geo-audit

# GEO Audit Orchestration Skill

## Purpose

This skill performs a comprehensive Generative Engine Optimization (GEO) audit of any website. GEO is the practice of optimizing web content so that AI systems (ChatGPT, Claude, Perplexity, Gemini, etc.) can discover, understand, cite, and recommend it. This audit measures how well a site performs across all GEO dimensions and produces an actionable improvement plan.

## Key Insight

Traditional SEO optimizes for search engine rankings. GEO optimizes for AI citation and recommendation. Sites that score high on GEO metrics see 30-115% more visibility in AI-generated responses (Georgia Tech / Princeton / IIT Delhi 2024 study). The two disciplines overlap but have distinct requirements.

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## Audit Workflow

### Phase 1: Discovery and Reconnaissance

**Step 1: Fetch Homepage and Detect Business Type**

1. Use WebFetch to retrieve the homepage at the provided URL.
2. Extract the following signals:
   - Page title, meta description, H1 heading
   - Navigation menu items (reveals site structure)
   - Footer content (reveals business info, location, legal pages)
   - Schema.org markup on homepage (Organization, LocalBusiness, etc.)
   - Pricing page link (SaaS indicator)
   - Product listing patterns (E-commerce indicator)
   - Blog/resource section (Publisher indicator)
   - Service pages (Agency indicator)
   - Address/phone/Google Maps embed (Local business indicator)

3. Classify the business type using these patterns:

| Business Type | Detection Signals |
|---|---|
| **SaaS** | Pricing page, "Sign up" / "Free trial" CTAs, app.domain.com subdomain, feature comparison tables, integration pages |
| **Local Business** | Physical address on homepage, Google Maps embed, "Near me" content, LocalBusiness schema, service area pages |
| **E-commerce** | Product listings, shopping cart, product schema, category pages, price displays, "Add to cart" buttons |
| **Publisher** | Blog-heavy navigation, article schema, author pages, date-based archives, RSS feeds, high content volume |
| **Agency/Services** | Case studies, portfolio, "Our Work" section, team page, client logos, service descriptions |
| **Hybrid** | Combination of above signals -- classify by dominant pattern |

**Step 2: Crawl Sitemap and Internal Links**

1. Attempt to fetch `/sitemap.xml` and `/sitemap_index.xml`.
2. If sitemap exists, extract up to 50 unique page URLs prioritized by:
   - Homepage (always include)
   - Top-level navigation pages
   - High-value pages (pricing, about, contact, key service/product pages)
   - Blog posts (sample 5-10 most recent)
   - Category/landing pages
3. If no sitemap exists, crawl internal links from the homepage:
   - Extract all `<a href>` links pointing to the same domain
   - Follow up to 2 levels deep
   - Prioritize pages linked from main navigation
4. Respect `robots.txt` directives -- do not fetch disallowed paths.
5. Enforce a maximum of 50 pages and a 30-second timeout per fetch.

**Step 3: Collect Page-Level Data**

For each page in the crawl set, record:
- URL, title, meta description, canonical URL
- H1-H6 heading structure
- Word count of main content
- Schema.org types present
- Internal/external link counts
- Images with/without alt text
- Open Graph and Twitter Card meta tags
- Response status code
- Whether the page has structured data

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### Phase 2: Parallel Subagent Delegation

Delegate analysis to 5 specialized subagents. Each subagent operates on the collected page data and produces a category score (0-100) plus findings.

**Subagent 1: AI Visibility Analysis (geo-ai-visibility)**
- Analyze content blocks for quotability by AI systems (citability scoring)
- Check AI crawler access via robots.txt and llms.txt presence
- Scan brand presence across YouTube, Reddit, Wikipedia, LinkedIn
- Score brand authority signals that AI models use for entity recognition

**Subagent 2: Platform Optimization (geo-platform-analysis)**
- Assess readiness for Google AI Overviews, ChatGPT, Perplexity, Gemini, Bing Copilot
- Check platform-specific ranking factors and optimization opportunities

**Subagent 3: Technical GEO Infrastructure (geo-technical)**
- Analyze robots.txt for AI crawler access
- Verify meta tags, headers, and technical accessibility for AI systems
- Check page speed, server-side rendering, and Core Web Vitals
- Assess security headers and mobile optimization

**Subagent 4: Content E-E-A-T Quality (geo-content)**
- Evaluate Experience, Expertise, Authoritativeness, Trustworthiness signals
- Check author bios, credentials, source citations
- Assess content freshness, depth, and originality
- Verify "About" page quality and team credentials

**Subagent 5: Schema & Structured Data (geo-schema)**
- Validate all schema.org markup
- Check for GEO-critical schema types (FAQ, HowTo, Organization, Product, Article)
- Assess schema completeness and accuracy
- Identify missing schema opportunities

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### Phase 3: Score Aggregation and Report Generation

#### Composite GEO Score Calculation

The overall GEO Score (0-100) is a weighted average of six category scores:

| Category | Weight | What It Measures |
|---|---|---|
| **AI Citability** | 25% | How quotable/extractable content is for AI systems |
| **Brand Authority** | 20% | Third-party mentions, entity recognition signals |
| **Content E-E-A-T** | 20% | Experience, Expertise, Authoritativeness, Trustworthiness |
| **Technical GEO** | 15% | AI crawler access, llms.txt, rendering, speed |
| **Schema & Structured Data** | 10% | Schema.org markup quality and completeness |
| **Platform Optimization** | 10% | Presence on platforms AI models train on and cite |

**Formula:**
```
GEO_Score = (Citability * 0.25) + (Brand * 0.20) + (EEAT * 0.20) + (Technical * 0.15) + (Schema * 0.10) + (Platform * 0.10)
```

#### Score Interpretation

| Score Range | Rating | Interpretation |
|---|---|---|
| 90-100 | Excellent | Top-tier GEO optimization; site is highly likely to be cited by AI |
| 75-89 |