{"slug":"zubair-trabzada-geo-schema","name":"geo-schema","description":"Schema.org structured data audit and generation optimized for AI discoverability — detect, validate, and generate JSON-LD markup","long_description":"---\nname: geo-schema\ndescription: Schema.org structured data audit and generation optimized for AI discoverability — detect, validate, and generate JSON-LD markup\nversion: 1.0.0\nauthor: geo-seo-claude\ntags: [geo, schema, structured-data, json-ld, entity-recognition, ai-discoverability]\nallowed-tools: Read, Grep, Glob, Bash, WebFetch, Write\n---\n\n# GEO Schema & Structured Data\n\n## Purpose\n\nStructured 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.\n\n## How to Use This Skill\n\n1. Fetch the target page HTML using `fetch_page.py` (see note below)\n2. Detect all existing structured data (JSON-LD, Microdata, RDFa)\n3. Validate detected schemas against Schema.org specifications\n4. Identify missing recommended schemas based on business type\n5. Generate ready-to-use JSON-LD code blocks\n6. Output GEO-SCHEMA-REPORT.md\n\n---\n\n## Step 1: Detection\n\n**IMPORTANT:** WebFetch converts HTML to markdown and strips `` content, which removes JSON-LD blocks. Use `fetch_page.py` instead:\n```bash\npython3 ~/.claude/skills/geo/scripts/fetch_page.py page\n```\nThe output includes a `structured_data` array with all parsed JSON-LD blocks from the page.\n\n### Scan for JSON-LD\nLook for `