClaudeBot are blocked in robots.txt, those platforms can't cite you
• Generic content without data — "We're the best" won't get cited. "Our customers see 3x improvement in [metric]" will
• Forgetting to monitor — You can't improve what you don't measure. Check AI visibility monthly at minimum

---

Tool Integrations

For implementation, see the tools registry.

Tool	Use For
semrush	AI Overview tracking, keyword research, content gap analysis
ahrefs	Backlink analysis, content explorer, AI Overview data
gsc	Search Console performance data, query tracking
ga4	Referral traffic from AI sources

---

Task-Specific Questions

1. What are your top 10-20 most important queries?
2. Have you checked if AI answers exist for those queries today?
3. Do you have structured data (schema markup) on your site?
4. What content types do you publish? (Blog, docs, comparisons, etc.)
5. Are competitors being cited by AI where you're not?
6. Do you have a Wikipedia page or presence on review sites?

---

Related Skills

• seo-audit: For traditional technical and on-page SEO audits
• schema: For implementing structured data that helps AI understand your content
• content-strategy: For planning what content to create
• competitors: For building comparison pages that get cited
• programmatic-seo: For building SEO pages at scale
• copywriting: For writing content that's both human-readable and AI-extractable

Other files in this skill

• evals/evals.json
• references/agent-readiness.md
• references/citations-vs-recommendations.md
• references/content-patterns.md
• references/content-types.md
• references/format-volatility.md
• references/okf.md
• references/platform-ranking-factors.md
• references/youtube-ai-citations.md

references/agent-readiness.md (verbatim)

Agent Readiness — Can an Agent Reach, Navigate, and Parse Your Site?

AI visibility work splits into two layers: what your content says (the rest of this skill) and whether an agent can get to it at all. This reference covers the second layer — the access/discovery/parseability audit — plus the emerging shift from agent-readable to agent-actionable sites.

Two free scoring tools shipped in August 2026 and turned this into a measurable discipline:

Tool	Run it	Method
Is Agentic (Vercel + Ora)	npx is-agentic yourdomain.com or is-agentic.com	100+ checks; Essential checks carry most of the score; Recommended checks activate only when evidence shows you have that surface (API, MCP server, commerce); not-applicable checks are excluded, not failed; includes an observed agent journey showing where a real agent hit friction
Frase Agent Readiness Checker	frase.io/tools/agent-readiness	Access / Discovery / Parseability triad; 80+ = agents can reliably use the site, 60–79 = solid with gaps, <60 = real access problems

Run one before and after any agent-readiness work — the score is a shareable artifact and the failed checks are your worklist. (Both are vendor tools with a product behind them; the checks are the value, not the pitch.)

The three questions

1. Access — can an agent get to the page and see real content?

• Core content in the initial HTML response. Most agents never execute JavaScript. If the content only exists after client-side rendering, it doesn't exist. This is the #1 essential check in both tools.
• No bot challenge or firewall block on the request path. Aggressive bot protection (Cloudflare challenges, WAF rules) that blocks GPTBot, PerplexityBot, ClaudeBot, etc. is self-inflicted invisibility. Audit what your CDN/WAF actually does to those user agents — many sites block them by default without anyone deciding to.
• Correct HTTP behavior: real status codes (no soft-404s), stable canonical URLs, recoverable errors.

2. Discovery — do your files tell agents what's here?

• robots.txt with an explicit AI-crawler stance — name the major AI crawlers and state your policy, rather than leaving it to be assumed (see the bot-access table in SKILL.md for the allow/block list).
• A sitemap that loads and parses cleanly.
• llms.txt at the domain root (see Machine-Readable Files in SKILL.md).
• llms-full.txt — the newer companion: your entire site content in one file, so an agent gets everything in a single request instead of crawling. Emerging, cheap to generate alongside llms.txt, and scored as bonus signal by both tools.
• robots.txt content-usage statements — an emerging convention for declaring what AI may do with your content (train / cite / summarize), so the answer comes from you instead of being assumed.

3. Parseability — once there, can the agent tell what the page is?

• Valid, substantive structured data (JSON-LD — see the schema skill).
• A Markdown representation of the page. This is the newest technique in the stack, two implementations:
  • Content negotiation: serve compact Markdown at the same canonical URL when the request asks for Accept: text/markdown, with a Vary header keeping the HTML and Markdown cache entries separate. (This is how Is Agentic serves its own reports — agents get Markdown, browsers get HTML, one URL.)
  • Link header: an HTTP Link header on the HTML page pointing to a parallel Markdown version — discoverable without guessing URLs.
• Clear document structure — one H1, headings that answer sub-questions, extractable answer blocks (the content-patterns reference).

Emerging: agent-actionable, not just agent-readable

Reading is becoming table stakes. The next race is whether an agent can act on your site — fill the form, book the meeting, start the trial. WebMCP is the emerging standard here: a page declares its forms and CTAs as callable tools with input schemas, so an agent doesn't have to reverse-engineer your UI. Early days (label: emerging, not yet a ranking/citation signal), but the direction is clear — if agents are becoming buyers, the site that exposes "start trial" as a structured action wins the agent-mediated conversion that a pretty button loses.

Practical today: make sure your highest-intent actions (signup, pricing, demo booking, contact) work without JavaScript-only flows, have labeled semantic form fields, and return machine-readable confirmation.

Citation-source volatility (why you diversify)

Third-party citation mixes are not stable — they shift overnight with model and retrieval updates, and August 2026 provided the case study: ChatGPT's query fan-out changes nearly wiped Reddit as a citation source within days (practitioner-reported by multiple AEO teams; one had been earning 24-hour citations from Reddit at 1M+ impressions/month before the change). Meanwhile the same practitioners report business-owned websites dominate Gemini citations (~60%).

What this means for strategy:

• Never concentrate AI-visibility work in one third-party surface. The Presence pillar's list (Wikipedia, Reddit, YouTube, podcasts, review sites, Quora) is a portfolio, not a menu to pick one from. A surface that's 2% of citations today can be 0% after one retrieval update — or vice versa.
• Owned-site fundamentals hedge the volatility. Platform deals and retrieval changes reshuffle third-party sources; your own agent-readable site is the one surface no platform can drop you from — and on Gemini it's already the dominant citation class.
• Treat any citation-share statistic as dated. The "Reddit = 1.8% of ChatGPT citations" class of stats (including the ones in this skill) are snapshots — check the date, and verify against your own citation monitoring (the DIY monitoring loop in SKILL.md) before betting budget on them.
• Speed is real: fresh content on retrieved surfaces can be cited within ~24 hours. AI search rewards freshness faster than classic SEO ever did.

---

Agent-readiness check taxonomy distilled from Vercel/Ora's Is Agentic (is-agentic.com) and Frase's Agent Readiness Checker (both August 2026, credited); citation-volatility events practitioner-reported (Ashni of Hype Partners (@ashnichrist) and others, August 2026) — labeled accordingly, verify against your own monitoring.

references/citations-vs-recommendations.md (verbatim)

Citations vs. Recommendations: The AI Visibility Ladder

Being cited by an AI engine and being recommended by it are two different outcomes governed by two different systems. A citation means your page was useful enough to pull information from. A recommendation means the model put your brand on the buyer's shortlist. Optimizing for the first does not automatically earn the second — and for smaller brands, conflating them leads to content strategies that can actively help competitors.

Source note: the analysis and data in this reference draw on Lily Ray's (Amsive) 2026 study of B2B "best [category] software" queries, behavioral studies by Scrunch and SimilarWeb, and commentary by John-Henry Scherck (Growth Plays).

---

The Visibility Ladder

AI visibility is a ladder, not a binary. Each rung has different selection criteria and different measurement:

Rung	What it means	What governs it	How to see it
1. Retrieved	The model read your content while building its answer, without citing it	Crawlability, parseable structure, query relevance	Mostly invisible; bot logs hint at it
2. Cited	Your page appears as a source in the answer	Content usefulness: structure, statistics, clarity, freshness	Prompt-tracking tools, AI Overview source lists
3. Mentioned	Your brand is named in the answer text	Entity recognition + how the web talks about you	Prompt-tracking tools
4. Recommended	Your product is on the shortlist the buyer actually considers	Aggregate web consensus — reviews, forums, analysts, press, video — largely independent of your own content	Prompt tracking + the framing around the mention

Rungs 1–3 are legitimate signals your content is working, and most prompt-tracking tools report them. But rung 4 is where buying behavior changes, and it's earned differently: citation is about whether your content is useful to consult; recommendation is mostly a reflection of what the broader web says about you — whether you published a guide on the topic or not.

There is also a shadow rung: recommended against. On detailed, requirements-heavy prompts, models increasingly name products a buyer should avoid for their use case, with sources. The downside of weak third-party consensus is no longer just absence from the shortlist — it can be an explicit rule-out. This makes monitoring the framing around your mentions (favorable / neutral / hedged / negative), not just counting them, part of the job.

---

The Self-Promotional Listicle Risk

The common tactic — publish a "best [category] software" guide, rank yourself #1, and let it shape both organic search and AI answers — now has a stage-dependent payoff.

The data: Lily Ray (Amsive) analyzed 100 B2B "best [category] software" queries across three dates in spring 2026. Across the dataset, self-promotional listicles earned 323 citations in AI Overviews — and in 224 of them (69% of the citations), the answer left the publishing brand out of the recommendations, pointing buyers to competitors instead.

The mechanism: the model treats your guide as a source about the category. It happily extracts the competitor names, comparisons, and evaluation criteria you compiled — then makes its recommendation from web-wide consensus, where the established players dominate. For an emerging brand, a self-promotional buyer's guide can function as a vote for your competitors: you did the research that helps the model describe them.

The split by stage:

• Established category leaders get both outcomes. Their guides earn citations and their brands get recommended — because analysts, review sites, and forum discussions already validate them. For leaders, a definitive buyer's guide is highly advantageous: it shapes how the whole category (competitors included) gets described.
• Emerging brands may win the citation and even shape the category's framing, but miss the recommendation. That's not a wasted outcome — influencing how an LLM defines the category and its evaluation criteria is real positioning work — but it is not the shortlist placement the tactic promises.

What this changes (and doesn't): genuinely useful buyer's guides still belong in a B2B content strategy at any stage. What changes is the expectation and the investment split. If you're not yet the consensus pick, weight effort toward the offsite signals that actually govern recommendations (below) rather than publishing a plethora of self-ranked listicles.

---

What Earns Recommendations

Recommendation is a consensus signal. The inputs the models weigh live mostly off your site:

Channel	Why it moves recommendations	Related skill
Review platforms (G2, Capterra, TrustRadius, app stores)	Third-party validation models treat as evidence of legitimacy	customer-research (review generation loops)
Analyst coverage (Gartner, Forrester, industry reports)	High-authority category framing; models echo analyst shortlists	public-relations
Communities and forums (Reddit, HN, Slack/Discord, niche forums)	Unprompted practitioner discussion is heavily retrieved and hard to fake	community-marketing
Earned media and PR	Independent sources repeating your positioning beyond your own site	public-relations
Video and podcasts	Increasingly retrieved; transcripts carry brand + category associations	video, social

The test to apply before investing in another self-ranked guide: if a model ignored everything on our domain, would the rest of the web still put us on the shortlist? If not, that gap is the priority. AEO discourse often stops at "are we in the answer?" — the better question is "are we credible enough to be recommended?"

The encouraging flip side: earning an AI recommendation is harder to game than a top search ranking ever was. The durable strategy is the same at every stage — be the best fit for a clear set of buyers, and give those buyers reasons to talk about you in public, where the models can retrieve it.

---

What a Recommendation Is Worth

Two behavioral studies quantified the gap between rungs:

• Scrunch (opt-in panel linking AI conversations to subsequent web behavior, compared against each user's own baseline — observational, not a controlled experiment): a genuine recommendation ("a great option is X") was associated with people searching for, visiting, and evaluating a brand about twice as often as a passing mention. For users with no recent observed engagement with the brand, a recommendation was followed within a week by +182% branded searches, +117% site visits, and +185% product views.
• SimilarWeb (thousands of real user journeys, seven days post-answer): when ChatGPT recommended a brand, it received roughly 2.5× more new visitors the following week than the competitors left off the list.

The attribution blind spot: in the SimilarWeb data, only about 9% of those post-recommendation visits arrived as visible AI referral traffic; the largest share arrived via branded search, with direct and other channels making up the rest — indistinguishable from ordinary organic visitors. AI recommendations are already sending real, engaged buyers, but standard attribution underreports the AI touch.

Measurement triad (no single signal is complete; together they give a reliable read):

1. AI prompt tracking — whether and how you're mentioned/recommended in LLM answers, even when no click ever lands (tools in SKILL.md's Monitoring section). Track the framing around mentions — recommended, neutral, hedged, or recommended-against — not just the count.
2. Self-reported attribution — a "how did you hear about us?" field catches buyers whose journey started in an AI chat but arrived via branded search or direct.
3. Sales call recordings — buyers' own language often reveals an AI conversation shaped the shortlist long before any form fill.

Also watch branded search volume as a proxy: sustained lifts without a matching campaign are increasingly AI-influence showing up under another name.

---

Applying This

• Auditing an established brand: buyer's guides and comparison content are high-leverage — publish the definitive version and shape the category's evaluation criteria.
• Auditing an emerging brand: publish the genuinely useful guides your ICP needs, but set expectations (citation and framing, not near-term recommendation) and rebalance investment toward reviews, communities, analysts, and earned media.
• Reporting: report the ladder, not a single "AI visibility" number — retrieved/cited/mentioned/recommended plus mention framing. A rising citation count with a flat recommendation rate is a specific, diagnosable gap: the web doesn't yet corroborate your content.
• Risk check: for requirements-heavy queries in your category, check whether models recommend against you, and trace the sources they cite when they do.

references/content-patterns.md (verbatim)

AEO and GEO Content Patterns

Reusable content block patterns optimized for answer engines and AI citation.

---

Contents

• Answer Engine Optimization (AEO) Patterns (Definition Block, Step-by-Step Block, Comparison Table Block, Pros and Cons Block, FAQ Block, Listicle Block)
• Generative Engine Optimization (GEO) Patterns (Statistic Citation Block, Expert Quote Block, Authoritative Claim Block, Self-Contained Answer Block, Evidence Sandwich Block)
• Domain-Specific GEO Tactics (Technology Content, Health/Medical Content, Financial Content, Legal Content, Business/Marketing Content)
• Voice Search Optimization (Question Formats for Voice, Voice-Optimized Answer Structure)

Answer Engine Optimization (AEO) Patterns

These patterns help content appear in featured snippets, AI Overviews, voice search results, and answer boxes.

Definition Block

Use for "What is [X]?" queries.

## What is [Term]?

[Term] is [concise 1-sentence definition]. [Expanded 1-2 sentence explanation with key characteristics]. [Brief context on why it matters or how it's used].

Example:

## What is Answer Engine Optimization?

Answer Engine Optimization (AEO) is the practice of structuring content so AI-powered systems can easily extract and present it as direct answers to user queries. Unlike traditional SEO that focuses on ranking in search results, AEO optimizes for featured snippets, AI Overviews, and voice assistant responses. This approach has become essential as over 60% of Google searches now end without a click.

Step-by-Step Block

Use for "How to [X]" queries. Optimal for list snippets.

## How to [Action/Goal]

[1-sentence overview of the process]

1. **[Step Name]**: [Clear action description in 1-2 sentences]
2. **[Step Name]**: [Clear action description in 1-2 sentences]
3. **[Step Name]**: [Clear action description in 1-2 sentences]
4. **[Step Name]**: [Clear action description in 1-2 sentences]
5. **[Step Name]**: [Clear action description in 1-2 sentences]

[Optional: Brief note on expected outcome or time estimate]

Example:

## How to Optimize Content for Featured Snippets

Earning featured snippets requires strategic formatting and direct answers to search queries.

1. **Identify snippet opportunities**: Use tools like Semrush or Ahrefs to find keywords where competitors have snippets you could capture.
2. **Match the snippet format**: Analyze whether the current snippet is a paragraph, list, or table, and format your content accordingly.
3. **Answer the question directly**: Provide a clear, concise answer (40-60 words for paragraph snippets) immediately after the question heading.
4. **Add supporting context**: Expand on your answer with examples, data, and expert insights in the following paragraphs.
5. **Use proper heading structure**: Place your target question as an H2 or H3, with the answer immediately following.

Most featured snippets appear within 2-4 weeks of publishing well-optimized content.

Comparison Table Block

Use for "[X] vs [Y]" queries. Optimal for table snippets.

## [Option A] vs [Option B]: [Brief Descriptor]

| Feature | [Option A] | [Option B] |
|---------|------------|------------|
| [Criteria 1] | [Value/Description] | [Value/Description] |
| [Criteria 2] | [Value/Description] | [Value/Description] |
| [Criteria 3] | [Value/Description] | [Value/Description] |
| [Criteria 4] | [Value/Description] | [Value/Description] |
| Best For | [Use case] | [Use case] |

**Bottom line**: [1-2 sentence recommendation based on different needs]

Pros and Cons Block

Use for evaluation queries: "Is [X] worth it?", "Should I [X]?"

## Advantages and Disadvantages of [Topic]

[1-sentence overview of the evaluation context]

### Pros

- **[Benefit category]**: [Specific explanation]
- **[Benefit category]**: [Specific explanation]
- **[Benefit category]**: [Specific explanation]

### Cons

- **[Drawback category]**: [Specific explanation]
- **[Drawback category]**: [Specific explanation]
- **[Drawback category]**: [Specific explanation]

**Verdict**: [1-2 sentence balanced conclusion with recommendation]

FAQ Block

Use for topic pages with multiple common questions. Essential for FAQ schema.

## Frequently Asked Questions

### [Question phrased exactly as users search]?

[Direct answer in first sentence]. [Supporting context in 2-3 additional sentences].

### [Question phrased exactly as users search]?

[Direct answer in first sentence]. [Supporting context in 2-3 additional sentences].

### [Question phrased exactly as users search]?

[Direct answer in first sentence]. [Supporting context in 2-3 additional sentences].

Tips for FAQ questions:

• Use natural question phrasing ("How do I..." not "How does one...")
• Include question words: what, how, why, when, where, who, which
• Match "People Also Ask" queries from search results
• Keep answers between 50-100 words

Listicle Block

Use for "Best [X]", "Top [X]", "[Number] ways to [X]" queries.

Caveat for self-promotional listicles: ranking yourself #1 in your own "best [category]" guide gets the page cited far more reliably than it gets your brand recommended — for emerging brands, AI answers often harvest the competitor names from the guide and recommend them instead. See citations-vs-recommendations.md before building these at scale.

## [Number] Best [Items] for [Goal/Purpose]

[1-2 sentence intro establishing context and selection criteria]

### 1. [Item Name]

[Why it's included in 2-3 sentences with specific benefits]

### 2. [Item Name]

[Why it's included in 2-3 sentences with specific benefits]

### 3. [Item Name]

[Why it's included in 2-3 sentences with specific benefits]

---

Generative Engine Optimization (GEO) Patterns

These patterns optimize content for citation by AI assistants like ChatGPT, Claude, Perplexity, and Gemini.

Statistic Citation Block

Statistics increase AI citation rates by 15-30%. Always include sources.

[Claim statement]. According to [Source/Organization], [specific statistic with number and timeframe]. [Context for why this matters].

Example:

Mobile optimization is no longer optional for SEO success. According to Google's 2024 Core Web Vitals report, 70% of web traffic now comes from mobile devices, and pages failing mobile usability standards see 24% higher bounce rates. This makes mobile-first indexing a critical ranking factor.

Expert Quote Block

Named expert attribution adds credibility and increases citation likelihood.

"[Direct quote from expert]," says [Expert Name], [Title/Role] at [Organization]. [1 sentence of context or interpretation].

Example:

"The shift from keyword-driven search to intent-driven discovery represents the most significant change in SEO since mobile-first indexing," says Rand Fishkin, Co-founder of SparkToro. This perspective highlights why content strategies must evolve beyond traditional keyword optimization.

Authoritative Claim Block

Structure claims for easy AI extraction with clear attribution.

[Topic] [verb: is/has/requires/involves] [clear, specific claim]. [Source] [confirms/reports/found] that [supporting evidence]. This [explains/means/suggests] [implication or action].

Example:

E-E-A-T is the cornerstone of Google's content quality evaluation. Google's Search Quality Rater Guidelines confirm that trust is the most critical factor, stating that "untrustworthy pages have low E-E-A-T no matter how experienced, expert, or authoritative they may seem." This means content creators must prioritize transparency and accuracy above all other optimization tactics.

Self-Contained Answer Block

Create quotable, standalone statements that AI can extract directly.

**[Topic/Question]**: [Complete, self-contained answer that makes sense without additional context. Include specific details, numbers, or examples in 2-3 sentences.]

Example:

**Ideal blog post length for SEO**: The optimal length for SEO blog posts is 1,500-2,500 words for competitive topics. This range allows comprehensive topic coverage while maintaining reader engagement. HubSpot research shows long-form content earns 77% more backlinks than short articles, directly impacting search rankings.

Evidence Sandwich Block

Structure claims with evidence for maximum credibility.

[Opening claim statement].

Evidence supporting this includes:
- [Data point 1 with source]
- [Data point 2 with source]
- [Data point 3 with source]

[Concluding statement connecting evidence to actionable insight].

---

Domain-Specific GEO Tactics

Different content domains benefit from different authority signals.

Technology Content

• Emphasize technical precision and correct terminology
• Include version numbers and dates for software/tools
• Reference official documentation
• Add code examples where relevant

Health/Medical Content

• Cite peer-reviewed studies with publication details
• Include expert credentials (MD, RN, etc.)
• Note study limitations and context
• Add "last reviewed" dates

Financial Content

• Reference regulatory bodies (SEC, FTC, etc.)
• Include specific numbers with timeframes
• Note that information is educational, not advice
• Cite recognized financial institutions

Legal Content

• Cite specific laws, statutes, and regulations
• Reference jurisdiction clearly
• Include professional disclaimers
• Note when professional consultation is advised

Business/Marketing Content

• Include case studies with measurable results
• Reference industry research and reports
• Add percentage changes and timeframes
• Quote recognized thought leaders

---

Voice Search Optimization

Voice queries are conversational and question-based. Optimize for these patterns:

Question Formats for Voice

• "What is..."
• "How do I..."
• "Where can I find..."
• "Why does..."
• "When should I..."
• "Who is..."

Voice-Optimized Answer Structure

• Lead with direct answer (under 30 words ideal)
• Use natural, conversational language
• Avoid jargon unless targeting expert audience
• Include local context where relevant
• Structure for single spoken response

Back to coreyhaines31/marketingskills (marketing skills for agents) or Agent skills.