GEO Schema & Structured Data

tags: [geo, schema, structured-data, json-ld, entity-recognition, ai-discoverability]

GEO Schema & Structured Data

What Is This?

The "GEO Schema & Structured Data" skill is a specialized audit and generation tool designed to enhance the discoverability of entities by AI systems through robust structured data. It focuses on detecting, validating, and generating Schema.org-compliant structured data, primarily in JSON-LD format, for websites and digital assets. Unlike traditional SEO schema tools that primarily target search engine rich results, this skill is engineered to optimize for AI discoverability-ensuring that large language models and AI search platforms can accurately recognize, interpret, and cite your entity.

At its core, this skill automates the process of extracting existing structured data from web pages, assessing its completeness and validity, and then recommending or generating additional JSON-LD code to ensure full entity representation. This process is crucial for developing an entity graph that AI systems can trust, enabling higher citation probability and better representation in knowledge-driven search environments.

Why Use It?

Structured data is the linchpin of machine-readable entity understanding. While traditional schema markup supports SEO and rich snippets in search engines, its application in the context of GEO (Generative Entity Optimization) is fundamentally about teaching AI what your entity is, what it does, and how it relates to other entities.

Key reasons to use the GEO Schema & Structured Data skill include:

AI Discoverability: Complete and accurate structured data ensures AI systems recognize and trust your entity, increasing the likelihood of citations in generative AI search results.
Schema Coverage: The skill not only detects existing schema but identifies gaps based on the entity type and business context, recommending or generating additional markup as needed.
Validation: Automated validation against Schema.org standards ensures that your structured data is both syntactically and semantically correct.
Efficiency: Streamlines the process of structured data management-no need for manual inspection, validation, or generation.
Reporting: Outputs a comprehensive GEO-SCHEMA-REPORT.md, providing actionable insights for further optimization.

How to Use It

The GEO Schema & Structured Data skill is designed for ease of integration into your entity optimization workflow. Below are the detailed steps to leverage its full potential:

1. Fetch the Target Page

HTML

To preserve all structured data (especially JSON-LD usually embedded in the <head> section), use the provided fetch_page.py script rather than generic web fetch tools. WebFetch typically strips head content, which can result in incomplete detection.

python3 ~/.claude/skills/geo/scripts/fetch_page.py <url> page

This command fetches the HTML content of the specified page and extracts structured data into a structured_data array.

2. Detect Existing Structured

Data

The tool scans the fetched HTML for all types of schema markup, including:

JSON-LD
Microdata
RDFa

It parses these formats and aggregates the results for further analysis.

3. Validate Detected

Schemas

Each discovered structured data block is validated against the latest Schema.org specifications. The validation process checks for:

Syntactic correctness (e.g., valid JSON)
Appropriate use of schema types and properties
Alignment with entity context

Validation output highlights errors and warnings, facilitating quick remediation.

4. Identify Missing Recommended

Schemas

Based on the type of business or entity, the skill cross-references detected schemas against a knowledge base of recommended schema types (e.g., LocalBusiness, Organization, Product). It identifies gaps and suggests additional schema types or properties that should be present for optimal AI comprehension.

5. Generate Ready-to-Use JSON-LD Code

Blocks

For missing or incomplete schema, the skill generates new JSON-LD blocks tailored to the entity's context. These code blocks are ready to deploy in your website's HTML.

Example output:

{
  "@context": "https://schema.org",
  "@type": "LocalBusiness",
  "name": "Happycapy Coffee",
  "address": {
    "@type": "PostalAddress",
    "streetAddress": "123 Main St",
    "addressLocality": "Capytown",
    "addressRegion": "CA",
    "postalCode": "90210"
  },
  "telephone": "+1-800-555-1234",
  "url": "https://happycapy.com"
}

6. Output

GEO-SCHEMA-REPORT.md

After analysis and generation, the skill compiles a detailed report in markdown format. This report summarizes:

Detected schemas
Validation results
Missing recommendations
Generated JSON-LD code blocks

The report provides clear, actionable steps for implementation.

When to Use It

Deploy the GEO Schema & Structured Data skill in the following scenarios:

Entity Optimization for AI: When you want to ensure your entity is fully represented and discoverable by AI models and generative search platforms.
Structured Data Audits: Periodically audit your site or digital assets to maintain schema integrity and completeness.
Pre-Launch Checks: Before launching a new website, landing page, or digital asset to maximize AI discoverability from day one.
Ongoing Maintenance: As your business evolves, use the skill to update your structured data and adapt to new Schema.org standards.

Important Notes

Do Not Use WebFetch for Initial Extraction: To avoid missing JSON-LD blocks, especially those in the <head>, always use fetch_page.py for HTML extraction.
Schema.org Evolution: The schema landscape evolves. Regularly re-audit your structured data to remain compliant and competitive.
Business-Specific Recommendations: The tool's recommendations are context-aware, but always review generated code to ensure accuracy and relevance.
Data Privacy: Ensure no sensitive or personal data is inadvertently included in your structured data.

By leveraging the GEO Schema & Structured Data skill, you position your entity for maximum visibility, trust, and citation potential across the rapidly evolving AI discovery ecosystem.

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