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/ai-seo

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

Use this Skill: https://skilld.dev/gh/coreyhaines31/marketingskills/ai-seo

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referencesai-attribution.md

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Measuring AI Search Impact Past the Referral Click

AI answers influence buyers who never click a link. They read a recommendation, then search your brand or type your URL a few days later. Referral reports miss most of that, so "AI sends us almost no traffic" is usually a measurement gap, and the impact sits in other channels.

Profound joined more than two million AI conversations with later browsing behavior (Profound, July 2026; observational, US-only, measured site visits rather than purchases). Two findings shape the setup below:

  • Only 42% of the downstream visits after an AI assistant introduced a brand happened within 24 hours. A same-session window misses most of the effect.
  • More than 97% of those visits carried no AI referral parameter, even after ChatGPT made its links easier to click.

SimilarWeb's journey data points the same way: about 9% of post-recommendation visits arrived as visible AI referrals (see citations-vs-recommendations.md). Profound sells AI visibility tracking, so treat its numbers as vendor research, and don't present either study's lift as a forecast for one brand.

Four layers

Report each layer separately. Rising visibility does not prove rising revenue.

Layer What it captures Where it comes from
Exposure Mentions, recommendations, prominence, framing, citations Prompt tracking (see prompt-portfolio.md)
Immediate behavior AI referral sessions, landing pages, engaged sessions Analytics, with AI referrers grouped (see the analytics skill)
Delayed behavior Branded search, direct visits, return visits, cross-session conversions Search Console branded queries, analytics, CRM
Business outcome Signups, leads, qualified pipeline, purchases, revenue, retention CRM and billing

Setup

  1. Name the decision. "Should we keep funding AEO content?" needs a different design from "Did fixing our pricing page change anything?"
  2. Define exposure by engine, prompt cohort, market, language, and date, so you know what changed and when.
  3. Fix referral capture. Group known AI referrers (chatgpt.com, perplexity.ai, gemini.google.com, copilot.microsoft.com, claude.ai, and so on) into one channel. Preserve landing page, referrer, and query parameters. Expect it to undercount.
  4. Add delayed windows. Measure branded search and direct visits to exposed pages over 1-hour, 24-hour, and 7-day windows after a known exposure change.
  5. Ask people. A "How did you hear about us?" field with an AI option and a "What did you ask?" follow-up, plus tagged sales-call notes, catch journeys analytics can't see.
  6. Join to outcomes in the CRM without counting the same conversion twice across layers.

Evaluation designs

Pick the strongest design you can afford, and say which one you used.

Design Use when Confidence
Holdout You can leave comparable pages, products, or markets untouched Highest
Matched comparison Similar pages or markets exist, but you can't randomize Medium
Interrupted time series One clear intervention date and a long, stable baseline Medium
Pre/post on a stable cohort Nothing better is possible Low; confounders easily swamp it
Exposure panel or incrementality study Budget for a vendor panel Varies with the panel's method

List confounders before reading results: campaigns, launches, press, seasonality, pricing changes, and model or retrieval updates.

Reporting language

Say which of three things you have: an observation ("branded search rose 18% after the change"), an association ("pages with more AI mentions saw more direct visits"), or a causal result ("the holdout pages didn't move; the treated pages did"). Most AEO reporting only supports the first two. Say so.

Respect privacy rules in panel and self-reported data: aggregate small groups and follow your consent and retention policies.


Framework lineage: the four-layer model, attribution windows, and evaluation designs adapted (re-expressed and restructured) from Profound's MIT-licensed ai-search-attribution-planner skill and its "AI mention effect" study (July 2026), credited.

Source: SKILL.md on GitHub

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    The skill provides expert guidance on AI Search Engine Optimization (AEO). The main security consideration is a low-severity risk of indirect prompt injection, as the agent is tasked with analyzing external websites and AI search outputs that may contain untrusted content.

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Activeupdated 5 days ago
metadata
{
  "version": "2.10.1"
}
  • ai-seo
  • aeo
  • llm-optimization
  • answer-engine
  • content-marketing
  • structured-data
  • generative-search
  • ai-visibility

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Optimizes content to be discovered and cited by AI search engines like ChatGPT, Perplexity, Claude, Google AI Overviews, and Copilot. Covers content structure for extractability, authority signals, platform-specific ranking factors, and AI bot access configuration.

Generated from the current SKILL.md.

Does this skill help with Google AI Overviews?
Yes, but the approach differs from other AI platforms. For Google AI Overviews, follow standard SEO practices and E-E-A-T — Google explicitly states no special markup or AI-specific optimization is required. For ChatGPT, Claude, and Perplexity, layer on extractable structure (definition blocks, FAQs, comparison tables) and llms.txt files.
What if my site is blocked from AI crawlers?
You won't be cited by that platform. Check your robots.txt for blocks on GPTBot, PerplexityBot, ClaudeBot, Google-Extended, and Bingbot. You can allow search/citation crawlers while blocking training-only crawlers like CCBot.
Does AI SEO require creating separate content from traditional SEO?
No. Write for people and organize with clear headings, paragraphs, and structure. This satisfies both traditional search and AI platforms. Avoid creating scaled or duplicate content specifically for AI, which violates Google's spam policies.
Which optimization methods boost AI visibility the most?
According to Princeton GEO research, citing sources (+40%), adding statistics with sources (+37%), and including expert quotations (+30%) deliver the largest visibility boosts across AI platforms like Perplexity. Combining fluency improvements with statistics yields the highest gains.
Can I rank on page 2 or 3 and still get cited in AI answers?
Yes. AI systems select sources based on content quality, structure, and relevance — not just rank position. A well-structured page with clear answers and authority signals can be cited even if it ranks lower in traditional search.

Generated from the current SKILL.md. These answers refresh after source changes.