geo-schemalisted

# GEO Schema & Structured Data ## Purpose Structured data is the primary machine-readable signal that tells AI systems what an entity IS, what it does, and how it connects to other entities. While schema markup has traditionally been about earning Google rich results, its role in GEO is fundamentally different: **structured data is how AI models understand and trust your entity**. A complete entity graph in structured data dramatically increases citation probability across all AI search platforms. ## How to Use This Skill 1. Fetch the target page HTML using curl or WebFetch 2. Detect all existing structured data (JSON-LD, Microdata, RDFa) 3. Validate detected schemas against Schema.org specifications 4. Identify missing recommended schemas based on business type 5. Generate ready-to-use JSON-LD code blocks 6. Output GEO-SCHEMA-REPORT.md --- ## Step 1: Detection ### Scan for JSON-LD Look for `<script type="application/ld+json">` blocks in the HTML. Parse each block as JSON. A page may contain multiple JSON-LD blocks — collect all of them. ### Scan for Microdata Look for elements with `itemscope`, `itemtype`, and `itemprop` attributes. Map the hierarchy of nested items. Note: Microdata is harder for AI crawlers to parse than JSON-LD. Flag a recommendation to migrate to JSON-LD if Microdata is the only format found. ### Scan for RDFa Look for elements with `typeof`, `property`, and `vocab` attributes. Similar to Microdata — recommend migration to JSON-LD. ### Priority