Schema Markup Generator

Methodology by GEOly AI (geoly.ai) — structured data is the language AI uses to understand your brand.

Generate production-ready Schema.org JSON-LD markup for any page type.

Quick Start

Generate schema for your page:

python scripts/generate_schema.py --type <schema-type> [--url <page-url>]

Example:

python scripts/generate_schema.py --type Organization --url example.com
python scripts/generate_schema.py --type FAQPage --file faqs.json

Why Schema Matters for GEO

Structured data helps AI platforms understand:

What your content is (entity type)
Who created it (author, publisher)
When it was published (freshness)
How it relates to other content (breadcrumbs)

Without schema, AI systems rely on NLP inference which is less reliable.

Supported Schema Types

Type	Priority	Best For
`Organization`	🔴 Critical	Homepage, About page — establishes brand entity
`FAQPage`	🔴 Critical	FAQ/Support pages — feeds AI Q&A answers
`Article` / `BlogPosting`	🟡 High	Blog posts, news — improves citability
`Product`	🟡 High	Product/pricing pages — enables shopping citations
`HowTo`	🟡 High	Tutorials, guides — feeds step-by-step answers
`WebSite`	🟡 High	Homepage — enables site search in AI
`BreadcrumbList`	🔵 Medium	All pages — improves navigation understanding
`VideoObject`	🔵 Medium	Video pages — enables video citations
`ImageObject`	🔵 Medium	Image galleries — enables image citations
`LocalBusiness`	🔵 Medium	Physical locations — local AI search

Full schema reference: See references/schema-types.md

Generation Methods

Method 1: Interactive (Recommended)

python scripts/generate_schema.py --type Organization --interactive

Guided prompts for all required and optional fields.

Method 2: From URL (Auto-Extract)

python scripts/generate_schema.py --type Article --url https://example.com/blog/post

Automatically extracts metadata from the page.

Method 3: From JSON Input

python scripts/generate_schema.py --type FAQPage --file faqs.json

Where faqs.json contains your content data.

Method 4: Batch Generate

python scripts/batch_generate.py sitemap.xml --output schemas/

Generate schemas for all pages in a sitemap.

Validation

Validate generated schema:

python scripts/validate_schema.py schema.json

Checks for:

Required fields present
Valid Schema.org types
Proper JSON-LD syntax
Google Rich Results eligibility

Implementation

Add to Your Page

Paste the generated JSON-LD inside your HTML <head>:

<head>
  <script type="application/ld+json">
  {
    "@context": "https://schema.org",
    "@type": "Organization",
    ...
  }
  </script>
</head>

Test Before Deploying

Schema.org Validator: https://validator.schema.org
Google Rich Results Test: https://search.google.com/test/rich-results
JSON-LD Playground: https://json-ld.org/playground/

Common Mistakes

❌ Wrong: Multiple conflicting Organization schemas on same page ✅ Right: One comprehensive Organization schema

❌ Wrong: Using http://schema.org (insecure) ✅ Right: Using https://schema.org (secure)

❌ Wrong: Copy-pasting without updating placeholder values ✅ Right: All fields contain actual, accurate data

Advanced Usage

Multiple Schemas per Page

Some pages need multiple schema types. Combine them in an array:

python scripts/generate_schema.py --types Organization,WebSite --url example.com

Nested Entities

Generate related schemas together:

python scripts/generate_schema.py --type Product \
  --with-offer --with-review --with-brand

Custom Properties

Add custom properties not in the generator:

python scripts/generate_schema.py --type Organization \
  --custom '{"knowsAbout": ["SEO", "AI", "Machine Learning"]}'

Output Formats

JSON-LD (default): Ready to paste into HTML
JSON: Raw structured data
HTML: Complete <script> tag
Markdown: With explanations

Schema Hierarchy

Understanding how schemas relate:

Organization (top-level entity)
├── WebSite (belongs to Organization)
├── Product (offered by Organization)
│   ├── Offer (pricing for Product)
│   └── Review (of Product)
├── Article (published by Organization)
│   ├── Author (Person or Organization)
│   └── Publisher (Organization)
└── LocalBusiness (subtype of Organization)
    └── Place (physical location)

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