geo-review

What this skill does

# GEO Review

Evaluate how well your application and content are optimized for AI-powered search and answer engines — ChatGPT, Perplexity, Google AI Overviews, Claude, and other generative AI systems that cite web sources. Traditional SEO gets you ranked in a link list; GEO gets you **cited in AI-generated answers**.

## When to use

Use `/geo-review` when:
- Your product is discovered through AI assistants (developer tools, SaaS, APIs)
- You want to appear in Google AI Overviews
- Users find your product by asking AI "what's the best X for Y?"
- You publish documentation, guides, or educational content
- Your competitors are showing up in AI answers and you're not
- Building thought leadership content that AI should reference
- Launching a new product where AI-driven discovery matters

## Why GEO Matters Now

- **40% of Gen Z** uses TikTok and AI chatbots instead of Google for search (Adobe 2024)
- **Google AI Overviews** now appear for ~30% of search queries, pushing traditional results below the fold
- **Perplexity** processes 100M+ queries/month, citing web sources in every answer
- **ChatGPT with browsing** and search is becoming a primary research tool
- AI systems don't rank links — they **select and cite sources** based on different signals than traditional SEO
- Being the source an AI quotes is the new "position #1"

## Standards & Frameworks Referenced

- **GEO research** (Georgia Tech / Princeton / IIT Delhi, 2024) — "GEO: Generative Engine Optimization"
- **Google E-E-A-T** — Experience, Expertise, Authoritativeness, Trustworthiness
- **Schema.org** — Structured data for entity understanding
- **llms.txt** — Emerging standard for AI crawler instructions (similar to robots.txt for LLMs)
- **Retrieval-Augmented Generation (RAG)** — How AI systems fetch and cite content

## Phase Overview

```
Phase 1: EDUCATE   → How AI search works differently from traditional search
Phase 2: SCOPE     → Identify content types, target queries, AI visibility goals
Phase 3: ANALYZE   → Content analysis + browser-based AI search validation
Phase 4: REPORT    → Findings with citation gap analysis and confidence scores
Phase 5: REMEDIATE → Fix guidance + YAML regression tests
```

---

## Phase 1: Educate

> **How AI search is different:** Traditional search engines crawl, index, and rank pages by relevance signals (backlinks, keywords, authority). AI answer engines do something fundamentally different — they retrieve content, understand it semantically, and synthesize answers by selecting the most citation-worthy sources. Your content needs to be **clear, specific, authoritative, and directly answerable** to be selected.

> **Key insight:** AI systems prefer content that makes **specific, verifiable claims** with **supporting evidence**. Vague marketing copy is ignored. Concrete statements with data, comparisons, and clear structure get cited.

---

## Phase 2: Scope

### Gather context

1. **Auto-detect from codebase/content:**
   - Content pages (docs, blog, landing pages, about, pricing, FAQ)
   - Existing structured data (JSON-LD, Schema.org)
   - Content management approach (static, CMS, MDX, etc.)
   - llms.txt presence
   - Sitemap and content organization
   - Author/expertise signals
   - Publication dates and freshness signals

2. **Ask the user** (one at a time):
   - **Product type**: What does your product/site do? (needed to understand AI query context)
   - **Target URL**: Where is the content published?
   - **Target AI queries**: What questions should AI answer with your content? (e.g., "best CI/CD tool for startups", "how to implement OAuth in Node.js")
   - **Competitors**: Who else shows up when AI answers these queries? (optional but valuable)
   - **Content goals**: Documentation? Thought leadership? Product discovery? All of the above?

3. **Map content landscape:**
   - Key content pages and their purpose
   - Target queries each page should satisfy
   - Current AI citation status (test a few queries in ChatGPT/Perplexity)
   - Content gaps vs competitors

---

## Phase 3: Analyze

Open a browser session with `new_session` using `record_evidence: true`. Run all applicable check categories.

### Category A: Content Citation-Worthiness (CITE)

| Check ID | Check | Principle | Method |
|----------|-------|-----------|--------|
| CITE-01 | Content contains specific, verifiable claims | GEO research | Scan pages for concrete statements with data/numbers |
| CITE-02 | Statistics and original data are present | GEO research | Check for unique numbers, benchmarks, research findings |
| CITE-03 | Content directly answers target queries | RAG retrieval | Match content against target queries — does it contain direct answers? |
| CITE-04 | Claims have supporting evidence or citations | E-E-A-T | Check for source references, links, data attribution |
| CITE-05 | Content is specific (not generic/vague) | GEO research | Analyze content for specificity vs marketing fluff |
| CITE-06 | Comparison content exists (vs alternatives) | AI preference | Check for "X vs Y" or comparison tables that AI can cite |
| CITE-07 | Content has clear, quotable summary sentences | Citation format | Check if key paragraphs start with citable claims |
| CITE-08 | Unique perspective or data (not regurgitated) | E-E-A-T | Assess originality — does this add something AI can't already synthesize? |
| CITE-09 | Content demonstrates first-hand experience | E-E-A-T (Experience) | Check for case studies, personal experience, real examples |
| CITE-10 | Technical accuracy and depth | E-E-A-T (Expertise) | Assess whether content goes beyond surface level |

**Browser validation:** Navigate to content pages. Extract text content. Analyze for claim density, statistics, quotable statements. Compare against target queries for direct answer matching.

### Category B: Content Structure for AI Retrieval (STRUCT)

| Check ID | Check | Principle | Method |
|----------|-------|-----------|--------|
| STRUCT-01 | Clear heading hierarchy maps to questions | RAG chunking | Check if H2/H3 headings are question-shaped or topic-clear |
| STRUCT-02 | FAQ sections with direct Q&A format | AI preference | Check for FAQ sections, question-answer pairs |
| STRUCT-03 | Definition/explanation paragraphs lead with the answer | Retrieval | Check if paragraphs front-load the key claim (inverted pyramid) |
| STRUCT-04 | Tables and structured comparisons present | AI preference | Check for HTML tables with clear headers |
| STRUCT-05 | Content is chunked into digestible sections (300-500 words) | RAG chunking | Measure section lengths between headings |
| STRUCT-06 | Lists used for multi-point information | AI preference | Check for ordered/unordered lists for multi-step or multi-item content |
| STRUCT-07 | Code examples are complete and runnable (for technical content) | Developer experience | Check code blocks for completeness and language tags |
| STRUCT-08 | TL;DR or summary at top of long content | Retrieval | Check for executive summary or key takeaways section |

**Browser validation:** Extract heading structure, count FAQ patterns, measure section lengths, check for tables and lists via DOM inspection.

### Category C: Authority & Trust Signals (AUTH)

| Check ID | Check | Principle | Method |
|----------|-------|-----------|--------|
| AUTH-01 | Author information present (name, bio, credentials) | E-E-A-T | Check for author bylines, about sections |
| AUTH-02 | Organization/brand identity clear | Entity recognition | Check for About page, consistent branding |
| AUTH-03 | Publication and update dates visible | Freshness | Check for date metadata on content pages |
| AUTH-04 | Sources and references cited | E-E-A-T | Check for outbound links to authoritative sources |
| AUTH-05 | Testimonials/social proof present | Trust | Check for customer quotes, logos, case studies |
| AUTH-06 | Professional contact information available | Trust | Check for contact page, physical address, support channels |
|