geo-schema

Audit structured data and generate Schema.org JSON-LD for AI discoverability.

Updated Mar 22, 2026
One-click install
npx skills add https://github.com/engineai-nz/engineai-skills --skill geo-schema-engineai-nz
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: geo-schema
Source: https://github.com/engineai-nz/engineai-skills/tree/main/geo/geo-schema
Command: npx skills add https://github.com/engineai-nz/engineai-skills --skill geo-schema-engineai-nz

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

It helps you audit a page’s structured data and generate correct, AI-friendly JSON-LD so search and AI systems can reliably understand your entity and content.

Core Features & Use Cases

  • Structured-data detection across formats: identifies JSON-LD, Microdata, and RDFa on a target page and flags when migration to JSON-LD is needed.
  • Schema validation for correctness and completeness: checks JSON validity, Schema.org type recognition, required/recommended properties, URL resolution, nesting, and server-rendering vs JS-injection risk.
  • Ready-to-paste JSON-LD generation: produces JSON-LD using a schema.org @graph approach (Organization/Person, WebSite + SearchAction, and business-type specific schemas like Article, LocalBusiness, Product, or SoftwareApplication), and outputs a GEO-SCHEMA-REPORT.md summary.

Quick Start

Use geo-schema to audit the structured data on https://example.com by fetching the page HTML and generating validated JSON-LD suitable for AI discoverability.

Frequently Asked Questions about geo-schema

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I audit structured data on a webpage for schema.org errors?

To audit structured data, fetch the target page HTML to detect JSON-LD, Microdata, and RDFa formats. The audit validates JSON syntax, recognizes Schema.org types, checks required properties, and verifies URL resolution to identify missing or incorrect entities.

What is JSON-LD structured data for entity recognition?

JSON-LD structured data is a server-rendered script format that uses Schema.org vocabularies to define entities. It enables search and AI systems to reliably understand business types like Organization, LocalBusiness, or Product through validated @graph structures and sameAs references.

How do I generate Schema.org JSON-LD for AI discoverability?

Generate Schema.org JSON-LD by building an @graph structure containing Organization or Person, WebSite with SearchAction, and business-specific schemas. Output uses absolute URLs and validated properties to produce ready-to-paste JSON-LD optimized for AI discoverability.

Does server-rendered HTML matter for JSON-LD validation?

Server-rendered HTML matters significantly for JSON-LD validation because JS-injected structured data risks being missed by crawlers. Validating server-rendered HTML ensures search and AI systems can reliably parse the entity graph without executing JavaScript.

When do I need to migrate Microdata or RDFa to JSON-LD?

You need to migrate Microdata or RDFa to JSON-LD when structured data detection flags outdated formats on your page. Migrating to JSON-LD using a Schema.org @graph approach ensures better entity recognition and compatibility with modern AI discoverability workflows.