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?

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How AI Search Works

The AI Search Landscape

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 (https://developers.google.com/search/docs/fundamentals/ai-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.

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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 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:

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.

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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:

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 (1.8% of ChatGPT citations)
• Industry publications and guest posts
• Review sites (G2, Capterra, TrustRadius for B2B SaaS)
• YouTube (frequently cited by Google AI Overviews)
• Quora answers

Actions:

• Ensure your Wikipedia page is accurate and current
• Participate authentically in Reddit communities
• Get featured in industry roundups and comparison articles
• Maintain updated profiles on relevant review platforms
• Create YouTube content for key how-to queries
• 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.

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 (https://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 (https://cloud.google.com/blog/products/data-analytics/how-the-open-knowledge-format-can-improve-data-sharing) 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 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.

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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)

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Content Types That Get Cited Most

Not all content is equally citable. Prioritize these formats:

Underperformers for AI citation:

• Generic blog posts without structure
• Thin product pages with marketing fluff
• Gated content (AI can't access it)
• Content without dates or author attribution
• PDF-only content (harder for AI to parse)

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.

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Monitoring AI Visibility

What to Track

AI Visibility Monitoring Tools

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

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.

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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.

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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.

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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

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Tool Integrations

For implementation, see the tools registry (../../tools/REGISTRY.md).

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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?

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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

---

Supporting file: evals/evals.json

{
  "skill_name": "ai-seo",
  "evals": [
    {
      "id": 1,
      "prompt": "How do I make sure our SaaS product shows up in AI search results? We're a project management tool and we keep getting left out of ChatGPT and Perplexity recommendations when people ask about project management software.",
      "expected_output": "Should check for product-marketing.md first. Should apply the three pillars framework: Structure (make content extractable), Authority (make content citable), Presence (be where AI looks). Should run through the AI Visibility Audit checklist across platforms (Google AI Overviews, ChatGPT, Perplexity, etc.). Should check content extractability (clear definitions, structured comparisons, statistics). Should reference Princeton GEO research findings (citations improve visibility +40%, statistics +37%). Should check AI bot access in robots.txt. Should provide a prioritized action plan.",
      "assertions": [
        "Checks for product-marketing.md",
        "Applies three pillars framework (Structure, Authority, Presence)",
        "Runs AI Visibility Audit across platforms",
        "Checks content extractability",
        "References Princeton GEO research findings",
        "Checks AI bot access in robots.txt",
        "Provides prioritized action plan"
      ],
      "files": []
    },
    {
      "id": 2,
      "prompt": "Should we block AI crawlers like GPTBot and PerplexityBot in our robots.txt? We're worried about content theft.",
      "expected_output": "Should address the AI bot access question directly. Should explain the tradeoff: blocking AI bots prevents training on your content but also prevents AI platforms from citing and recommending you. Should reference the specific bots and their purposes (GPTBot, Google-Extended, PerplexityBot, ClaudeBot, etc.). Should provide the recommended robots.txt configuration. Should explain that blocking may hurt AI visibility more than it protects content. Should provide a nuanced recommendation based on business goals.",
      "assertions": [
        "Addresses the blocking tradeoff directly",
        "Explains impact on AI visibility vs content protection",
        "Lists specific AI bot user agents",
        "Provides recommended robots.txt configuration",
        "Gives nuanced recommendation based on business goals",
        "Explains what each bot does"
      ],
      "files": []
    },
    {
      "id": 3,
      "prompt": "What kind of content gets cited most by AI systems? We want to create content specifically optimized for AI search.",
      "expected_output": "Should reference the content types that get cited most, including comparisons (~33% of AI citations), definitive guides (~15%), and other high-citation content types. Should explain why these formats work (they provide the structured, extractable, authoritative information AI systems need). Should provide specific recommendations for creating AI-optimized content: clear definitions, structured data, original statistics, comparison tables, expert quotes. Should reference the Princeton GEO research on what increases citation probability.",
      "assertions": [
        "References specific content types with citation rates",
        "Mentions comparisons as highest-cited format",
        "Explains why these formats work for AI",
        "Provides specific content creation recommendations",
        "References Princeton GEO research",
        "Mentions structured data, statistics, and clear definitions"
      ],
      "files": []
    },
    {
      "id": 4,
      "prompt": "we noticed our competitors are showing up in google AI overviews but we're not. what do we need to change?",
      "expected_output": "Should trigger on casual phrasing. Should focus specifically on Google AI Overviews visibility. Should explain how AI Overviews selects sources (authoritative, well-structured, directly answers queries). Should run through the Structure pillar checklist: content extractability, heading hierarchy, answer-first format, structured data. Should check Authority signals: domain authority, citations, E-E-A-T. Should recommend specific content structure changes. Should suggest monitoring approach.",
      "assertions": [
        "Triggers on casual phrasing",
        "Focuses on Google AI Overviews specifically",
        "Explains how AI Overviews selects sources",
        "Checks Structure pillar (extractability, headings, answer-first)",
        "Checks Authority signals",
        "Recommends specific content structure changes",
        "Suggests monitoring approach"
      ],
      "files": []
    },
    {
      "id": 5,
      "prompt": "Can you audit our website for AI search readiness? We want to know how visible we are across ChatGPT, Perplexity, Google AI Overviews, and other AI platforms.",
      "expected_output": "Should run the full AI Visibility Audit. Should check each platform in the landscape (Google AI Overviews, ChatGPT, Perplexity, Claude, Gemini, Copilot). Should evaluate all three pillars: Structure (content extractability, JSON-LD, clear definitions), Authority (citations, backlinks, E-E-A-T signals), Presence (AI bot access, platform-specific factors). Should provide findings organized by pillar. Should provide a prioritized action plan with specific fixes.",
      "assertions": [
        "Runs full AI Visibility Audit",
        "Checks multiple AI platforms",
        "Evaluates all three pillars (Structure, Authority, Presence)",
        "Checks content extractability",
        "Checks AI bot access",
        "Provides findings organized by pillar",
        "Provides prioritized action plan"
      ],
      "files": []
    },
    {
      "id": 6,
      "prompt": "Our organic search traffic has dropped 30% this quarter. Can you do a full SEO audit to figure out what's going on?",
      "expected_output": "Should recognize this is a traditional SEO audit request, not specifically an AI SEO task. Should defer to or cross-reference the seo-audit skill, which handles comprehensive traditional SEO audits including crawlability, technical foundations, on-page optimization, and content quality. May mention AI search as one factor to investigate but should make clear that seo-audit is the primary skill for this task.",
      "assertions": [
        "Recognizes this as a traditional SEO audit request",
        "References or defers to seo-audit skill",
        "Does not attempt a full traditional SEO audit using AI SEO patterns",
        "May mention AI search as one factor to consider"
      ],
      "files": []
    },
    {
      "id": 7,
      "prompt": "We're a seed-stage data-quality startup (barely anyone knows us yet). Plan: publish 20 'best data quality tools' style listicles ranking ourselves #1 so ChatGPT and AI Overviews recommend us. Good idea?",
      "expected_output": "Should apply references/citations-vs-recommendations.md rather than endorsing the plan as-is. Should explain the citation vs. recommendation distinction — self-promotional listicles from low-authority brands often earn citations while the AI answer recommends the competitors named in the guide instead (cites the study directionally: ~69% of self-promotional listicle citations — 224 of 323 — excluded the publisher from recommendations). Should present the visibility ladder (retrieved → cited → mentioned → recommended) and explain recommendation is governed by offsite consensus (reviews, forums, analysts, press). Should NOT say 'don't publish guides' — should reframe: publish a small number of genuinely useful guides for category framing, and rebalance investment toward reviews/communities/earned media. Should mention the attribution blind spot (AI-influenced visits mostly appear as branded search/direct; only a small share is visible AI traffic) and the measurement triad (prompt tracking, self-reported attribution, call recordings).",
      "assertions": [
        "Does not endorse 20 self-ranked listicles as a path to AI recommendations for a low-authority brand",
        "Distinguishes citations from recommendations with the different governing criteria",
        "References the visibility ladder (retrieved/cited/mentioned/recommended)",
        "Warns the guides may surface competitors in AI answers (vote-for-competitors mechanism)",
        "Recommends offsite consensus building (reviews, communities, analysts, or PR) as the recommendation lever",
        "Does not tell the user to stop publishing buyer's guides entirely — reframes expectations toward citation and category framing",
        "Mentions the attribution blind spot and at least two of: prompt tracking, self-reported attribution, call recordings"
      ],
      "files": []
    }
  ]
}

---

Supporting file: references/citations-vs-recommendations.md

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).

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The Visibility Ladder

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

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.

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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.

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What Earns Recommendations

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

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.

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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.

---

Supporting file: references/content-patterns.md

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

---

Supporting file: references/content-types.md

AI SEO by Content Type

Tactical guidance for optimizing specific content types for AI search citation. These tactics work for non-Google AI engines (ChatGPT, Claude, Perplexity, Copilot) and don't hurt Google AI Overviews / AI Mode.

For the cross-cutting strategy, see SKILL.md (../SKILL.md).

---

SaaS Product Pages

Goal: Get cited in "What is [category]?" and "Best [category]" queries. (Citation is the realistic goal here; being recommended in the answer depends on offsite consensus — see citations-vs-recommendations.md.)

Optimize:

• Clear product description in first paragraph (what it does, who it's for)
• Feature comparison tables (you vs. category, not just competitors)
• Specific metrics ("processes 10,000 transactions/sec" not "blazing fast")
• Customer count or social proof with numbers
• Pricing transparency (AI cites pages with visible pricing) — add a /pricing.md file so AI agents can parse your plans without rendering your page (see "Machine-Readable Files" in the main skill)
• FAQ section addressing common buyer questions

---

Blog Content

Goal: Get cited as an authoritative source on topics in your space.

Optimize:

• One clear target query per post (match heading to query)
• Definition in first paragraph for "What is" queries
• Original data, research, or expert quotes
• "Last updated" date visible
• Author bio with relevant credentials
• Internal links to related product/feature pages

---

Comparison / Alternative Pages

Goal: Get cited in "[X] vs [Y]" and "Best [X] alternatives" queries.

Optimize:

• Structured comparison tables (not just prose)
• Fair and balanced (AI penalizes obviously biased comparisons)
• Specific criteria with ratings or scores
• Updated pricing and feature data
• Cite the competitors skill for building these pages

---

Documentation / Help Content

Goal: Get cited in "How to [X] with [your product]" queries.

Optimize:

• Step-by-step format with numbered lists
• Code examples where relevant
• HowTo schema markup
• Screenshots with descriptive alt text
• Clear prerequisites and expected outcomes

---

Local Business / Ecom (Google emphasis)

Google's AI features pull from product feeds and business profiles for local + ecom queries. Optimize:

• Merchant Center feeds kept current with accurate inventory, pricing, attributes
• Google Business Profile complete with hours, services, photos, posts, Q&A answered
• Reviews — recent + sufficient volume; respond to reviews to signal active management
• Service area schema for local services
• Business Agent (where available) for conversational customer engagement

---

Supporting file: references/okf.md

Open Knowledge Format (OKF)

Google's v0.1 markdown spec for representing site content as an agent-readable bundle. Introduced on the Google Cloud blog (https://cloud.google.com/blog/products/data-analytics/how-the-open-knowledge-format-can-improve-data-sharing) on 2026-06-12 and shipped inside Knowledge Catalog.

What it is

OKF is a directory of cross-linked markdown files. Each file has:

• A YAML frontmatter block (type required; title, description, resource, tags, timestamp recommended)
• A standard markdown body
• Standard markdown links to other files in the bundle (which the spec treats as concept relationships)

An optional index.md lists the files for progressive disclosure. The bundle can be distributed as a git repo (recommended), a tarball/zip, or a subdirectory of a larger repo.

The full spec (https://github.com/GoogleCloudPlatform/knowledge-catalog/blob/HEAD/okf/SPEC.md) fits on one page. The repo lives under GoogleCloudPlatform (the "not an official Google product" disclaimer is Google's standard open-source boilerplate, not a denial — it appears on most of Google's open-source repos including their main AI samples repo).

A minimal concept file

---
type: Article
title: How to Connect the Ahrefs MCP Server to Manus
description: The official MCP servers, why they did not connect, and the fix.
resource: https://yoursite.com/blog/ahrefs-mcp-manus/
tags: [mcp, ahrefs]
---

# How to Connect the Ahrefs MCP Server to Manus

The body of the post, as clean markdown.

Add an index.md that lists all files so an agent can see the bundle's shape before opening each file, and that is the entire format.

Honest framing

Google built OKF for data teams sharing catalog metadata — BigQuery tables, API endpoints, metrics, playbooks. Most of the spec's examples are data-team artifacts, not blog posts. Google's blog post framing: "improve data sharing" and "standardized documentation" for collaboration across teams.

Pointing OKF at a marketing site is a clever repurposing popularized by Suganthan Mohanadasan (https://suganthan.com/blog/open-knowledge-format/). It's a legitimate use case for the format but not Google's primary one. Frame it accurately when explaining it to founders or marketing teams.

What it does for AI search today

Nothing immediate. Nothing crawls the web for OKF bundles yet — the spec is weeks old, no AI engine has announced integration, and Knowledge Catalog ingests bundles only for paying enterprise customers' data teams.

Treat OKF as protocol-layer registration — the same shape of bet as early schema.org adoption was a decade ago. Schema took the better part of ten years to pay off; people who shipped it early are still glad they did.

A secondary benefit that pays off today regardless: generating the bundle is itself an internal-linking audit. Suganthan's tool draws every page as a node and every internal link as an edge, so islands and orphans become obvious at a glance.

Where OKF fits in the agent-readable stack

These stack rather than compete. llms.txt is a signpost, OKF is the library.

How to ship one

Three options, ordered by how much effort they take:

1. Suganthan's free web tool (recommended for most sites)

suganthan.com/okf-generator (https://suganthan.com/okf-generator/) — paste a URL or sitemap, crawls up to 100 pages, returns a downloadable bundle. Also draws the resulting page graph so you can spot disconnected pages before publishing.

2. WordPress plugin (pending wp.org approval)

Suganthan's plugin (free, GPL, awaiting wp.org approval at time of writing) installs in a minute, serves the bundle at /okf/, and rebuilds on every publish or edit so it stays in sync. Direct download link is in his blog post (https://suganthan.com/blog/open-knowledge-format/). Requires WordPress 6.0+ and PHP 7.4+. Read-only — never edits posts or settings.

3. By hand

Only practical for a handful of pages. Each post becomes a markdown file with frontmatter that you cross-link manually. Miserable for a whole site.

Hosting & discovery

Serve the bundle at yoursite.com/okf/, starting with yoursite.com/okf/index.md:

• Static hosts / Cloudflare: drag and drop
• WordPress: Suganthan's plugin handles the serving
• Static sites with custom paths: upload the directory to /okf/
• Closed platforms (Wix, Squarespace, most page-builders): you usually can't serve files at custom paths — skip OKF entirely

After it's serving, add a line to llms.txt pointing to the bundle so agents that read llms.txt (today) can discover the bundle (later).

When to skip

• Site is <10 pages — overhead exceeds payoff
• Site is on a closed platform that won't allow custom paths
• You're not maintaining llms.txt, schema markup, or other machine-readable files (OKF compounds with those; alone it does nothing)
• You can't budget the 30 minutes a quarter to refresh the bundle as content changes

What to watch

OKF is v0.1, weeks old. Worth tracking, not worth obsessing over:

• Whether Google announces OKF support in AI Overviews / Knowledge Graph (currently no signal)
• Whether non-Google engines (ChatGPT, Perplexity, Claude) announce OKF reading
• Whether the spec moves to v1.0 (breaking changes are possible at <1.0)
• Whether Knowledge Catalog adds public ingestion endpoints
• Adoption signals — search GitHub for okf/index.md to see who's shipping bundles

---

Supporting file: references/platform-ranking-factors.md

How Each AI Platform Picks Sources

Each AI search platform has its own search index, ranking logic, and content preferences. This guide covers what matters for getting cited on each one.

Sources cited throughout: Princeton GEO study (KDD 2024), SE Ranking domain authority study, ZipTie content-answer fit analysis.

---

The Fundamentals

Every AI platform shares three baseline requirements:

1. Your content must be in their index — Each platform uses a different search backend (Google, Bing, Brave, or their own). If you're not indexed, you can't be cited.
2. Your content must be crawlable — AI bots need access via robots.txt. Block the bot, lose the citation.
3. Your content must be extractable — AI systems pull passages, not pages. Clear structure and self-contained paragraphs win.

Beyond these basics, each platform weights different signals. Here's what matters and where.

---

Google AI Overviews

Google AI Overviews pull from Google's own index and lean heavily on E-E-A-T signals (Experience, Expertise, Authoritativeness, Trustworthiness). They appear in roughly 45% of Google searches.

What makes Google AI Overviews different: They already have your traditional SEO signals — backlinks, page authority, topical relevance. The additional AI layer adds a preference for content with cited sources and structured data. Research shows that including authoritative citations in your content correlates with a 132% visibility boost, and writing with an authoritative (not salesy) tone adds another 89%.

Importantly, AI Overviews don't just recycle the traditional Top 10. Only about 15% of AI Overview sources overlap with conventional organic results. Pages that wouldn't crack page 1 in traditional search can still get cited if they have strong structured data and clear, extractable answers.

What to focus on:

• Schema markup is the single biggest lever — Article, FAQPage, HowTo, and Product schemas give AI Overviews structured context to work with (30-40% visibility boost)
• Build topical authority through content clusters with strong internal linking
• Include named, sourced citations in your content (not just claims)
• Author bios with real credentials matter — E-E-A-T is weighted heavily
• Get into Google's Knowledge Graph where possible (an accurate Wikipedia entry helps)
• Target "how to" and "what is" query patterns — these trigger AI Overviews most often

Watch for OKF. In June 2026 Google introduced the Open Knowledge Format (https://cloud.google.com/blog/products/data-analytics/how-the-open-knowledge-format-can-improve-data-sharing) — a markdown spec for agent-readable site bundles. There is no confirmed signal that AI Overviews factor it in today, but the spec is published, the GitHub repo lives under GoogleCloudPlatform, and it ships inside Knowledge Catalog. For protocol-layer "register early" plays, it has the same shape as early schema.org adoption did a decade ago. See Machine-Readable Files for AI Agents in the main SKILL.md for how to generate and serve a bundle.

---

ChatGPT

ChatGPT's web search draws from a Bing-based index. It combines this with its training knowledge to generate answers, then cites the web sources it relied on.

What makes ChatGPT different: Domain authority matters more here than on other AI platforms. An SE Ranking analysis of 129,000 domains found that authority and credibility signals account for roughly 40% of what determines citation, with content quality at about 35% and platform trust at 25%. Sites with very high referring domain counts (350K+) average 8.4 citations per response, while sites with slightly lower trust scores (91-96 vs 97-100) drop from 8.4 to 6 citations.

Freshness is a major differentiator. Content updated within the last 30 days gets cited about 3.2x more often than older content. ChatGPT clearly favors recent information.

The most important signal is content-answer fit — a ZipTie analysis of 400,000 pages found that how well your content's style and structure matches ChatGPT's own response format accounts for about 55% of citation likelihood. This is far more important than domain authority (12%) or on-page structure (14%) alone. Write the way ChatGPT would answer the question, and you're more likely to be the source it cites.

Where ChatGPT looks beyond your site: Wikipedia accounts for 7.8% of all ChatGPT citations, Reddit for 1.8%, and Forbes for 1.1%. Brand official sites are cited frequently but third-party mentions carry significant weight.

What to focus on:

• Invest in backlinks and domain authority — it's the strongest baseline signal
• Update competitive content at least monthly
• Structure your content the way ChatGPT structures its answers (conversational, direct, well-organized)
• Include verifiable statistics with named sources
• Clean heading hierarchy (H1 > H2 > H3) with descriptive headings

---

Perplexity

Perplexity always cites its sources with clickable links, making it the most transparent AI search platform. It combines its own index with Google's and runs results through multiple reranking passes — initial relevance retrieval, then traditional ranking factor scoring, then ML-based quality evaluation that can discard entire result sets if they don't meet quality thresholds.

What makes Perplexity different: It's the most "research-oriented" AI search engine, and its citation behavior reflects that. Perplexity maintains curated lists of authoritative domains (Amazon, GitHub, major academic sites) that get inherent ranking boosts. It uses a time-decay algorithm that evaluates new content quickly, giving fresh publishers a real shot at citation.

Perplexity has unique content preferences:

• FAQ Schema (JSON-LD) — Pages with FAQ structured data get cited noticeably more often
• PDF documents — Publicly accessible PDFs (whitepapers, research reports) are prioritized. If you have authoritative PDF content gated behind a form, consider making a version public.
• Publishing velocity — How frequently you publish matters more than keyword targeting
• Self-contained paragraphs — Perplexity prefers atomic, semantically complete paragraphs it can extract cleanly

What to focus on:

• Allow PerplexityBot in robots.txt
• Implement FAQPage schema on any page with Q&A content
• Host PDF resources publicly (whitepapers, guides, reports)
• Add Article schema with publication and modification timestamps
• Write in clear, self-contained paragraphs that work as standalone answers
• Build deep topical authority in your specific niche

---

Microsoft Copilot

Copilot is embedded across Microsoft's ecosystem — Edge, Windows, Microsoft 365, and Bing Search. It relies entirely on Bing's index, so if Bing hasn't indexed your content, Copilot can't cite it.

What makes Copilot different: The Microsoft ecosystem connection creates unique optimization opportunities. Mentions and content on LinkedIn and GitHub provide ranking boosts that other platforms don't offer. Copilot also puts more weight on page speed — sub-2-second load times are a clear threshold.

What to focus on:

• Submit your site to Bing Webmaster Tools (many sites only submit to Google Search Console)
• Use IndexNow protocol for faster indexing of new and updated content
• Optimize page speed to under 2 seconds
• Write clear entity definitions — when your content defines a term or concept, make the definition explicit and extractable
• Build presence on LinkedIn (publish articles, maintain company page) and GitHub if relevant
• Ensure Bingbot has full crawl access

---

Claude

Claude uses Brave Search as its search backend when web search is enabled — not Google, not Bing. This is a completely different index, which means your Brave Search visibility directly determines whether Claude can find and cite you.

What makes Claude different: Claude is extremely selective about what it cites. While it processes enormous amounts of content, its citation rate is very low — it's looking for the most factually accurate, well-sourced content on a given topic. Data-rich content with specific numbers and clear attribution performs significantly better than general-purpose content.

What to focus on:

• Verify your content appears in Brave Search results (search for your brand and key terms at search.brave.com)
• Allow ClaudeBot and anthropic-ai user agents in robots.txt
• Maximize factual density — specific numbers, named sources, dated statistics
• Use clear, extractable structure with descriptive headings
• Cite authoritative sources within your content
• Aim to be the most factually accurate source on your topic — Claude rewards precision

---

Allowing AI Bots in robots.txt

If your robots.txt blocks an AI bot, that platform can't cite your content. Here are the user agents to allow:

User-agent: GPTBot           # OpenAI — powers ChatGPT search
User-agent: ChatGPT-User     # ChatGPT browsing mode
User-agent: PerplexityBot    # Perplexity AI search
User-agent: ClaudeBot        # Anthropic Claude
User-agent: anthropic-ai     # Anthropic Claude (alternate)
User-agent: Google-Extended   # Google Gemini and AI Overviews
User-agent: Bingbot          # Microsoft Copilot (via Bing)
Allow: /

Training vs. search: Some AI bots are used for both model training and search citation. If you want to be cited but don't want your content used for training, your options are limited — GPTBot handles both for OpenAI. However, you can safely block CCBot (Common Crawl) without affecting any AI search citations, since it's only used for training dataset collection.

---

Where to Start

If you're optimizing for AI search for the first time, focus your effort where your audience actually is:

Start with Google AI Overviews — They reach the most users (45%+ of Google searches) and you likely already have Google SEO foundations in place. Add schema markup, include cited sources in your content, and strengthen E-E-A-T signals.

Then address ChatGPT — It's the most-used standalone AI search tool for tech and business audiences. Focus on freshness (update content monthly), domain authority, and matching your content structure to how ChatGPT formats its responses.

Then expand to Perplexity — Especially valuable if your audience includes researchers, early adopters, or tech professionals. Add FAQ schema, publish PDF resources, and write in clear, self-contained paragraphs.

Copilot and Claude are lower priority unless your audience skews enterprise/Microsoft (Copilot) or developer/analyst (Claude). But the fundamentals — structured content, cited sources, schema markup — help across all platforms.

Actions that help everywhere:

1. Allow all AI bots in robots.txt
2. Implement schema markup (FAQPage, Article, Organization at minimum)
3. Include statistics with named sources in your content
4. Update content regularly — monthly for competitive topics
5. Use clear heading structure (H1 > H2 > H3)
6. Keep page load time under 2 seconds
7. Add author bios with credentials

---

Supporting file: tools/REGISTRY.md

Marketing Tools Registry

Quick reference for AI agents to discover tool capabilities and integration methods.

How to Use This Registry

1. Find tools by category - Browse sections below for tools in each domain
2. Check integration methods - See what APIs, MCPs, CLIs, or SDKs are available
3. Read integration guides - Detailed setup and common operations in integrations/

---

Tool Index

---

By Category

Analytics

Track user behavior, measure conversions, and analyze marketing performance.

Agent recommendation: Start with GA4 if using Google ecosystem. Use Mixpanel or Amplitude for deeper product analytics. Plausible for privacy-focused sites.

SEO

Search engine optimization tools for keyword research, rank tracking, and site audits.

Agent recommendation: Google Search Console is essential (free). Add Semrush or Ahrefs for competitive research. DataForSEO for programmatic SERP data. Keywords Everywhere for quick keyword lookups. RankParse for agent workflows where per-call cost matters — backlinks, domain authority, and tech stack at a fraction of enterprise pricing.

CRM

Customer relationship management and sales tools.

Agent recommendation: HubSpot for startups/SMBs. Close for high-velocity inside sales. Salesforce for enterprise.

Payments

Payment processing and subscription management.

Agent recommendation: Stripe is the default for SaaS. Paddle for built-in tax compliance.

Referral & Affiliate

Tools for referral programs, affiliate tracking, and partner management.

Agent recommendation: Rewardful or Tolt for Stripe-based SaaS. PartnerStack for enterprise partner programs. Dub.co for link attribution.

Email

Email marketing, transactional email, and automation platforms.

Agent recommendation: Resend for transactional (dev-friendly). Sequenzy for lifecycle email, sequences, and agent-driven email marketing. Postmark for deliverability. Customer.io for advanced automation. Kit for creators. Beehiiv for newsletters. Klaviyo for e-commerce email/SMS. ActiveCampaign for email + CRM combo.

SMS / Messaging

SMS and MMS marketing platforms and programmable messaging APIs.

Agent recommendation: Klaviyo SMS for ecom already on Klaviyo email. Postscript for Shopify-first depth. Attentive for mid-market+ wanting concierge support. Twilio (or Plivo for lower cost) for custom builds and transactional/auth. AudienceTap when AI creative or on-pack QR opt-in matters.

Advertising

Paid advertising platforms and campaign management.

Agent recommendation: Google Ads for search intent. Meta for demand generation. LinkedIn for B2B.

Automation

Workflow automation and integration platforms.

Agent recommendation: Zapier SDK for agents that need to interact with any app directly. Zaps for always-on automations.

CRO & A/B Testing

Conversion rate optimization, heatmaps, and experimentation.

Agent recommendation: Hotjar for understanding user behavior. Optimizely for running experiments.

Scheduling

Booking and appointment scheduling tools.

Agent recommendation: Calendly for general use. SavvyCal for personalized booking experiences.

Forms & Surveys

Form builders and survey platforms.

Agent recommendation: Typeform for engaging forms and surveys.

Messaging

In-app messaging, chat, and customer communication.

Agent recommendation: Intercom for in-app messaging and customer support.

Social Media

Social media scheduling, management, and analytics.

Agent recommendation: Buffer for scheduling and analytics across social platforms.

Video

Video hosting, creation, and AI generation.

Agent recommendation: HeyGen for AI avatar videos (MCP-enabled). Hyperframes for templated, data-driven video from code. Wistia for hosting and analytics.

Data Enrichment

Company and person data enrichment for sales and marketing.

Agent recommendation: Clearbit for enrichment. Apollo for prospecting and outbound. ZoomInfo for enterprise B2B data with intent signals. Clay for waterfall enrichment across multiple providers.

Email Verification

Pre-outreach email deliverability validation.

Agent recommendation: Truelist for any prospect list before outreach — Apollo/ZoomInfo/Hunter data accuracy is typically 60–80%, validation is non-negotiable to keep sender reputation healthy.

Developer Intent / GitHub

Discovery channel for dev-tool SaaS prospecting via GitHub stargazers, forkers, and watchers.

Agent recommendation: Use github-prospects.js CLI to pull stargazers/forks of 3–5 anchor repos (competitors, category leaders, complementary tools). Filter to users with company field set, then enrich missing emails via Apollo or Hunter, then validate via Truelist before outreach.

Site Scraping (single-target only)

Programmatic page extraction for individual public business sites — not for the platforms hosting prospects (Google Maps, LinkedIn, Yelp, Apollo, etc.).

Agent recommendation: Default to Firecrawl for static-ish pages and structured extraction. Use Browserbase when the site requires JS rendering, form interaction, cookie consent, or auth — and when you want session recordings for debugging. For both: discovery happens on platforms (manual browser); extraction happens on the prospect's own website URL. Don't point either tool at LinkedIn, Google Maps, Yelp, or similar.

Reviews

Review management and social proof platforms.

Agent recommendation: Trustpilot for consumer products. G2 for B2B software.

Push Notifications

Push notification delivery platforms.

Agent recommendation: OneSignal for web and mobile push notifications.

Webinar

Webinar and virtual event platforms.

Agent recommendation: Demio for marketing-focused webinars. Livestorm for full event engagement.

Sales Engagement

Sales engagement and outreach automation platforms.

Agent recommendation: Outreach for enterprise sales teams managing multi-touch sequences at scale.

Product Analytics

Product analytics, feature adoption tracking, and in-app guidance.

Agent recommendation: Pendo for tracking feature adoption and delivering targeted in-app guidance.

Competitive Intelligence

Traffic analytics, competitor benchmarking, and market research.

Agent recommendation: Similarweb for competitor traffic analysis and market benchmarking.

Audience Research

Audience intelligence and behavioral research tools.

Agent recommendation: SparkToro for discovering where your ICP spends time — what they read, watch, listen to, follow, and search for. Essential for customer research, content strategy, and media buying decisions.

Visitor Identification

Website visitor de-anonymization for B2B sales and marketing.

Agent recommendation: RB2B for identifying anonymous B2B website visitors and routing high-intent visitors to outreach tools. Pairs well with Clay for enrichment and Instantly/Lemlist for cold email.

Revenue Intelligence

Sales conversation analytics, call recording, and deal intelligence.

Agent recommendation: Gong for mining sales call transcripts for customer research, competitive intelligence, and coaching insights. Essential for revenue attribution and win/loss analysis.

AI Content

AI-powered content generation and optimization platforms.

Agent recommendation: AirOps for building AI content workflows that generate SEO-optimized content at scale.

AI Search

AI-powered web search APIs built for LLMs and agents. Return structured results with on-demand text, highlights, and summaries.

Agent recommendation: Exa for neural search over the open web — content research, competitor/similar-page discovery, link prospecting, news monitoring, and audience research. Pairs well with seo-audit, content-strategy, and competitor-profiling skills.

Partner Ecosystem

Partner data sharing, co-sell, and ecosystem management.

Agent recommendation: Crossbeam for identifying partner account overlaps and co-sell opportunities. Introw for full partner relationship management — partner pipeline, commissions, tasks, and automated business review prep.

Email Outreach

Cold email outreach and email finding tools for link building and sales prospecting.

Agent recommendation: Hunter for finding emails. Lemlist or Instantly for sending cold email campaigns. Snov for combined finding + outreach.

Data Aggregation

Marketing data pipeline tools that connect multiple platforms for unified reporting.

Agent recommendation: Supermetrics for pulling data from multiple marketing platforms into unified reports. Coupler.io for automated data flows to spreadsheets and BI tools.

Commerce & CMS

E-commerce platforms and content management systems.

Agent recommendation: Shopify for e-commerce. Webflow for marketing sites. WordPress for blogs. For headless CMS: Sanity for developer-flexible content, Contentful for enterprise multi-locale, Strapi for self-hosted/budget-conscious. See headless CMS guide (../skills/content-strategy/references/headless-cms.md) for selection criteria.

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CLI Tools

Zero-dependency, single-file Node.js CLIs for tools that don't ship their own. See clis/README.md for install instructions and usage.

All CLIs follow a consistent pattern:

• No dependencies — Node 18+ only, uses native fetch
• JSON output — pipe to jq, save to file, or use in scripts
• Env var auth — set {TOOL}_API_KEY and go
• Consistent commands — {tool} <resource> <action> [options]

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MCP-Enabled Tools

These tools have Model Context Protocol servers available, enabling direct agent interaction:

• ga4 - Google Analytics 4 data access
• stripe - Payment and subscription management
• mailchimp - Email campaign management
• google-ads - Ad campaign management
• resend - Transactional email sending
• zapier - Workflow automation + SDK for 8,000+ app integrations
• zoominfo - B2B contacts and intent data
• clay - Data enrichment and outbound automation
• supermetrics - Cross-platform marketing data
• coupler - Marketing data pipelines
• outreach - Sales engagement sequences
• crossbeam - Partner ecosystem data
• introw - Partner relationship management
• exa - AI-powered web search for LLMs and agents

To use MCP tools, ensure the appropriate MCP server is configured in your environment.

Composio Integration

Composio (integrations/composio.md) provides managed OAuth and pre-built connectors for 500+ tools via a single MCP server. It adds MCP access to tools that don't have native MCP servers, including HubSpot, Salesforce, Meta Ads, LinkedIn Ads, Google Sheets, Slack, Notion, and more.

• Setup: npx @composio/mcp@latest setup
• Quick start: See tools/composio/README.md (composio/README.md)
• Marketing tool mapping: See tools/composio/marketing-tools.md (composio/marketing-tools.md)

Use Composio when you need MCP access to OAuth-heavy tools. Prefer native MCP servers (GA4, Stripe, Mailchimp, etc.) when available — they have deeper coverage.

Cogny Integration

Cogny (integrations/cogny.md) is a hosted MCP gateway focused on marketing channels — one federated MCP URL with managed OAuth across every channel you've connected. Narrower than Composio (marketing-only) and useful when you want SEO, paid social, and privacy-friendly analytics behind a single MCP login.

• Setup: connect channels at cogny.com (https://cogny.com), then in Claude.ai go to Settings → Connectors → Add custom connector and paste https://app.cogny.com/mcp
• Channels: Search Console, Bing Webmaster, Semrush, LinkedIn Ads, Reddit Ads, TikTok Ads, Plausible, Fathom
• Pricing: Solo plan starts at $9/mo (7-day trial)

Use Cogny when you only need marketing channels and want to avoid running your own OAuth proxy. Prefer native APIs when you need deep, custom control of a single tool.

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Quick Start by Use Case

Setting up analytics tracking

1. Read ga4.md (integrations/ga4.md) for web analytics
2. Read segment.md (integrations/segment.md) if routing to multiple tools

Launching a referral program

1. Read rewardful.md (integrations/rewardful.md) or tolt.md (integrations/tolt.md) for Stripe-based programs
2. Read dub-co.md (integrations/dub-co.md) for link tracking

Setting up email automation

1. Read customer-io.md (integrations/customer-io.md) for behavior-based automation
2. Read resend.md (integrations/resend.md) for transactional email

Running email outreach for backlinks

1. Read hunter.md (integrations/hunter.md) for finding emails
2. Read lemlist.md (integrations/lemlist.md) or instantly.md (integrations/instantly.md) for sending campaigns

Running paid ads

1. Read google-ads.md (integrations/google-ads.md) for search campaigns
2. Read meta-ads.md (integrations/meta-ads.md) for social campaigns