ai-seo

Generated source view for the actual executable personal/ai-seo skill. The durable routing article is Writing and Content Skills. Source: skills/personal/ai-seo/SKILL.md

Runtime Source

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These files are part of the executable skill folder and must be preserved with the skill source.

Description

When the user wants to optimize content for AI search engines, get cited by LLMs, or appear in AI-generated answers. Also use when the user mentions 'AI SEO,' 'AEO,' 'GEO,' 'LLMO,' 'answer engine optimization,' 'generative engine optimization,' 'LLM optimization,' 'AI Overviews,' 'optimize for ChatGPT,' 'optimize for Perplexity,' 'AI citations,' 'AI visibility,' 'zero-click search,' 'how do I show up in AI answers,' 'LLM mentions,' 'optimize for Claude/Gemini,' 'llms.txt,' 'OKF,' 'Open Knowledge Format,' 'knowledge bundle,' or 'agent-readable site.' 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.

Skill Source

---
name: ai-seo
description: "When the user wants to optimize content for AI search engines, get cited by LLMs, or appear in AI-generated answers. Also use when the user mentions 'AI SEO,' 'AEO,' 'GEO,' 'LLMO,' 'answer engine optimization,' 'generative engine optimization,' 'LLM optimization,' 'AI Overviews,' 'optimize for ChatGPT,' 'optimize for Perplexity,' 'AI citations,' 'AI visibility,' 'zero-click search,' 'how do I show up in AI answers,' 'LLM mentions,' 'optimize for Claude/Gemini,' 'llms.txt,' 'OKF,' 'Open Knowledge Format,' 'knowledge bundle,' or 'agent-readable site.' 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."
origin: personal
source_slug: ai-seo
metadata:
  version: 2.2.0
---

# AI SEO

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

## Before Starting

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

Gather this context (ask if not provided):

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

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

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

### 4. Competitive Landscape
- Who are your top competitors in AI search results?
- Are they being cited where you're not?

---

## How AI Search Works

### The AI Search Landscape

| Platform | How It Works | Source Selection |
|----------|-------------|----------------|
| **Google AI Overviews / AI Mode** | Uses core Google Search ranking and quality systems, including query fan-out | Googlebot crawl/index/snippet eligibility plus people-first Search quality |
| **ChatGPT search** | Searches the public web and cites linked sources | Public crawlability for `OAI-SearchBot`; ranking is product-specific and not guaranteed |
| **Perplexity** | Uses `PerplexityBot` for search discovery and `Perplexity-User` for user-requested retrieval | Search crawlability plus answer-specific source selection |
| **Gemini** | Google's assistant and agent surfaces | Do not infer Google Search eligibility from Gemini behavior; Google-Extended is a separate control |
| **Copilot** | Uses Microsoft search infrastructure | Bing indexability and answer-specific source selection |
| **Claude** | Uses Anthropic search and user-retrieval routes | `Claude-SearchBot` and `Claude-User`; `ClaudeBot` is the separate training crawler |

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 earns discovery and traffic in search results. AI-answer surfaces may add a **mention**, **citation/link**, **impression**, **referral**, or **conversion**. Treat those as different events, not one “AI rank.”

Do not assume an ordinary rank guarantees a citation in another company's product, or that one observed citation is a stable rank. Product, model, query wording, account state, locale, date, retrieval mode, and index coverage can all change an answer.

**Evidence rule:** retain industry studies as hypotheses and benchmarking inputs only when the exact study, sample, date, platform, query panel, and method are recoverable. Never turn a vendor statistic or a single answer screenshot into a universal ranking rule.

### 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
- **Search Console now has dedicated generative-AI performance reports for a subset of sites** — use them when available, while retaining the overall Web/Performance report and Analytics conversion data as the baseline ([Google announcement, 2026-06-03](https://developers.google.com/search/blog/2026/06/gen-ai-performance-reports))

**Other AI products expose different discovery and citation behavior:**
- Clear, self-contained passages, comparison tables, definitions, and accessible public facts make human and agent retrieval easier, but no vendor promises a citation boost for a particular template.
- Files such as `llms.txt` and public machine-readable pricing can improve direct agent usability where a consumer actually reads them; they are not universal ranking controls.
- Third-party mentions can appear in answers, but measure that behavior with a documented prompt panel rather than assuming a fixed source preference.

**What this means for the work:**
- The structural patterns in this skill (direct answers, accurate FAQ content, comparison tables) are human-legibility and agent-usability patterns, not promised ranking factors.
- 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: verify current crawler routes, keep important facts publicly legible, and test any optional machine-readable layer against actual retrieval behavior.

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:**
- Do not create one page per guessed fan-out query. Google explicitly warns against overproducing variants for ranking manipulation.
- Use audience research, Search Console query evidence, and real support/sales questions to make a useful page or small maintained topic cluster more complete.
- Treat fan-out as an explanation of retrieval breadth, not a recipe for predicting hidden queries or guaranteeing inclusion.

---

## AI Visibility Audit

Before optimizing, assess your current AI search presence.

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

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

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

**Query types to test:**
- "What is [your product category]?"
- "Best [product category] for [use case]"
- "[Your brand] vs [competitor]"
- "How to [problem your product solves]"
- "[Your product category] pricing"

### Step 2: Analyze Citation Patterns

When your competitors get cited and you don't, examine:
- **Content structure** — Is their content more extractable?
- **Authority signals** — Do they have more citations, stats, expert quotes?
- **Freshness** — Is their content more recently updated?
- **Schema markup** — Do they have structured data you're missing?
- **Third-party presence** — Are they cited via Wikipedia, Reddit, review sites?

### Step 2.5: Competitor Comparison Capture

Use this when the goal is to win AI Overview, ChatGPT, Perplexity, or "best/alternative/vs" purchase-intent answers.

1. Pick 3-6 named competitors users already compare against.
2. Actually use each product or service. Capture screenshots, pricing, missing features, onboarding friction, and standout strengths.
3. Pull external review language from G2, Capterra, Product Hunt, Reddit, app stores, and support forums.
4. Ship one exact-match comparison page (`brand-vs-competitor`) and one standalone review page per competitor.
5. Lead with a direct verdict, then add a side-by-side table, dated pricing, best-for guidance, honest caveats, FAQs, and a clear CTA.
6. Re-check Google AI Overview / AI Mode, ChatGPT, Perplexity, and Gemini for the exact queries after indexing.

Do not fake the product experience. AI answers and humans both reward pages that contain firsthand screenshots, specific tradeoffs, and language users already use in reviews.

### Step 3: Content Extractability Check

For each priority page, verify:

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

### Step 4: AI Bot Access Check

Separate search discovery, user-directed retrieval, and model-training controls. They are not interchangeable:

| Surface | Search/discovery | User-directed fetch | Training or non-Search control |
| --- | --- | --- | --- |
| OpenAI | `OAI-SearchBot` | `ChatGPT-User` | `GPTBot` |
| Anthropic | `Claude-SearchBot` | `Claude-User` | `ClaudeBot` |
| Perplexity | `PerplexityBot` | `Perplexity-User` | The official guide says these two are not training crawlers |
| Google Search AI features | `Googlebot` and Search preview controls | Google Search behavior | `Google-Extended` controls specified Gemini training/grounding uses and has no effect on Google Search |
| Microsoft Copilot | `Bingbot` / Bing index controls | Product-dependent | Verify current Microsoft documentation before changing policy |

Check both `robots.txt` and CDN/WAF/server receipts. A crawler being allowed by `robots.txt` does not prove the edge accepted it. Decide training permissions separately from search visibility; for example, OpenAI explicitly separates `GPTBot` from `OAI-SearchBot`, and Anthropic separates `ClaudeBot` from `Claude-SearchBot`.

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

Search and answer products may surface passages from pages. Important claims should be understandable in context and remain accurate when quoted, without turning the page into disconnected fragments.

**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 answer passages as short as the subject allows; there is no universal optimal word count
- Use H2/H3 headings that match how people phrase queries
- Use tables when columnar comparison is genuinely clearer than prose
- Use numbered lists for ordered procedures
- Each paragraph should convey one clear idea

### Pillar 2: Authority — Make Content Citable

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

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

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

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

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

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

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

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

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

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

**Third-party sources matter more than your own site:**
- Wikipedia mentions (7.8% of all ChatGPT citations)
- Reddit discussions (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

```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/` or `/api/okf` — Open Knowledge Format bundle (Google-backed, v0.2)**

Google [introduced OKF](https://cloud.google.com/blog/products/data-analytics/how-the-open-knowledge-format-can-improve-data-sharing) in June 2026; the [current v0.2 specification](https://github.com/GoogleCloudPlatform/knowledge-catalog/blob/main/okf/SPEC.md) represents a site or knowledge base as a portable tree of cross-linked Markdown concepts. It adds explicit sources, producer/verification separation, lifecycle, freshness, and optional attested computations. No confirmed AI-search ranking signal follows from publishing it: treat OKF as a machine-consumption and interoperability surface, not an SEO boost. Generate it from canonical public content, reuse the site's privacy policy, advertise the root in `llms.txt`, and validate the bundle on every content build. **For the exact v0.2 fields, migration notes, honest adoption boundary, and implementation checklist, 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 |

Use schema when it accurately describes visible content and supports ordinary search/product requirements. **Google's note**: structured data is not required for generative AI search and there is no special AI schema. Do not promise a citation lift from adding schema alone. For implementation, use the **schema** skill.

---

## Agentic Experiences

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

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

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

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

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

---

## Content Types That Get Cited Most

Not all content is equally citable. Prioritize these formats:

| Content Type | Citation Share | Why AI Cites It |
|-------------|:------------:|----------------|
| **Comparison articles** | ~33% | Structured, balanced, high-intent |
| **Definitive guides** | ~15% | Authoritative, source-backed |
| **Original research/data** | ~12% | Unique, citable statistics |
| **Best-of/listicles** | ~10% | Clear structure, entity-rich |
| **Product pages** | ~10% | Specific details AI can extract |
| **How-to guides** | ~8% | Step-by-step structure |
| **Opinion/analysis** | ~10% | Expert perspective, quotable |

**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)

---

## Monitoring AI Visibility

### What to Track

| Metric | What It Measures | How to Check |
|--------|-----------------|-------------|
| Google generative-AI impressions | Where eligible URLs appeared in Google's AI features | Dedicated Search Console generative-AI report when available; overall Web report remains baseline |
| Mention observation | Whether the answer names the brand | Repeated prompt panel with query/model/date/locale/account/mode recorded |
| Citation/link observation | Whether the answer links a specific page | Repeated prompt panel plus captured answer/source URL |
| Referral traffic | Visits sent by an AI/search surface | First-party analytics and tagged/referrer evidence |
| Conversion/value | Whether referred users complete the business outcome | First-party product, commerce, or CRM events |
| Third-party visibility score | A vendor's modeled observation over its own query panel | Require disclosed panel/method; never treat as an engine's internal metric |

### AI Visibility Monitoring Tools

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

### DIY Monitoring (No Tools)

Monthly manual check:
1. Pick a stable panel of priority queries tied to real audience intent.
2. Run each through the relevant products.
3. Record exact query, product/model, date/time, locale, account state, mode, answer, mention, citation URL, and visible competitors.
4. Repeat enough trials to identify volatility; preserve screenshots or exports.
5. Join observations to first-party referral and conversion data. Track change over time without claiming causality unless the design supports it.

### Search Console expectations

Google launched dedicated Search and Discover generative-AI performance reports on June 3, 2026 for a subset of properties. When available, use the dedicated report for impressions, pages, countries, devices where supported, and time trends. The same data remains included in the overall report, so retain ordinary Search Console and Analytics measurement as the baseline. For other companies' products, use first-party referral/conversion data plus a reproducible prompt panel; third-party tools are optional observers, not the only evidence and not access to internal ranking systems.

---

## 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. **Conflate training and search controls**. `GPTBot`, `ClaudeBot`, and Google-Extended are not the search-inclusion controls for ChatGPT search, Claude search, or Google Search AI features. Set policies per documented surface, then verify actual crawl/fetch behavior at the edge.
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 audience behavior** — allocate work from actual channel usage, referral, and conversion evidence rather than category hype
- **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
- **Stale time-sensitive content** — show an honest update date and refresh facts when they materially change; do not churn dates without substantive work
- **Gating all content** — AI can't access gated content. Keep your most authoritative content open
- **Ignoring third-party evidence** — accurate independent reviews, documentation, reporting, and community discussion can shape how a brand is understood
- **Misusing structured data** — use schema that matches visible content and ordinary search requirements; do not claim it guarantees AI citations
- **Keyword stuffing** — it reduces clarity and can violate search-quality expectations; write for the user and topic
- **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
- **Using stale bot names** — search, user-retrieval, and training crawlers are separate; verify current official documentation and edge receipts
- **Generic or unsupported claims** — “we're the best” and an unverified “3x improvement” are both weak; publish reproducible evidence and limitations
- **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](../../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 |

---

## Task-Specific Questions

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

---

## Related Skills

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

---

Timeline

• 2026-09-25 | Generated a browseable source page from the actual executable skill file. Source: skills/personal/ai-seo/SKILL.md