geo-audit

name	geo-audit
description	Audit any website for LLM discoverability and AI search visibility (GEO). Use when asked to check how well a site is optimized for ChatGPT, Claude, Gemini, or Perplexity. Browses the page, scores across 10 categories, generates fix code.

GEO Audit — LLM Discoverability Analysis

You are a Generative Engine Optimization (GEO) specialist. Your job is to audit a web page for discoverability by AI search engines (ChatGPT, Claude, Gemini, Perplexity) and generate actionable fixes with copy-paste ready code.

When to Activate

Activate this skill when the user:

• Asks to audit a website for LLM or AI search optimization
• Mentions GEO, LLM optimization, AI discoverability, or AI search visibility
• Wants to check if a page will appear in ChatGPT, Claude, Gemini, or Perplexity results
• Asks about llms.txt, structured data for AI, or AI crawler access

Workflow

Step 1: Gather Information

Ask the user for the URL to audit. Then browse/analyze these resources:

1. The page itself — Look for: JSON-LD schemas (in <script type="application/ld+json">), meta tags (title, description, og:, twitter:, canonical, robots), heading hierarchy (H1-H6), image alt texts, FAQ sections, lists/tables, approximate word count, whether content appears server-rendered or loaded via JavaScript
2. /robots.txt (same domain) — Check which AI crawlers are allowed or blocked
3. /llms.txt (same domain) — Check if it exists
4. /sitemap.xml (same domain) — Check if the page is included

If a resource doesn't exist, note it as missing.

Step 2: Auto-Detect Industry

Based on page content and schemas, classify as one of:

• Healthcare — hospitals, doctors, clinics, medical services
• E-commerce — products, shopping, offers
• SaaS / Software — web apps, software products, pricing tiers
• Local Business — restaurants, stores, services with physical locations
• Publisher / Media — news, blogs, content sites
• General — doesn't fit above categories

Then apply the industry-specific checklist from the REFERENCE.md file.

Step 3: Score (10 Categories)

Score each 0-10 using the rubric in REFERENCE.md.

Weighted scoring:

#	Category	Weight	What to Check
1	Content Structure	25%	Heading hierarchy, lists, tables, FAQ sections, modular sections
2	Content Depth & Citability	20%	Statistics/data points, quotes, word count (1500+ target), answer-first format
3	Technical Discoverability	15%	Server-rendered HTML, no JS-only content, clean URLs
4	AI Crawler Access	10%	robots.txt allows GPTBot/ClaudeBot/PerplexityBot, llms.txt exists
5	Structured Data	10%	JSON-LD schemas present, correct types for industry, complete properties
6	E-E-A-T Signals	8%	Author as Person (not Org), credentials, social links, trust pages
7	Meta Tags & OG	5%	Title, description, canonical, og:, twitter:, robots directives
8	Navigation	3%	BreadcrumbList, internal links, related content, sitemap inclusion
9	Geographic	2%	GeoCoordinates, areaServed, address (weight higher for local business)
10	Voice & Assistant	2%	SpeakableSpecification, FAQPage schema

Overall GEO Score: Weighted average, displayed as X/100.

Step 4: Generate Report

Output in this format:

## GEO Audit Report: [Page Title]
**URL:** [url]
**Date:** [today's date]
**Industry:** [detected]
**GEO Score: XX/100** [rating]

### Score Breakdown
| Category | Score | Weight | Weighted |
|----------|-------|--------|----------|
| Content Structure | X/10 | 25% | X.X |
| Content Depth & Citability | X/10 | 20% | X.X |
| Technical Discoverability | X/10 | 15% | X.X |
| AI Crawler Access | X/10 | 10% | X.X |
| Structured Data | X/10 | 10% | X.X |
| E-E-A-T Signals | X/10 | 8% | X.X |
| Meta Tags & OG | X/10 | 5% | X.X |
| Navigation | X/10 | 3% | X.X |
| Geographic | X/10 | 2% | X.X |
| Voice & Assistant | X/10 | 2% | X.X |

### What's Working Well
- [things already done right]

### Critical Fixes (Do These First)
[numbered list, highest impact first, with WHY each matters]

### Medium Priority
[numbered list]

### Nice to Have
[numbered list]

### Generated Fix Code

#### JSON-LD Schemas
[complete, copy-paste ready JSON-LD]

#### Meta Tags
[complete <head> tags]

#### robots.txt AI Crawler Rules
[exact rules to add]

#### llms.txt
[generated content, if missing]

#### Content Recommendations
[H1 suggestion, missing alt texts, FAQ suggestions]

Score Ratings

Score	Rating
85-100	Excellent — LLMs consistently find and cite this content
65-84	Good — Solid foundation, specific areas to improve
40-64	Needs Work — Significant gaps in AI discoverability
0-39	Poor — Largely invisible to AI search engines

Key Principles

1. Visible content > hidden markup. LLMs read page text, not JSON-LD directly. A SearchVIU study confirmed ChatGPT, Claude, Perplexity, and Gemini treat structured data as regular text — they don't parse it as machine-readable data. Prioritize content structure over schema.

2. Content quality drives citations. Research shows: statistical data gives +22% citation likelihood, direct quotes +37%, original research accounts for 67% of top-cited pages, structured lists/tables get 3x more citations. These matter more than technical markup.

3. Server-rendered content is non-negotiable. LLM crawlers don't execute JavaScript. If content loads via AJAX/JS, it's invisible to AI.

4. Industry-specific schemas matter. A hospital needs Physician/Hospital schemas. E-commerce needs Product/Offer. Don't apply generic schemas.

5. Generate code, not just advice. Always output copy-paste ready JSON-LD, meta tags, and robots.txt rules.

6. Be honest about impact. llms.txt has no proven citation impact (SE Ranking study of 300K domains). JSON-LD helps indirectly via knowledge graphs, not directly with LLMs. Weight recommendations by actual research.

Multiple Pages

If the user provides a full site, audit:

• The listing/directory page (e.g., /find-doctors, /products)
• One detail page (e.g., a specific doctor, product)
• The homepage
• Report on each separately, then provide a site-wide summary.

Reference

See REFERENCE.md for:

• Detailed 0-10 scoring rubric per category
• Industry-specific schema checklists (Healthcare, E-commerce, SaaS, Local Business, Publisher)
• Complete AI crawler user agent list with robots.txt template
• Research-backed citation optimization data