ai-seo

What this skill does

# AI SEO

Generative engine optimization (GEO) for getting cited by AI search platforms — not just ranked in traditional results.

---

## Table of Contents

- [Keywords](#keywords)
- [Quick Start](#quick-start)
- [How AI Search Differs from Traditional SEO](#how-ai-search-differs-from-traditional-seo)
- [The Three Pillars of AI Citability](#the-three-pillars-of-ai-citability)
- [Core Workflows](#core-workflows)
- [Content Patterns That Get Cited](#content-patterns-that-get-cited)
- [Schema Markup for AI Discovery](#schema-markup-for-ai-discovery)
- [Bot Access Configuration](#bot-access-configuration)
- [Monitoring and Tracking](#monitoring-and-tracking)
- [Best Practices](#best-practices)
- [Integration Points](#integration-points)

---

## Keywords

AI SEO, generative engine optimization, GEO, AI overviews, Google SGE, ChatGPT citations, Perplexity SEO, Claude citations, AI search optimization, semantic search, entity optimization, LLM visibility, AI-generated answers, structured data, schema markup, content extractability, AI citability, GPTBot, PerplexityBot, ClaudeBot, answer engine optimization

---

## Quick Start

### Run an AI Visibility Audit

1. Check robots.txt for AI bot access (GPTBot, PerplexityBot, ClaudeBot)
2. Test top 10 target queries on Perplexity, ChatGPT, and Google AI Overviews
3. Document which queries cite you, which cite competitors, and what content format wins
4. Score key pages against the Extractability Checklist
5. Prioritize pages with highest gap between search volume and current AI citation presence

### Optimize a Page for AI Citation

1. Add a clear definition block in the first 200 words for informational queries
2. Structure content with self-contained H2 sections that can be extracted independently
3. Add numbered steps for process queries, comparison tables for "X vs Y" queries
4. Replace all vague claims with attributed statistics ("According to [Source], [Year]")
5. Implement FAQPage, HowTo, or Article schema markup
6. Verify AI bots are allowed in robots.txt

---

## How AI Search Differs from Traditional SEO

### The Fundamental Shift

Traditional SEO gets your page ranked. AI SEO gets your content cited. These are different optimization targets.

| Dimension | Traditional SEO | AI SEO |
|-----------|----------------|--------|
| Goal | Rank on page 1 | Get cited in AI-generated answers |
| Success metric | Click-through rate | Citation frequency |
| Content priority | Keyword density | Answer extractability |
| Authority signal | Backlinks + domain authority | Backlinks + answer quality + attribution |
| User interaction | User clicks your link | AI extracts your answer; user may never visit |
| Content format | Long-form comprehensive | Self-contained extractable blocks |
| Optimization unit | The page | The paragraph or section |

### What Carries Over from Traditional SEO

- Domain authority still matters. AI systems prefer credible sources.
- Backlinks still signal trust and expertise.
- Technical SEO fundamentals (page speed, mobile-friendly, clean HTML) still apply.
- Quality content with original insights still wins.

### What Changes

- Keyword density matters less than answer clarity and directness
- Page-level optimization expands to section-level and paragraph-level optimization
- Internal linking serves discoverability for AI crawlers, not just PageRank flow
- Structured data becomes a primary signal, not a nice-to-have

---

## The Three Pillars of AI Citability

### Pillar 1: Structure (Extractable)

AI systems pull content in chunks. They find the paragraph, list, or definition that directly answers a query and extract it. Your content must be structured so answers are self-contained.

**Extractability requirements:**
- Definition blocks for "what is X" queries — tight, 1-2 sentence definitions in the first 200 words
- Numbered steps for "how to do X" queries — verb-first, self-contained steps
- Comparison tables for "X vs Y" queries — clean table format with headers
- FAQ blocks for question-based queries — explicit Q&A pairs
- Statistics with full attribution for data-oriented queries

**Anti-patterns that kill extractability:**
- Burying the answer in paragraph 8 of a 4,000-word essay
- Requiring context from previous sections to understand any individual section
- Using narrative prose for comparisons that should be tables
- Placing key definitions only in the conclusion

### Pillar 2: Authority (Citable)

AI systems do not just extract the most relevant answer — they extract the most credible one.

**Authority signals in the AI era:**
- **Domain authority** — High-DA domains get preferential citation
- **Author attribution** — Named authors with credentials outperform anonymous pages
- **Citation chains** — Your content cites credible sources, making you credible in turn
- **Recency** — AI systems prefer current information for time-sensitive queries
- **Original data** — Proprietary research, surveys, and studies get cited more because AI cannot find this data elsewhere
- **Consistent entity presence** — Your brand appears across authoritative sources as an entity

### Pillar 3: Presence (Discoverable)

AI systems must be able to find and index your content.

**Technical requirements:**
- AI crawlers allowed in robots.txt
- Fast page load and clean HTML
- No JavaScript-only rendering for important content
- Schema markup for content type classification
- Proper canonical signals
- HTTPS with valid certificates

---

## Core Workflows

### Workflow 1: AI Visibility Audit

**Step 1: Bot Access Verification**

Check robots.txt for AI crawler permissions:

```
# These bots must NOT be blocked for AI visibility:
GPTBot          # OpenAI / ChatGPT
PerplexityBot   # Perplexity
ClaudeBot       # Anthropic / Claude
Google-Extended # Google AI Overviews
anthropic-ai    # Anthropic (alternate)
Applebot-Extended  # Apple Intelligence
cohere-ai       # Cohere
```

If any AI bot is blocked, that is the single highest priority fix. Zero visibility on that platform until resolved.

**Step 2: Citation Testing**

Test top 10 target queries on each platform:

| Platform | How to Test | What to Record |
|----------|-------------|----------------|
| Perplexity | Search at perplexity.ai, check Sources panel | Cited? Which competitors cited? Content format winning? |
| ChatGPT | Web browsing enabled, check citations | Same |
| Google AI Overviews | Google query, check AI Overview panel | Same |
| Microsoft Copilot | Search at copilot.microsoft.com, check source cards | Same |
| Claude | Web search enabled queries | Same |

**Step 3: Content Extractability Scoring**

Score each key page (0-7):

- [ ] Clear definition of core concept in first 200 words
- [ ] Numbered lists or step-by-step sections for process queries
- [ ] FAQ section with direct Q&A pairs
- [ ] Statistics cited with source name and year
- [ ] Comparisons in table format (not narrative)
- [ ] H1 phrased as an answer or direct statement
- [ ] Schema markup present (FAQPage, HowTo, Article)

Interpretation: 0-3 = needs major restructuring. 4-5 = good baseline. 6-7 = strong.

**Step 4: Competitive Citation Analysis**

For each target query, document:
- Who is currently being cited (top 3 sources per platform)
- What content format wins (definition, list, table, quote)
- What your content lacks that cited competitors provide
- Where you have unique data or expertise competitors lack

### Workflow 2: Page Optimization for AI Citation

**Step 1: Lead with the Answer**

The first paragraph must contain the core answer to the target query. No preamble, no context-setting, no "In today's landscape..." openers.

**Step 2: Structure Self-Contained Sections**

Every H2 section must be answerable as a standalone excerpt:
- Each section opens with its main point
- Each section contains its own evidence
- No section requires reading previous sections to be understood
- Each section could be quoted out of context and still make sense

**Step 3: Add Extract