Use the playbook below as your instructions for this conversation.

How to use it in this chat:
1. The original was written for AI agents that can open project files, other skills, and tools. You will not have any of those here, and you do not need them.
2. Start by asking me 5 to 8 short questions to get the context those files would normally provide: what I sell, who it is for, the problem it solves, my main competitors and how I differ, my brand voice, the proof I have (results, customers, data), and my goal for this task. Skip anything I have already told you.
3. Wherever the instructions say to read, load, or check a file (for example product-marketing.md, brand-context.md, or a references folder), use my answers and your own expertise instead. If a referenced file is included at the end of this message, use it.
4. Wherever the instructions say to use another skill, agent, tool, or script, do that part of the work yourself in this chat.
5. Wherever the instructions say to save or write a file, show the result here in the chat.

---

PLAYBOOK

# 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.

## Reference Routing

Start with this skill's core workflow. Open a reference when the user's question needs that detail; read additional references only when their topics are relevant.

| User questions | Local reference |
|---|---|
| Why can an agent not read our site? Are bot access, discovery files, or HTML blocking it? Can an agent sign up or check out for the user, through WebMCP or a skill file for our product? | [Agent readiness](references/agent-readiness.md) |
| AI barely shows up in referrals. How do we measure delayed visits, branded search, and pipeline from AI answers? | [AI attribution](references/ai-attribution.md) |
| Is AI saying something wrong about our price, features, or limits? How do we find and fix the source? | [Answer accuracy](references/answer-accuracy.md) |
| Which vendor does Claude Code or Cursor pick when a user says "deploy this"? How do we become the tool an agent installs mid-task? | [Agent defaults](references/agent-defaults.md) |
| Why are we cited but absent from the shortlist? Can our own buyer's guide help competitors? How should we measure recommendations? | [Citations vs. recommendations](references/citations-vs-recommendations.md) |
| How should we structure a definition, comparison, or FAQ? What makes an answer block useful on its own? | [Content patterns](references/content-patterns.md) |
| How does the approach differ for product pages, docs, local businesses, or ecommerce? Which content type should we improve? | [Content types](references/content-types.md) |
| Did an engine change which page formats it cites? Should we still invest in listicles or comparison pages? How do we separate a shift from noisy measurements? | [Format volatility](references/format-volatility.md) |
| Should we publish LinkedIn posts, articles, or company-page content? What makes that content discoverable and citable? | [LinkedIn citations](references/linkedin-ai-citations.md) |
| How do we run AEO in other languages and countries? Is ChatGPT Shopping worth it for our product? | [Multilingual and commerce](references/multilingual-and-commerce.md) |
| What belongs in an OKF bundle? Is it an established AI-search requirement or an emerging experiment? | [Open Knowledge Format](references/okf.md) |
| Which prompts should we track, how often, which topics are we losing, and how do we test multi-turn buying conversations? | [Prompt portfolio](references/prompt-portfolio.md) |
| Where should we start for a particular AI engine? Which search and training crawlers should we distinguish? | [Platform ranking factors](references/platform-ranking-factors.md) |
| Why does AI describe our positioning incorrectly? How can third-party consensus reinforce a specific use case? | [Positioning and consensus](references/positioning-and-consensus.md) |
| How should a video's title, captions, chapters, or description support citations? What should we check before publishing? | [YouTube citations](references/youtube-ai-citations.md) |

Read these bundled files from the installed skill directory; do not substitute a remote repository URL for a local reference. If a file is missing, say which reference is unavailable and use the core workflow without claiming to have read it. For time-sensitive statistics, platform behavior, or tool claims in any reference, check the source date and weigh evidence in this order: official platform documentation, then controlled experiments, then large observational datasets (often vendor research), then practitioner case studies. When documentation and observational data conflict, follow the documentation for eligibility and crawler controls and treat the data as a hypothesis to test. Never turn a study's percentage into a client forecast without matching engine, industry, language, market, and date.

## 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?
- Do you have Wikipedia coverage or a presence on review sites?

---

## How AI Search Works

### The AI Search Landscape

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

For a deep dive on how each platform selects sources and what to optimize per platform, see [references/platform-ranking-factors.md](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. Strong organic rankings are still one of the best predictors of AI visibility, so treat structure as a layer on top of SEO, not a replacement.

**Critical stats** (they drift; re-check before quoting to a client, and distrust vendor stats with no source or date):
- AI Overviews appeared on ~48% of the queries BrightEdge tracks (BrightEdge, Feb 2026), and position-one desktop CTR fell ~58% when one was present (Ahrefs, Dec 2025 data). Both are sample-specific, so don't forecast a site's traffic loss from them
- Adding statistics and citations raised visibility by roughly 30–40% in the original GEO study (Aggarwal et al., 2023)

### 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 beyond top-ranked pages, including Reddit, Wikipedia, and review sites, in a mix that varies by engine

**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, write for people and 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, with one owning page per fact family (matrix in [references/content-patterns.md](references/content-patterns.md)).

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

---

## AI Visibility Audit

Before optimizing, assess your current AI search presence.

### Step 1: Check AI Answers for Your Key Queries

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

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

**Query types to test**, spread across buyer awareness stages and written the way people actually type into a chat box:
- Problem-aware: "How do I [problem your product solves]?"
- Solution-aware: "What is [your product category]?" / "Best [category] for [use case]"
- Product-aware: "[Your brand] vs [competitor]" / "Is [your brand] good for [use case]?"
- Most aware: "[Your brand] pricing" / "Does [your brand] integrate with [tool]?"

Keep the list to prompts that would change revenue. A citation for a glossary question ("what is gross profit?") rarely sells anything. Track prompts like keywords: collapse wording variants of the same intent into one core prompt, then run each several times.

### 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?

**Turn findings into work.** If an assistant states something wrong or vague about you, the page that should state that fact isn't saying it plainly; fix that page first. If you're absent, the sources it cites for the prompt are your target list for PR, review sites, and directories.

### Step 3: Content Extractability Check

For each priority page, verify:

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

### Step 4: AI Bot Access Check

Audit AI user agents by purpose. Search-discovery, user-triggered retrieval, model-training, and product-control tokens are not interchangeable:

- **Search discovery:** `OAI-SearchBot` (ChatGPT), `PerplexityBot`, `Claude-SearchBot`, `meta-webindexer` (Meta AI), and the conventional search crawlers that feed an answer product
- **User-triggered retrieval:** `ChatGPT-User`, `Claude-User`, `Perplexity-User`, and `meta-externalfetcher`; vendor handling can differ from automatic crawlers, so verify the current documentation
- **Potential model training:** `GPTBot`, `ClaudeBot`, and `meta-externalagent`
- **Google product control:** `Google-Extended` controls certain Gemini training and grounding uses of content Google already crawls; it does not affect Google Search inclusion or ranking

Default to one wildcard (`User-agent: *`) rule that allows agents, and add named groups only for deliberate exceptions such as a training opt-out; an allowlist of named crawlers silently excludes agent search APIs that run their own crawlers, and a named group replaces the `*` rules for that bot. Check each relevant user-agent group and any WAF or CDN rules separately. A publisher can allow search discovery while disallowing model-development crawlers; do not infer that blocking a training crawler necessarily blocks citations. See [references/platform-ranking-factors.md](references/platform-ranking-factors.md) for the full robots.txt configuration.

---

## Optimization Strategy

### The Three Pillars

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

### Pillar 1: Structure — Make Content Extractable

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

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

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

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

### Pillar 2: Authority — Make Content Citable

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

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

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

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

**Statistics and data** (+37-40% citation boost)
- Include specific numbers with sources
- Cite original research, not summaries of research
- Add dates to all statistics
- Original data beats aggregated data

**Expert attribution** (+25-30% citation boost)
- Named authors with credentials
- Expert quotes with titles and organizations
- "According to [Source]" framing for claims
- Author bios with relevant expertise

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

**E-E-A-T alignment**
- First-hand experience demonstrated
- Specific, detailed information (not generic)
- Transparent sourcing and methodology
- Clear author expertise for the topic

### Pillar 3: Presence — Be Where AI Looks

AI systems cite your own site and the places that talk about you, in proportions that vary by engine and category. In one large dataset, company sites took about 57% of citations (ChatGPT 47%, Gemini 69%; Profound, July 2026), while shortlist recommendations lean on third-party consensus. Measure your category's citation mix before choosing channels; see [references/citations-vs-recommendations.md](references/citations-vs-recommendations.md).

**Third-party surfaces to cover:**
- Wikipedia (7.8% of ChatGPT citations in Profound's Aug 2024–Jun 2025 data)
- Reddit (1.8% in the same data, then nearly wiped from ChatGPT by Aug 2026 retrieval changes; still retrieved elsewhere; see the volatility section in [references/agent-readiness.md](references/agent-readiness.md))
- Industry publications and guest posts
- LinkedIn — among the most-cited domains for professional queries, but which surface gets cited depends on the engine (ChatGPT now favors posts over articles; Gemini barely cites LinkedIn). Front-load the target phrase, since a post's first words become its URL slug. See [references/linkedin-ai-citations.md](references/linkedin-ai-citations.md)
- Review sites (G2, Capterra, TrustRadius for B2B SaaS)
- YouTube (frequently cited by Google AI Overviews)
- Podcasts (episodes get transcribed, show notes published — both get crawled and cited)
- Quora answers

**Actions:**
- Ensure your Wikipedia page is accurate and current
- Participate authentically in Reddit communities — but as one surface in a portfolio, never the whole strategy (citation mixes shift overnight with retrieval updates)
- Get featured in industry roundups and comparison articles
- Make every third-party profile (G2, Capterra, Gartner, Crunchbase, LinkedIn) describe you with the same segment and positioning as your About page; models look for consensus. For repositioning, mergers, and head terms, see [references/positioning-and-consensus.md](references/positioning-and-consensus.md)
- Create YouTube content for key how-to queries — models don't watch the video, they read the text layer around it; see [references/youtube-ai-citations.md](references/youtube-ai-citations.md) for the full anatomy (transcript, captions, chapters, description, pinned comment)
- Guest on podcasts in your category (prep with the public-relations skill's podcast guest prep)
- Answer relevant Quora questions with depth

### Machine-Readable Files for AI Agents

> **Google's stance**: not required for AI Overviews or AI Mode. Their guide explicitly says you don't need new markup, AI files, or markdown to appear in generative AI search.
>
> **Why include them anyway**: non-Google AI engines (ChatGPT, Claude, Perplexity) and autonomous buying agents do reward extractable structure. The files below help with those engines without harming Google.

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

**Audit this layer first**: [references/agent-readiness.md](references/agent-readiness.md) — the access/discovery/parseability checklist, free scoring tools (`npx is-agentic`, Frase's checker), Markdown content negotiation + `Link` headers, `llms-full.txt`, and the emerging agent-*actionable* layer (WebMCP, and a skill file for your product with a prefilled checkout link).

Add these machine-readable files to your site root:

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

```markdown
# 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](references/okf.md).**

### Schema Markup for AI

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

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

No well-documented study isolates a schema lift on AI citations, so treat schema as context that helps engines parse your pages and expect no fixed lift from it. **Google's note**: structured data is "not required for generative AI search" but is recommended for overall SEO strategy. For implementation, use the **schema** skill.

---

## Agentic Experiences

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

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

**What to do:**
- **Render meaningful content without heavy JS gymnastics** — if the page is blank until 4 frameworks finish loading, agents see blank
- **Semantic HTML** — use `<main>`, `<nav>`, `<article>`, `<button>`, proper heading hierarchy, `alt` text on images
- **Clean accessibility tree** — every interactive element labelled; ARIA used correctly (or not at all when native HTML suffices)
- **Stable selectors / predictable layouts** — agents struggle with sites that re-render every interaction
- **Visible pricing, specs, contact info** — anything an agent would need to make a buying recommendation should be on a public, indexable page (this is where `/pricing.md` and similar files help). Date the facts that change ("4.7 on G2 as of October 2026") so an agent can judge freshness
- **Proof as text** — testimonials, ratings, and named customer logos in the HTML as text, not baked into images or injected by a widget script. If you can't select it with the mouse, an agent may not read it

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

For ecom and local business specifically, Google highlights:
- **Merchant Center feeds** + **Google Business Profile** for product/service visibility in AI Search
- **Business Agent** for conversational customer engagement (where applicable)

---

## Content Types That Get Cited Most

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

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

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

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

---

## Monitoring AI Visibility

### What to Track

| Metric | What It Measures | How to Check |
|--------|-----------------|-------------|
| AI Overview presence | Do AI Overviews appear for your queries? | Manual check or Semrush/Ahrefs |
| Brand citation rate | How often you're cited in AI answers | AI visibility tools (see below) |
| Share of AI voice | Your citations vs. competitors | Peec AI, Otterly, ZipTie, Omnia |
| Citation sentiment | How AI describes your brand | Manual review + monitoring tools |
| Recommendation rate | Whether you're on the shortlist, not just cited (see [citations-vs-recommendations.md](references/citations-vs-recommendations.md)) | Prompt tracking + mention framing |
| Source attribution | Which pages get cited, and what AI exposure does downstream | AI referrals plus delayed branded and direct visits ([ai-attribution.md](references/ai-attribution.md)) |

### AI Visibility Monitoring Tools

| Tool | Coverage | Best For |
|------|----------|----------|
| **Otterly AI** | ChatGPT, Perplexity, Google AI Overviews | Share of AI voice tracking |
| **Peec AI** | ChatGPT, Gemini, Perplexity, Claude, Copilot+ | Multi-platform monitoring at scale |
| **ZipTie** | Google AI Overviews, ChatGPT, Perplexity | Brand mention + sentiment tracking |
| **LLMrefs** | ChatGPT, Perplexity, AI Overviews, Gemini | SEO keyword → AI visibility mapping |
| **Profound, Ahrefs Brand Radar, Semrush** | Major assistants and Google AI surfaces | Larger programs: cited sources, answer text, prompt demand (Profound's studies are cited in the references as vendor research) |
| **Omnia** | Google AI Mode, Google AI Overviews, ChatGPT, Perplexity, Gemini, Claude, Copilot | AI visibility monitoring |

### 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? Log it in a spreadsheet and track month-over-month

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

### Search Console expectations

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

---

## What NOT to Do

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

1. **Write separate content "for AI"**. Same content should serve people and AI. Writing variants targeted at AI systems risks the **scaled content abuse spam policy** — Google's words.
2. **Chunk pages into AI-bait fragments**. Google's guide is direct: *"Don't break your content into tiny pieces for AI to better understand it."* Use normal paragraph + heading structure.
3. **Generate at scale for ranking manipulation**. AI-generated content is fine *if* it meets Search Essentials and spam policies. Mass-producing thin variations does not.
4. **Pursue inauthentic mentions**. Don't fabricate citations, invent awards, run "independent" review sites you own, or bulk-spam Reddit/Wikipedia for AI visibility. Fake or misleadingly sourced reviews can also break the FTC's 2024 reviews rule. Real participation only.
5. **Treat every AI user agent as the same control**. `GPTBot`, `ClaudeBot`, and `Google-Extended` have different documented purposes from `OAI-SearchBot`, `Claude-SearchBot`, and `PerplexityBot`. Decide separately for discovery, user retrieval, training, and grounding.
6. **Hide your main content behind JS that doesn't render**. Both core Search and AI agents need to see your content; JS-only rendering loses both audiences.
7. **Skip E-E-A-T fundamentals**. Author identity, first-hand experience, expertise signals, transparent sourcing — Google's guide leans heavily on these for AI features.

---

## AI SEO by Content Type

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

---

## Common Mistakes

- **Ignoring AI search entirely** — AI Overviews appear on roughly half of tracked queries in many industries (BrightEdge, Feb 2026), and assistant usage keeps growing
- **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** — Shortlist recommendations lean on what reviews, forums, and press say about you, even where your own site earns most citations
- **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 discovery crawlers without checking their purpose** — Restricting `OAI-SearchBot`, `PerplexityBot`, or `Claude-SearchBot` may reduce search visibility; training controls are separate
- **Generic content without data** — "We're the best" won't get cited. "Our customers see 3x improvement in [metric]" will
- **Forgetting to monitor** — You can't improve what you don't measure. Check AI visibility monthly at minimum

---

## Tool Integrations

For implementation, see the [tools registry](https://github.com/coreyhaines31/marketingskills/blob/main/tools/REGISTRY.md).

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

---

## Related Skills

- **seo-audit**: For traditional technical and on-page SEO audits; **link-building** for earning links and mentions on the third-party pages AI engines cite
- **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
- **lead-magnets**: For packaging an owned course or content library as an installable agent skill (different from public-site AI visibility)

---

REFERENCE FILE: skills/ai-seo/references/agent-readiness.md

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

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

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

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

For developer documentation specifically, Fern's **Agent Score** grades a docs site for coding agents (run `npx afdocs check <docs-url>` locally first, or score it at buildwithfern.com/agent-score for a public report page).

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

## The AgentReady standard

[AgentReady.org](https://agentready.org) (launched October 21, 2026) is an open spec for what a product should implement to be usable by agents. Ora leads it with a group of collaborators that includes Vercel, Stripe, and Google (current list on the site). It orders practices the way agents meet a site — **find → read → act** — and gives each a stable ID and a strength:

- **MUST** — agents failed without it in the studies. Only three: allow agents through robots.txt and bot protection (AR-FIND-01), put the answer in the initial HTML (AR-READ-01), return real HTTP status codes (AR-READ-02) — plus, *if the product should complete tasks*, an MCP server or CLI (AR-ACT-10).
- **SHOULD** — measurably shaped where agents went (first-party docs, sitemap, homepage links, llms.txt, JSON-LD, fenced code, pricing as text, auth, OpenAPI).
- **MAY** — emerging conventions (Markdown mirrors, AI Catalog).

Cite the IDs in audit findings ("fails AR-READ-01") so fixes map to a public spec rather than your opinion. In October 2026, only about 6% of sites Ora scanned graded B or above, so most sites have real gaps (live figures on the site).

**Why it matters (Ora research, "AX is the New AEO," arXiv:2609.34951, September 2026 — vendor research with a public dataset):**

- When an agent can't read the site, it answers anyway (~99% of dead-end journeys) from web search and other people's pages. Those answers were **3.7× as likely to contain none of the facts asked for**.
- Agent-ready businesses were **clearly recommended 1.9× as often** (20% vs 11%), matched on brand fame and third-party citations.
- **Pricing accuracy was 60% from the site's own pages vs 37% from elsewhere** — the widest gap of any question type. Agents that couldn't read the site punted to "contact sales" more often (22% vs 15%).
- 82% of answers traced to a page the agent fetched; that page was a **docs page 47% of the time** and llms.txt 14%.
- Agents use the same pages people do: docs reached in 83% of runs, homepage 69% (the first fetch 92% of the time), then a cliff — `.well-known/*` 23%, `openapi.json` 21% where shipped, `sitemap.xml` 4%.

Per the skill's evidence order, treat these as observational data from a party with a scanner to sell, not a forecast for any one site.

## The three questions

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

- **Core content in the initial HTML response.** Most agents never execute JavaScript. If the content only exists after client-side rendering, it doesn't exist. This is the #1 essential check in both tools.
- **No bot challenge or firewall block** on the intended discovery path. Blocking `OAI-SearchBot`, `PerplexityBot`, or `Claude-SearchBot` may reduce search visibility. Blocking model-development crawlers such as `GPTBot` or `ClaudeBot` can be an intentional training opt-out and is not, by itself, a visibility failure. Audit what your CDN/WAF actually does to each user agent — many sites block them by default without anyone deciding to. Bot protection is a **separate gate from robots.txt**: a WAF that answers agents with a 403 or a challenge page blocks them however permissive the file is. Let verified agents through by the providers' published IP ranges or Web Bot Auth signatures. In Ora's studies, a bot block was one of only two changes that broke agents outright (the other was a JS-only answer); not-agent-ready sites blocked agents 2.1× as often.
- **Correct HTTP behavior**: real status codes, stable canonical URLs, recoverable errors. Return a 404 (or a redirect) for missing paths — a single-page app that returns 200 for every path makes dead URLs look live to an agent. When throttling, send a **429 with `Retry-After`** instead of a challenge page; agents treat 429 as "back off and return."
- **Pricing as text on a first-party page** — plans, free tier, and limits in the HTML, not an image, a calculator, or a sales form (the accuracy gap above).

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

- **robots.txt with a deliberate AI-crawler stance.** AgentReady's default is one wildcard rule (`User-agent: *`) that allows agents, rather than an allowlist of named crawlers — an allowlist silently excludes agent search APIs (Exa, Tavily, Brave, Parallel) that run their own crawlers. Add named groups only for deliberate exceptions, such as blocking training crawlers (`GPTBot`, `ClaudeBot`) while leaving search (`OAI-SearchBot`, `Claude-SearchBot`) and user-triggered fetchers (`ChatGPT-User`, `Claude-User`) on the wildcard. Two catches: a named group like `User-agent: GPTBot` *replaces* your `*` rules for that bot, so audit existing groups; and user-triggered fetchers may not consult robots.txt at all. See the bot-access guidance in SKILL.md.
- **First-party docs that answer.** Docs were the most-read and most-cited page type in Ora's runs. Put setup, quickstart, and pricing answers on canonical, current docs pages and link them from the homepage.
- **A sitemap that loads and parses cleanly.** Standard for crawlers and search engines, even though agents fetch it directly in only ~4% of runs.
- **Link every discovery file from pages agents already read.** 86–100% of visits to llms.txt, `openapi.json`, `.well-known/*`, and AGENTS.md arrived through a link, not a guessed path. Put them in the footer, a resources section, or `<link>` tags in the homepage head. A file nothing points to is a file nothing fetches.
- **llms.txt at the domain root** (see Machine-Readable Files in SKILL.md).
- **`llms-full.txt`** — the newer companion: your entire site content in one file, so an agent gets everything in a single request instead of crawling. Emerging, cheap to generate alongside llms.txt, and scored as bonus signal by both tools.
- **robots.txt content-usage statements** — an emerging convention for declaring what AI may do with your content (train / cite / summarize), so the answer comes from you instead of being assumed.
- **AI Catalog** (MAY, unmeasured) — a JSON manifest at `/.well-known/ai-catalog.json` listing every agent-facing resource you ship (MCP server card, OpenAPI, A2A agent card, docs, llms.txt, skills), linked from the homepage head with `<link rel="ai-catalog" href="/.well-known/ai-catalog.json">`. Registries index published catalogs so agents can search across sites. Cheap to add once you have more than one agent surface.

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

- **Valid, substantive structured data** (JSON-LD — see the `schema` skill). Most agents read a page's text, not its `<script>` blocks, and Markdown converters may drop them — so **repeat every critical fact as visible text**. A price that exists only in JSON-LD or a hydration payload (e.g. the annual price behind a pricing toggle) goes unseen.
- **Fenced, language-tagged code blocks.** When an agent's answer included code, 61% of the time it lifted lines verbatim from a page on the site.
- **A Markdown representation of the page** (MAY). Agents requested Markdown on ~65% of web fetches, but format did **not** change task success in Ora's controlled experiment — nor did content negotiation or agent detection. Make the HTML work first; add a mirror second. Two implementations:
  - **Content negotiation**: serve compact Markdown at the *same canonical URL* when the request asks for `Accept: text/markdown`, with a `Vary` header keeping the HTML and Markdown cache entries separate. (This is how Is Agentic serves its own reports — agents get Markdown, browsers get HTML, one URL.)
  - **Link header or tag**: an HTTP `Link` header, or `<link rel="alternate" type="text/markdown" href="/page.md">` in the head, pointing to a parallel Markdown version — discoverable without guessing URLs.
- Clear document structure — one H1, headings that answer sub-questions, extractable answer blocks (the content-patterns reference).

## Emerging: agent-actionable, not just agent-readable

Reading is close to solved: in Ora's April 2026 scan, 80% of sites were reachable to every major AI crawler, but only 3% shipped a spec-grade MCP server, and OAuth metadata checks passed on ~1%. AgentReady's act tier applies when the product should *complete* a task, not just be read about:

- **An MCP server or a CLI** (AR-ACT-10, MUST for acting) — the interface an agent acts through. MCP for agents in chat and IDE hosts, a CLI for agents in a shell. Ship the one that fits how the product is used; don't ship an empty surface to tick the box.
- **Auth an agent can finish** (AR-ACT-01) — self-serve API keys with documented scopes; for user-delegated access, OAuth 2.0 with discoverable metadata under `/.well-known` (RFC 8414, RFC 9728).
- **A public API with OpenAPI** (AR-ACT-02) — operation IDs, typed responses and errors, documented rate limits.

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

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

**Ways to add WebMCP** (all early; confirm browser and assistant support before promising anything):
- **Implement it yourself.** Follow the WebMCP draft to declare each action as a tool with an input schema, wired to the form or API the page already uses. Keep the tool list short (the 2–4 highest-intent actions) and reuse existing validation, spam protection, and permissions rather than building a parallel path.
- **Use your platform's support, if it has any.** Some site platforms and agent workflows add WebMCP for you. Ploy ◆, for example, has a Ploybook that inspects the site, defines tool contracts, implements them against existing form logic, and validates them. It's a guided workflow, not a hosted MCP server. ◆ marks a [Verified Partner](https://github.com/coreyhaines31/marketingskills/blob/main/tools/REGISTRY.md#verified-partners) of this repository (disclosure, not endorsement); name an alternative alongside it when you mention it.
- **Have a coding agent do it** in the site's repo, with the same scoping: a few actions, existing logic, a test for each.

Whichever path, test that each tool rejects bad input, that submissions land where the normal form's do (CRM, inbox, conversion tracking), and that nothing bypasses consent or rate limits.

### Ship a skill file for your product

A lighter act surface than MCP or WebMCP: a public `SKILL.md` (linked from llms.txt and your docs) that a user loads into their own Claude Code or Cursor session. `llms.txt` tells an agent what you are; a skill file tells it how to buy or set up your product for this user, and it runs where the user's context already is.

**Case: LinkDR (October 2026).** The backlink marketplace's `linkdr.com/SKILL.md` has the agent inspect the user's site, propose the target page and anchor text, price the order from a published table, and return one checkout link. The founder described it as a sales funnel running inside the buyer's agent. The pattern is the point; the numbers are theirs.

What to put in it:
- **Use the context the agent already has.** Onboarding stalls where the buyer must make a decision they can't make yet (which URL, which plan, which integration). Tell the agent to read the user's repo or site and propose an answer with its reasoning, then let the user confirm.
- **Pricing as text in the file** — the full table and any volume or annual rules, so the agent quotes exact numbers instead of guessing.
- **A prefilled checkout or signup URL.** Once the user agrees, the agent packs the choices into query parameters (`/agent/checkout?plan=pro&seats=5&billing=annual`) and hands back one link. No auth, API keys, or MCP install. Recompute the price on the server and never trust an amount in the URL; validate every parameter; add a source parameter so agent-built orders show up in conversion tracking.
- **An offer for ordering through an agent is fine when it's stated as information** ("code AGENT50 takes $50 off"). The agent shows it; the user decides.
- **Test with the agents your buyers use.** Run the file in a few fresh sessions and check that the agent proposes sensible defaults, quotes the right prices, and builds a valid URL.

**Don't make it coercive.** Instructions that force an upsell on every user, or that are tuned until the agent always pushes your offer regardless of the user's goal, are the manipulation [agent-defaults.md](agent-defaults.md) warns about: agent platforms treat them as prompt injection, and users who notice stop trusting the tool. Recommend the option that fits the user's stated goal and budget, mention one larger option only when it serves that goal, and let the user say no.

For products an agent installs mid-task (developer tools, APIs, infrastructure), the next step past acting on your site is being the vendor the agent picks in the first place. See [agent-defaults.md](agent-defaults.md).

## Citation-source volatility (why you diversify)

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

LinkedIn moved in the same update: ChatGPT's LinkedIn citations fell ~36% in mid-August 2026, and its citations of Pulse articles fell ~86% from May to Oct while citations of feed posts rose. Perplexity and Copilot moved the other way. See [linkedin-ai-citations.md](linkedin-ai-citations.md).

What this means for strategy:

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

---

*Agent-readiness check taxonomy distilled from Vercel/Ora's Is Agentic (is-agentic.com) and Frase's Agent Readiness Checker (both August 2026, credited); AgentReady practice IDs, strengths, and study figures from agentready.org and Ora's research (October 2026, credited — vendor-run, dataset public at github.com/ora/research); citation-volatility events practitioner-reported (Ashni of Hype Partners (@ashnichrist) and others, August 2026) — labeled accordingly, verify against your own monitoring.*

---

REFERENCE FILE: skills/ai-seo/references/ai-attribution.md

# 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](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](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: Marketing Skills by Corey Haines, shared under the MIT License.

MIT License

Copyright (c) 2025 Corey Haines

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.