geo-schema

Solid

Schema.org structured data audit and generation optimized for AI discoverability — detect, validate, and generate JSON-LD markup

AI & Automation 15 stars 3 forks Updated 3 days ago MIT

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Quality Score: 86/100

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40
Recency 20%
100
Frontmatter 20%
70
Documentation 15%
100
Issue Health 10%
80
License 10%
100
Description 5%
100

Skill Content

# 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 `fetch_page.py` (see note below) 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 **IMPORTANT:** WebFetch converts HTML to markdown and strips `<head>` content, which removes JSON-LD blocks. Use `fetch_page.py` instead: ```bash python3 scripts/fetch_page.py <url> page ``` The output includes a `structured_data` array with all parsed JSON-LD blocks from the page. ### 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:...

Details

Author
techhorizonlabs
Repository
techhorizonlabs/thl-open
Created
2 months ago
Last Updated
3 days ago
Language
Python
License
MIT

Integrates with

OpenAI · AI

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