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 \x3Cschema-type> [--url \x3Cpage-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

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 \x3Chead>:

\x3Chead>
  \x3Cscript type="application/ld+json">
  {
    "@context": "https://schema.org",
    "@type": "Organization",
    ...
  }
  \x3C/script>
\x3C/head>

Test Before Deploying

1. Schema.org Validator: https://validator.schema.org
2. Google Rich Results Test: https://search.google.com/test/rich-results
3. 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 \x3Cscript> 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)

See Also

• Schema type reference: references/schema-types.md
• Field requirements: references/field-reference.md
• Google guidelines: references/google-guidelines.md
• Examples by industry: references/examples.md