name: aeo-audit description: Use when auditing, improving, measuring, or operating AEO for a brand, website, article, offer, video, or content plan across Google AI Overviews, Google AI Mode, ChatGPT, Perplexity, Claude, Copilot, Gemini, and other answer engines. Applies AEO principles including SEO fundamentals, training data, real-time retrieval, query fan-out, people-first content, non-commodity content, content structure, freshness, authority, consensus, citations, brand mentions, YouTube visibility, technical crawlability, AI analytics, bot activity, self-reported attribution, ROI, misinformation monitoring, and platform-specific visibility.

AEO Audit

Use this skill to evaluate and improve visibility in AI-generated search answers. Treat AEO as a probability problem: the goal is to increase the odds that an AI system mentions, cites, or accurately describes the brand.

Core Principles

AI search draws from both training data and real-time retrieval.
Traditional SEO remains foundational because retrieval often depends on search-like discovery.
For Google Search generative AI features, treat AEO/GEO as SEO applied to AI search experiences, not as a separate hack-based discipline.
AI prompts can trigger query fan-out, where one prompt becomes many synthetic subqueries.
Optimize for broad topical coverage, not only exact-match keywords, but do not create thin pages for every fan-out variation.
AI citations are probabilistic, so use AI visibility instead of fixed rankings.
Consensus, freshness, authority, third-party mentions, and source quality can increase citation probability.
Different platforms cite different ecosystems, so diagnose by platform when possible.
Content should be people-first and citation-ready: fresh, focused, structured, entity-rich, easy to parse, and genuinely useful.
Off-site mentions and YouTube assets can be major AEO levers.
Technical access matters: blocked crawlers, JavaScript-only content, slow pages, and poor structure can reduce AI retrieval.
AEO measurement needs multiple signals: AI referral traffic, AI bot activity, self-reported attribution, share of voice, cited domains, topic coverage, and sentiment.
AI referral traffic is usually undercounted; use it for trends, not absolute value.
AEO value includes direct clicks, prequalified visitors, and brand awareness inside AI conversations.

Workflow

Define the target.
- Identify the brand, product, topic, page, or offer.
- Clarify the audience and the AI platforms that matter most.
Map likely AI prompts.
- List buyer, research, comparison, problem-aware, and recommendation-style prompts.
- Include prompts that mention competitors, alternatives, reviews, pricing, use cases, and risks.
Expand into fan-out topics.
- Break each prompt into adjacent subquestions an AI system might retrieve.
- Look for missing coverage around definitions, comparisons, examples, pricing, implementation, objections, proof, and next steps.
- Use fan-out to identify coverage gaps, not to create scaled thin content variants.
Audit retrieval readiness.
- Check whether the target content is crawlable, clear, current, and search-discoverable.
- Prefer content that directly answers specific subquestions with concise, sourceable sections.
- Note where traditional SEO improvements would also help AEO.
Audit content citation readiness.
- Check whether each major section uses BLUF: answer first, support after.
- Confirm important H2 sections are clear and useful on their own for readers and retrieval.
- Look for entity-rich specificity: named products, brands, categories, tools, places, use cases, and relationships.
- Prefer clear declarative sentences with one main idea per sentence.
- Flag stale pages, old comparisons, outdated stats, and sleeper pages with authority but traffic decline.
- Recommend citation-friendly formats where useful: listicles, comparisons, reviews, "best X," "top X," X-versus-Y, and original data.
- Check whether content is non-commodity: first-hand experience, expert analysis, original data, practical examples, or a distinct point of view.
- Reject AI-only rewrites, generic summaries, or pages that would not satisfy a real visitor.
Audit consensus and authority.
- Look for consistent mentions across the brand site, third-party publishers, communities, directories, reviews, podcasts, videos, and social platforms.
- Identify unsupported claims, conflicting descriptions, outdated information, or thin third-party presence.
- Prioritize mention opportunities in three tiers: editorial/review/listicle pages, community/Q&A/forum pages, and owned media properties.
- Recommend authentic mention earning only; do not recommend artificial mention seeding or spam.
Audit YouTube visibility.
- Identify whether YouTube videos already rank for important niche keywords.
- Prioritize search-hit topics over viral-hit topics.
- Recommend clear keyword titles, descriptive summaries, timestamps, spoken keywords/entities, and formats matching current search intent.
- Treat transcripts as both potential citation sources and training examples.
Audit technical AEO.
- Check for AI crawler blocks in robots.txt, especially GPTBot, OAI-SearchBot, ClaudeBot, and Google-Extended.
- Flag JavaScript-only content that may be invisible to some AI crawlers.
- Note page speed issues that could hurt real-time retrieval.
- Check heading hierarchy, clean HTML structure, and focused paragraphs.
- Treat schema as useful SEO support, not a required or guaranteed AI citation lever.
- Recommend redirects for recurring AI hallucinated URLs that produce 404s.
- Treat llms.txt as optional for Google AI visibility, but important when the user asks about Agentic Browsing, AI-agent accessibility, PageSpeed's Agentic Browsing category, or a tool specifically reports an llms.txt failure.
- When auditing llms.txt, verify that /llms.txt is a real Markdown text file and is not falling through to an SPA/app-shell HTML response.
- Validate the basic llms.txt recommendation pattern: a required H1 (# Site or Project Name), a short blockquote summary, useful context paragraphs, H2 sections, and Markdown list links such as - [Page name](https://example.com/page): Why this page matters.
- Include links to the highest-value official pages: homepage, product/program pages, FAQ, about/trust pages, support/contact, key articles, sitemap, and robots.txt. Use an ## Optional section for secondary legal or low-priority pages.
- Ensure llms.txt is concise, current, factual, and human-readable. It should guide AI agents to authoritative content; it should not replace the sitemap, robots.txt, schema, or strong page content.
- After adding or changing llms.txt, verify live status, content type, and body with curl -I https://example.com/llms.txt and curl https://example.com/llms.txt, then rerun the Agentic Browsing/PageSpeed check.
- For Google AI features, check indexability, snippet eligibility, crawlability, Search Essentials compliance, JavaScript SEO, page experience, and duplicate-content issues.
- For local and ecommerce sites, check whether business and product details are maintained through appropriate Google surfaces.
Audit measurement and analytics.
- Check whether AI referral traffic is isolated in analytics using known AI sources.
- Identify which pages receive AI traffic and whether those pages are fresh, accurate, and conversion-ready.
- Identify important pages that receive no AI traffic and investigate content, crawlability, internal linking, topic demand, or mention gaps.
- Check whether AI bot activity is available through server logs, CDN logs, or analytics.
- Separate training bots from search/citation bots where possible.
- Recommend self-reported attribution in signup, checkout, demo, or post-purchase flows.
Audit ROI and progress tracking.

Treat raw AI traffic as incomplete and often smaller than SEO traffic.
Look for conversion quality, assisted awareness, branded search lift, and self-reported AI influence.
Track AI share of voice against competitors.
Track cited domains, topic coverage, and mention sentiment over time.
Recommend a monthly quick check and quarterly competitive audit.

Audit misinformation risk.

Check whether AI systems describe the brand accurately, specifically, and positively.
Identify vague official information that could allow third-party misinformation to fill the gap.
Recommend specific official pages or FAQs with dates, numbers, company facts, pricing, product details, and direct corrections.
If misinformation appears, update owned content first, then pursue corrections from third-party sources.

Diagnose by platform.

Google AI Overviews: Prioritize foundational SEO, indexed and snippet-eligible pages, helpful non-commodity content, crawlability, page experience, useful media, and authentic authority signals.
Google AI Mode: Treat as a Google Search generative AI experience where SEO fundamentals still apply; monitor actual source behavior separately from AI Overviews.
ChatGPT: Prioritize publisher mentions, editorial authority, knowledge-base clarity, widespread brand associations, and trusted third-party context.
Perplexity: Prioritize pages that already perform well in search and provide clear citation-worthy answers.

Produce the output.

Start with the highest-leverage gaps.
Separate recommendations into content, SEO, third-party mentions, YouTube, technical access, measurement, misinformation, freshness, authority, and platform-specific actions.
Include quick wins and longer-term visibility plays.

Output Format

When asked for an AEO audit, use this structure:

## AEO Snapshot

Brief summary of current AI visibility strengths and weaknesses.

## Likely AI Prompts

- Prompt examples the target should appear for.

## Query Fan-Out Gaps

- Missing subtopics or questions AI systems may retrieve.

## Visibility Factors

- Consensus:
- Freshness:
- Authority:
- Retrieval readiness:
- Content citation readiness:
- Third-party mentions:
- YouTube visibility:
- Technical access:
- Measurement readiness:
- Misinformation risk:

## Platform Notes

- Google AI Overviews:
- Google AI Mode:
- ChatGPT:
- Perplexity:

## Recommended Actions

1. Highest-priority action.
2. Next action.
3. Longer-term action.

## Operating Cadence

- This week:
- Monthly:
- Quarterly:

Guardrails

Do not treat AEO as a replacement for SEO.
For Google Search, do not present AEO/GEO as a separate system from SEO; use Google's framing that generative AI search optimization is still optimizing for the search experience.
Do not claim exact AI ranking positions; describe visibility probability and observed citations instead.
Do not assume all AI platforms use the same sources.
When current platform behavior, market share, or citations matter, verify with current research or live checks.
Keep recommendations practical: every recommendation should help content coverage, retrieval, authority, consensus, freshness, or platform-specific visibility.
Do not recommend creating separate pages for every query fan-out variation. Avoid scaled thin content.
Do not recommend content rewritten only for AI systems. Prioritize people-first, non-commodity content.
Do not overstate chunking. Clear sections help readers and retrieval, but Google does not require tiny AI chunks.
Do not overstate llms.txt, special AI files, or schema as Google AI visibility requirements.
Do not dismiss llms.txt issues when the user is trying to improve AI-agent accessibility or PageSpeed's Agentic Browsing score. In that case, fix the file to follow the Markdown/H1/link-list pattern while still explaining that it is not a guaranteed Google AI ranking lever.
Do not pursue inauthentic mentions.
Do not recommend fake freshness. Content updates should be meaningful.
Do not spam communities for mentions. Recommend useful participation in relevant discussions.
Do not overstate AI analytics precision. Explain undercounting and attribution gaps.
Do not judge AEO ROI on referral traffic alone; include conversion quality, self-attribution, brand awareness, and share-of-voice movement.

--- name: aeo-audit description: Use when auditing, improving, measuring, or operating AEO for a brand, website, article, offer, video, or content plan across Google AI Overviews, Google AI Mode, ChatGPT, Perplexity, Claude, Copilot, Gemini, and other answer engines. Applies AEO principles including SEO fundamentals, training data, real-time retrieval, query fan-out, people-first content, non-commodity content, content structure, freshness, authority, consensus, citations, brand mentions, YouTube visibility, technical crawlability, AI analytics, bot activity, self-reported attribution, ROI, misinformation monitoring, and platform-specific visibility. --- # AEO Audit Use this skill to evaluate and improve visibility in AI-generated search answers. Treat AEO as a probability problem: the goal is to increase the odds that an AI system mentions, cites, or accurately describes the brand. ## Core Principles - AI search draws from both **training data** and **real-time retrieval**. - Traditional SEO remains foundational because retrieval often depends on search-like discovery. - For Google Search generative AI features, treat AEO/GEO as SEO applied to AI search experiences, not as a separate hack-based discipline. - AI prompts can trigger **query fan-out**, where one prompt becomes many synthetic subqueries. - Optimize for broad topical coverage, not only exact-match keywords, but do not create thin pages for every fan-out variation. - AI citations are probabilistic, so use **AI visibility** instead of fixed rankings. - Consensus, freshness, authority, third-party mentions, and source quality can increase citation probability. - Different platforms cite different ecosystems, so diagnose by platform when possible. - Content should be people-first and citation-ready: fresh, focused, structured, entity-rich, easy to parse, and genuinely useful. - Off-site mentions and YouTube assets can be major AEO levers. - Technical access matters: blocked crawlers, JavaScript-only content, slow pages, and poor structure can reduce AI retrieval. - AEO measurement needs multiple signals: AI referral traffic, AI bot activity, self-reported attribution, share of voice, cited domains, topic coverage, and sentiment. - AI referral traffic is usually undercounted; use it for trends, not absolute value. - AEO value includes direct clicks, prequalified visitors, and brand awareness inside AI conversations. ## Workflow 1. Define the target. - Identify the brand, product, topic, page, or offer. - Clarify the audience and the AI platforms that matter most. 2. Map likely AI prompts. - List buyer, research, comparison, problem-aware, and recommendation-style prompts. - Include prompts that mention competitors, alternatives, reviews, pricing, use cases, and risks. 3. Expand into fan-out topics. - Break each prompt into adjacent subquestions an AI system might retrieve. - Look for missing coverage around definitions, comparisons, examples, pricing, implementation, objections, proof, and next steps. - Use fan-out to identify coverage gaps, not to create scaled thin content variants. 4. Audit retrieval readiness. - Check whether the target content is crawlable, clear, current, and search-discoverable. - Prefer content that directly answers specific subquestions with concise, sourceable sections. - Note where traditional SEO improvements would also help AEO. 5. Audit content citation readiness. - Check whether each major section uses BLUF: answer first, support after. - Confirm important H2 sections are clear and useful on their own for readers and retrieval. - Look for entity-rich specificity: named products, brands, categories, tools, places, use cases, and relationships. - Prefer clear declarative sentences with one main idea per sentence. - Flag stale pages, old comparisons, outdated stats, and sleeper pages with authority but traffic decline. - Recommend citation-friendly formats where useful: listicles, comparisons, reviews, "best X," "top X," X-versus-Y, and original data. - Check whether content is non-commodity: first-hand experience, expert analysis, original data, practical examples, or a distinct point of view. - Reject AI-only rewrites, generic summaries, or pages that would not satisfy a real visitor. 6. Audit consensus and authority. - Look for consistent mentions across the brand site, third-party publishers, communities, directories, reviews, podcasts, videos, and social platforms. - Identify unsupported claims, conflicting descriptions, outdated information, or thin third-party presence. - Prioritize mention opportunities in three tiers: editorial/review/listicle pages, community/Q&A/forum pages, and owned media properties. - Recommend authentic mention earning only; do not recommend artificial mention seeding or spam. 7. Audit YouTube visibility. - Identify whether YouTube videos already rank for important niche keywords. - Prioritize search-hit topics over viral-hit topics. - Recommend clear keyword titles, descriptive summaries, timestamps, spoken keywords/entities, and formats matching current search intent. - Treat transcripts as both potential citation sources and training examples. 8. Audit technical AEO. - Check for AI crawler blocks in `robots.txt`, especially GPTBot, OAI-SearchBot, ClaudeBot, and Google-Extended. - Flag JavaScript-only content that may be invisible to some AI crawlers. - Note page speed issues that could hurt real-time retrieval. - Check heading hierarchy, clean HTML structure, and focused paragraphs. - Treat schema as useful SEO support, not a required or guaranteed AI citation lever. - Recommend redirects for recurring AI hallucinated URLs that produce 404s. - Treat `llms.txt` as optional for Google AI visibility, but important when the user asks about Agentic Browsing, AI-agent accessibility, PageSpeed's Agentic Browsing category, or a tool specifically reports an `llms.txt` failure. - When auditing `llms.txt`, verify that `/llms.txt` is a real Markdown text file and is not falling through to an SPA/app-shell HTML response. - Validate the basic llms.txt recommendation pattern: a required H1 (`# Site or Project Name`), a short blockquote summary, useful context paragraphs, H2 sections, and Markdown list links such as `- [Page name](https://example.com/page): Why this page matters`. - Include links to the highest-value official pages: homepage, product/program pages, FAQ, about/trust pages, support/contact, key articles, sitemap, and robots.txt. Use an `## Optional` section for secondary legal or low-priority pages. - Ensure `llms.txt` is concise, current, factual, and human-readable. It should guide AI agents to authoritative content; it should not replace the sitemap, robots.txt, schema, or strong page content. - After adding or changing `llms.txt`, verify live status, content type, and body with `curl -I https://example.com/llms.txt` and `curl https://example.com/llms.txt`, then rerun the Agentic Browsing/PageSpeed check. - For Google AI features, check indexability, snippet eligibility, crawlability, Search Essentials compliance, JavaScript SEO, page experience, and duplicate-content issues. - For local and ecommerce sites, check whether business and product details are maintained through appropriate Google surfaces. 9. Audit measurement and analytics. - Check whether AI referral traffic is isolated in analytics using known AI sources. - Identify which pages receive AI traffic and whether those pages are fresh, accurate, and conversion-ready. - Identify important pages that receive no AI traffic and investigate content, crawlability, internal linking, topic demand, or mention gaps. - Check whether AI bot activity is available through server logs, CDN logs, or analytics. - Separate training bots from search/citation bots where possible. - Recommend self-reported attribution in signup, checkout, demo, or post-purchase flows. 10. Audit ROI and progress tracking. - Treat raw AI traffic as incomplete and often smaller than SEO traffic. - Look for conversion quality, assisted awareness, branded search lift, and self-reported AI influence. - Track AI share of voice against competitors. - Track cited domains, topic coverage, and mention sentiment over time. - Recommend a monthly quick check and quarterly competitive audit. 11. Audit misinformation risk. - Check whether AI systems describe the brand accurately, specifically, and positively. - Identify vague official information that could allow third-party misinformation to fill the gap. - Recommend specific official pages or FAQs with dates, numbers, company facts, pricing, product details, and direct corrections. - If misinformation appears, update owned content first, then pursue corrections from third-party sources. 12. Diagnose by platform. - **Google AI Overviews:** Prioritize foundational SEO, indexed and snippet-eligible pages, helpful non-commodity content, crawlability, page experience, useful media, and authentic authority signals. - **Google AI Mode:** Treat as a Google Search generative AI experience where SEO fundamentals still apply; monitor actual source behavior separately from AI Overviews. - **ChatGPT:** Prioritize publisher mentions, editorial authority, knowledge-base clarity, widespread brand associations, and trusted third-party context. - **Perplexity:** Prioritize pages that already perform well in search and provide clear citation-worthy answers. 13. Produce the output. - Start with the highest-leverage gaps. - Separate recommendations into content, SEO, third-party mentions, YouTube, technical access, measurement, misinformation, freshness, authority, and platform-specific actions. - Include quick wins and longer-term visibility plays. ## Output Format When asked for an AEO audit, use this structure: ```markdown ## AEO Snapshot Brief summary of current AI visibility strengths and weaknesses. ## Likely AI Prompts - Prompt examples the target should appear for. ## Query Fan-Out Gaps - Missing subtopics or questions AI systems may retrieve. ## Visibility Factors - Consensus: - Freshness: - Authority: - Retrieval readiness: - Content citation readiness: - Third-party mentions: - YouTube visibility: - Technical access: - Measurement readiness: - Misinformation risk: ## Platform Notes - Google AI Overviews: - Google AI Mode: - ChatGPT: - Perplexity: ## Recommended Actions 1. Highest-priority action. 2. Next action. 3. Longer-term action. ## Operating Cadence - This week: - Monthly: - Quarterly: ``` ## Guardrails - Do not treat AEO as a replacement for SEO. - For Google Search, do not present AEO/GEO as a separate system from SEO; use Google's framing that generative AI search optimization is still optimizing for the search experience. - Do not claim exact AI ranking positions; describe visibility probability and observed citations instead. - Do not assume all AI platforms use the same sources. - When current platform behavior, market share, or citations matter, verify with current research or live checks. - Keep recommendations practical: every recommendation should help content coverage, retrieval, authority, consensus, freshness, or platform-specific visibility. - Do not recommend creating separate pages for every query fan-out variation. Avoid scaled thin content. - Do not recommend content rewritten only for AI systems. Prioritize people-first, non-commodity content. - Do not overstate chunking. Clear sections help readers and retrieval, but Google does not require tiny AI chunks. - Do not overstate `llms.txt`, special AI files, or schema as Google AI visibility requirements. - Do not dismiss `llms.txt` issues when the user is trying to improve AI-agent accessibility or PageSpeed's Agentic Browsing score. In that case, fix the file to follow the Markdown/H1/link-list pattern while still explaining that it is not a guaranteed Google AI ranking lever. - Do not pursue inauthentic mentions. - Do not recommend fake freshness. Content updates should be meaningful. - Do not spam communities for mentions. Recommend useful participation in relevant discussions. - Do not overstate AI analytics precision. Explain undercounting and attribution gaps. - Do not judge AEO ROI on referral traffic alone; include conversion quality, self-attribution, brand awareness, and share-of-voice movement.