| name | aeo-geo-optimizer |
| description | Answer Engine Optimization (AEO) and Generative Engine Optimization (GEO) specialist. Optimize content and websites to appear in AI-generated answers from ChatGPT, Perplexity, Claude, Google AI Overviews, and other LLM-powered search experiences. Use when the user asks about AI search optimization, AEO, GEO, AI Overviews, appearing in AI answers, LLM citations, or optimizing for generative search. |
| license | MIT |
| origin | custom |
| author | Rebecca Rae Barton |
| author_url | https://github.com/thatrebeccarae |
| metadata | {"version":"1.0.0","category":"seo","domain":"ai-search-optimization","updated":"2026-03-18T00:00:00.000Z","tested":"2026-03-18T00:00:00.000Z","tested_with":"Claude Code v2.1"} |
AEO/GEO Optimizer
Optimize content and websites for AI-powered search experiences — ChatGPT, Perplexity, Claude, Google AI Overviews, and Bing Copilot.
Install
git clone https://github.com/thatrebeccarae/claude-marketing.git && cp -r claude-marketing/skills/aeo-geo-optimizer ~/.claude/skills/
Why This Matters
Traditional SEO optimizes for 10 blue links. AEO/GEO optimizes for AI-generated answers. When someone asks ChatGPT or Perplexity a question, the answer synthesizes from sources — and those sources get cited, linked, and trusted. If your content is not structured for AI consumption, you are invisible in the fastest-growing search channel.
Core Concepts
AEO vs GEO vs Traditional SEO
| Dimension | Traditional SEO | AEO (Answer Engine) | GEO (Generative Engine) |
|---|
| Target | Google/Bing SERPs | Featured snippets, AI Overviews, voice assistants | ChatGPT, Perplexity, Claude citations |
| Goal | Rank on page 1 | Be THE answer | Be cited in AI-generated responses |
| Content format | Long-form, keyword-rich | Concise, structured Q&A | Authoritative, quotable, fact-dense |
| Signals | Backlinks, keywords, UX | Schema markup, direct answers, authority | E-E-A-T, data density, citation-worthiness |
| Measurement | Rankings, traffic | Answer box appearance, voice search hits | AI citation tracking, brand mentions in AI |
The Citation Hierarchy
AI models prioritize sources based on:
- Authority signals — Domain authority, author expertise, institutional backing
- Content structure — Clear headings, direct answers, structured data
- Freshness — Recent publication dates, updated statistics
- Specificity — Exact numbers, named sources, verifiable claims
- Uniqueness — Original research, proprietary data, novel frameworks
AEO/GEO Audit Workflow
Step 1: Assess Current AI Visibility
-
Test AI citation presence: Query ChatGPT, Perplexity, and Google AI Overviews with questions your content should answer. Document which queries cite your content vs competitors.
-
Check structured data: Validate schema markup coverage using Google Rich Results Test or Schema.org validator.
-
Evaluate content structure: Score each page on AEO readiness using the Content Scorecard below.
Step 2: Content Scorecard
Rate each piece of content (1-5) on these dimensions:
| Dimension | Score 1 (Poor) | Score 5 (Excellent) |
|---|
| Direct answers | Buried in paragraphs | Clear Q&A format, first-sentence answers |
| Data density | Opinions without evidence | Specific numbers, percentages, dates |
| Source attribution | No citations | Named sources, linked studies |
| Structure | Wall of text | H2/H3 hierarchy, lists, tables |
| Schema markup | None | Article, FAQ, HowTo, or relevant type |
| Freshness signals | No dates | Published date, "Updated" date, recent data |
| Author authority | No byline | Named author with expertise credentials |
| Quotability | Meandering prose | Crisp, self-contained statements AI can extract |
Scoring: 32-40 = AI-ready. 24-31 = Needs optimization. Below 24 = Major rework needed.
Step 3: Optimize for AI Citation
Content Structure Patterns
The Direct Answer Pattern:
## [Question as H2]
[One-sentence direct answer.] [Supporting context in 2-3 sentences.]
**Key details:**
- [Specific data point]
- [Specific data point]
- [Source attribution]
The Definition Pattern:
## What Is [Term]?
[Term] is [clear, concise definition in one sentence]. [Elaboration with context.] [How it differs from related concepts.]
The Comparison Pattern:
## [X] vs [Y]: Key Differences
| Dimension | [X] | [Y] |
|-----------|-----|-----|
| [Aspect 1] | [Specific detail] | [Specific detail] |
| [Aspect 2] | [Specific detail] | [Specific detail] |
**Bottom line:** [One-sentence recommendation with reasoning.]
The Statistics Pattern:
## [Topic] Statistics ([Year])
- **[Stat 1]**: [Number] ([Source, Year])
- **[Stat 2]**: [Number] ([Source, Year])
- **[Stat 3]**: [Number] ([Source, Year])
*Sources: [List with links]*
Writing for AI Extraction
- Lead with the answer. AI models extract the first sentence after a heading. Make it count.
- Use specific numbers. "Revenue increased 47% year-over-year" beats "revenue increased significantly."
- Name your sources. "According to a 2026 McKinsey report" is citable; unsourced claims are not.
- Create self-contained paragraphs. Each paragraph should make sense extracted in isolation.
- Use comparison tables. AI models love structured comparisons — they are easy to synthesize.
- Include "What is" and "How to" headings. These directly match common AI queries.
- Add freshness signals. Include publication date, last-updated date, and date-stamp your statistics.
- Write quotable sentences. Crisp, declarative statements that AI can extract verbatim.
Technical Optimization
- Schema markup — See the schema-markup-generator skill for implementation
- Canonical URLs — Ensure AI models find the authoritative version
- XML sitemap — Keep it current so AI crawlers find new content
- Page speed — AI crawlers respect crawl budgets; fast sites get crawled more
- robots.txt — Ensure AI crawlers (GPTBot, anthropic-ai, PerplexityBot) are not blocked
Step 4: Monitor AI Visibility
AI Crawler User Agents
| Crawler | User Agent | Purpose |
|---|
| OpenAI | GPTBot | ChatGPT training and browsing |
| Anthropic | anthropic-ai, ClaudeBot | Claude training and citations |
| Perplexity | PerplexityBot | Perplexity search citations |
| Google | Google-Extended | Gemini/AI Overview training |
| Microsoft | Bingbot (+ AI signals) | Bing Copilot citations |
| Meta | Meta-ExternalAgent | Meta AI features |
| Apple | Applebot-Extended | Apple Intelligence |
robots.txt Recommendations
# Allow AI crawlers for maximum AI search visibility
User-agent: GPTBot
Allow: /
User-agent: anthropic-ai
Allow: /
User-agent: ClaudeBot
Allow: /
User-agent: PerplexityBot
Allow: /
User-agent: Google-Extended
Allow: /
Decision framework: If your goal is AI visibility (AEO/GEO), allow all AI crawlers. If you have licensing concerns about training data, selectively block training-only crawlers while allowing search/citation crawlers.
Measurement Approaches
| Method | What It Tracks | Tools |
|---|
| Manual citation checks | Query AI platforms, document citations | ChatGPT, Perplexity, Google |
| Server log analysis | AI crawler frequency and pages crawled | Log analyzers, custom scripts |
| Brand mention monitoring | Your brand/content mentioned in AI answers | Manual checks, brand monitoring tools |
| Referral traffic | Traffic from AI platforms | GA4 (check referral sources for chat.openai.com, perplexity.ai) |
| Schema validation | Structured data coverage and errors | Google Search Console, Rich Results Test |
Content Types and AI Optimization
Blog Posts / Articles
- Add FAQ schema for common questions
- Structure with clear H2 question headings
- Include "Key Takeaways" or "TL;DR" section
- Date-stamp all statistics
Product / Service Pages
- Add Product or Service schema
- Include comparison tables vs alternatives
- Answer "What is [product]?" in first paragraph
- List specific features with quantified benefits
Documentation / How-To Content
- Add HowTo schema with explicit steps
- Number every step
- Include time estimates and difficulty level
- Add "Prerequisites" and "Common Mistakes" sections
Research / Data Content
- Add Dataset schema where applicable
- Lead with key findings before methodology
- Create a "Key Statistics" summary section
- Cite sample sizes, date ranges, and confidence levels
Anti-Patterns (Never Do)
- Do not block AI crawlers if your goal is AI visibility
- Do not write "click here" or "read more below" — AI extracts content out of context
- Do not bury answers in long introductions — lead with the answer
- Do not use vague qualifiers — "many," "significant," "some" — use specific numbers
- Do not neglect author bylines — E-E-A-T signals matter to AI models
- Do not duplicate content across pages — AI models deduplicate and may ignore both
- Do not over-optimize for one AI platform — optimize for all of them
- Do not forget internal linking — AI crawlers follow links to build topical authority maps
Integration with Other Skills
- technical-seo-audit — Run technical audit first, then layer AEO/GEO optimization
- schema-markup-generator — Generate the structured data this skill recommends
- seo-content-writer — Apply AEO writing patterns during content creation
- content-creator — Use brand voice analysis to maintain voice while optimizing for AI
