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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
- Fetch the target page HTML using
fetch_page.py (see note below)
- Detect all existing structured data (JSON-LD, Microdata, RDFa)
- Validate detected schemas against Schema.org specifications
- Identify missing recommended schemas based on business type
- Generate ready-to-use JSON-LD code blocks
- 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:
python3 ~/.claude/skills/geo/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: Microdata is harder for AI crawlers to parse than JSON-LD. Flag a recommendation to migrate to JSON-LD if Microdata is the only format found.
Scan for RDFa
Look for elements with typeof, property, and vocab attributes. Similar to Microdata — recommend migration to JSON-LD.
Priority Order
JSON-LD is the strongly recommended format for GEO. Google, Bing, and AI platforms all process JSON-LD most reliably. If the site uses Microdata or RDFa exclusively, flag this as a high-priority migration.
Step 2: Validation
For each detected schema block, validate:
- Valid JSON : Is the JSON-LD syntactically valid? Check for trailing commas, unquoted keys, malformed strings.
- Valid @type : Does the
@type match a recognized Schema.org type? Check against https://schema.org/docs/full.html.
- Required Properties : Does the schema include all required properties for its type? (See per-type requirements below.)
- Recommended Properties : Does the schema include recommended properties that increase AI discoverability?
- sameAs Links : Does the schema include
sameAs properties linking to other platform presences?
- URL Validity : Do all URLs in the schema resolve (not 404)?
- Nesting : Is the schema properly nested (e.g., author inside Article, address inside Organization)?
- Rendering Method : Is the JSON-LD in the server-rendered HTML or injected via JavaScript? Per Google's December 2025 guidance, JavaScript-injected structured data may face delayed processing. Flag any schema that requires JS execution.
Step 3: Schema Types for GEO
Organization (CRITICAL — every business site)
Essential for entity recognition across all AI platforms. This is how AI models identify WHAT the business is.
Required properties:
@type: "Organization" (or subtype: Corporation, LocalBusiness, etc.)name: Official business nameurl: Official website URLlogo: URL to logo image (ImageObject preferred)
Recommended properties for GEO:
sameAs: Array of ALL platform URLs (see sameAs strategy below)description: 1-2 sentence description of the organizationfoundingDate: ISO 8601 datefounder: Person schemaaddress: PostalAddress schemacontactPoint: ContactPoint with telephone, email, contactTypeareaServed: Geographic areanumberOfEmployees: QuantitativeValueindustry: Text or DefinedTermaward: Array of awards received
LocalBusiness (for businesses with physical locations)
Extends Organization. Critical for local AI search results and Google Gemini.
Additional required properties:
address: Full PostalAddresstelephone: Phone numberopeningHoursSpecification: Operating hours
Recommended for GEO:
geo: GeoCoordinates (latitude, longitude)priceRange: Price indicatoraggregateRating: AggregateRating schemareview: Array of Review schemashasMap: URL to Google Maps
Article + Author (CRITICAL for publishers)
The Author schema is one of the strongest E-E-A-T signals for AI platforms.
Article required:
@type: "Article" (or NewsArticle, BlogPosting, TechArticle)headline: Article titledatePublished: ISO 8601dateModified: ISO 8601 (critical for freshness signals)author: Person or Organization schemapublisher: Organization schema with logoimage: Representative image
Author (Person) required for GEO:
name: Full nameurl: Author page URL on the sitesameAs: LinkedIn, Twitter, personal site, Google Scholar, ORCIDjobTitle: Professional titleworksFor: Organization schemaknowsAbout: Array of expertise areasalumniOf: Educational institutionsaward: Professional awards
Product (for e-commerce)
Required:
name, description, imageoffers: Offer with price, priceCurrency, availabilitybrand: Brand schemasku or gtin/mpn
Recommended for GEO:
aggregateRating: AggregateRatingreview: Array of individual reviewscategory: Product categorymaterial, weight, width, height (where applicable)
FAQPage
Status as of 2024 : Google restricts FAQ rich results to government and health sites. However, the FAQPage schema still serves GEO purposes — AI platforms parse FAQ structured data for question-answer extraction. Implement it for AI readability even though rich results may not appear.
Structure:
@type: "FAQPage"mainEntity: Array of Question schemas, each with acceptedAnswer containing an Answer schema
SoftwareApplication (for SaaS)
Required:
name, descriptionapplicationCategory: e.g., "BusinessApplication"operatingSystem: Supported platformsoffers: Pricing
Recommended for GEO:
aggregateRating: User ratingsfeatureList: Array of features (strong citation signal)screenshot: ScreenshotssoftwareVersion: Current versionreleaseNotes: Link to changelog
WebSite + SearchAction (for sitelinks search box)
Structure:
{
"@type": "WebSite",
"name": "Site Name",
"url": "https://example.com",
"potentialAction": {
"@type": "SearchAction",
"target": {
"@type": "EntryPoint",
"urlTemplate": "https://example.com/search?q={search_term_string}"
},
"query-input": "required name=search_term_string"
}
}
Person (standalone — for personal brands, authors, thought leaders)
Use as a standalone schema on About/Bio pages. This builds the entity graph for individual expertise.
Required: name, url Recommended for GEO: sameAs, jobTitle, worksFor, knowsAbout, alumniOf, award, description, image
speakable Property (for voice/AI assistants)
The speakable property marks specific sections of content as particularly suitable for voice and AI assistant consumption. Add to Article or WebPage schemas.
{
"@type": "Article",
"speakable": {
"@type": "SpeakableSpecification",
"cssSelector": [".article-summary", ".key-takeaway"]
}
}
This signals to AI assistants which passages are the best candidates for citation or reading aloud.
Step 4: Deprecated/Changed Schemas to Flag
| Schema | Status | Note |
|---|
| HowTo | Rich results deprecated Aug 2023 | Still useful for AI parsing, but do not promise rich results |
| FAQPage | Restricted to govt/health Aug 2023 | Still useful for AI parsing (see above) |
| SpecialAnnouncement | Deprecated 2023 | Was for COVID; remove if still present |
| CourseInfo | Replaced by Course updates 2024 | Use updated Course schema properties |
VideoObject contentUrl | Changed behavior 2024 | Must point to actual video file, not page URL |
| Review snippet | Stricter enforcement 2024 | Self-serving reviews on product pages may not display |
Flag any deprecated schemas found and recommend replacements.
Step 5: sameAs Strategy (CRITICAL for Entity Recognition)
The sameAs property is the single most important structured data property for GEO. It tells AI systems: "This entity on my website is the SAME entity as these profiles elsewhere." This creates the entity graph that AI platforms use to verify, trust, and cite sources.
Recommended sameAs Links (in priority order)
- Wikipedia article — highest authority entity link
- Wikidata item — machine-readable entity identifier (e.g.,
https://www.wikidata.org/wiki/Q12345)
- LinkedIn — company page or personal profile
- YouTube — channel URL
- Twitter/X — profile URL
- Facebook — page URL
- Crunchbase — company profile (for startups/tech)
- GitHub — organization or personal profile (for tech)
- Google Scholar — author profile (for researchers/academics)
- ORCID — researcher identifier (for academics)
- Instagram — profile URL
- Apple App Store / Google Play — app listings (for software)
- BBB — Better Business Bureau listing (for US businesses)
- Industry directories — relevant vertical directories
sameAs Audit Process
- Collect all known web presences for the entity
- Check that each URL resolves (not 404 or redirected)
- Verify the Organization/Person schema includes ALL of them
- Check that the information on each platform is consistent (name, description, founding date, etc.)
- Flag any platforms where the entity should have a presence but does not
Step 6: JSON-LD Generation
Based on the detected business type, generate ready-to-paste JSON-LD blocks. Always generate:
- Organization or Person (depending on entity type) — always
- WebSite with SearchAction — always for the homepage
- Business-type-specific — Article for publishers, Product for e-commerce, LocalBusiness for local, SoftwareApplication for SaaS
- BreadcrumbList — for any page deeper than homepage
Generation Rules
- Use the
@graph pattern to include multiple schemas in one JSON-LD block
- All URLs must be absolute (not relative)
- Include
@id properties for cross-referencing between schemas
- Use ISO 8601 for all dates
- Include
speakable on Article schemas with CSS selectors pointing to key content sections
- Place JSON-LD in
<head> section — NOT injected via JavaScript
Template: Organization with Full GEO Signals
{
"@context": "https://schema.org",
"@type": "Organization",
"@id": "https://example.com/#organization",
"name": "Company Name",
"url": "https://example.com",
"logo": {
"@type": "ImageObject",
"url": "https://example.com/logo.png",
"width": 600,
"height": 60
},
"description": "Concise description of what the company does.",
"foundingDate": "2020-01-15",
"founder": {
"@type": "Person",
"name": "Founder Name",
"sameAs": "https://www.linkedin.com/in/founder"
},
"address": {
"@type": "PostalAddress",
"streetAddress": "123 Main St",
"addressLocality": "City",
"addressRegion": "State",
"postalCode": "12345",
"addressCountry": "US"
},
"contactPoint": {
"@type": "ContactPoint",
"telephone": "+1-555-555-5555",
"contactType": "customer service",
"email": "support@example.com"
},
"sameAs": [
"https://en.wikipedia.org/wiki/Company_Name",
"https://www.wikidata.org/wiki/Q12345",
"https://www.linkedin.com/company/company-name",
"https://www.youtube.com/@companyname",
"https://twitter.com/companyname",
"https://github.com/companyname",
"https://www.crunchbase.com/organization/company-name"
],
"knowsAbout": [
"Topic 1",
"Topic 2",
"Topic 3"
]
}
Scoring Rubric (0-100)
| Criterion | Points | How to Score |
|---|
| Organization/Person schema present and complete | 15 | 15 if full, 10 if basic, 0 if none |
| sameAs links (5+ platforms) | 15 | 3 per valid sameAs link, max 15 |
| Article schema with author details | 10 | 10 if full author schema, 5 if name only, 0 if none |
| Business-type-specific schema present | 10 | 10 if complete, 5 if partial, 0 if missing |
| WebSite + SearchAction | 5 | 5 if present, 0 if not |
| BreadcrumbList on inner pages | 5 | 5 if present, 0 if not |
| JSON-LD format (not Microdata/RDFa) | 5 | 5 if JSON-LD, 3 if mixed, 0 if only Microdata/RDFa |
| Server-rendered (not JS-injected) | 10 | 10 if in HTML source, 5 if JS but in head, 0 if dynamic JS |
| speakable property on articles |
Output Format
Generate GEO-SCHEMA-REPORT.md with:
# GEO Schema & Structured Data Report — [Domain]
Date: [Date]
## Schema Score: XX/100
## Detected Schemas
| Page | Schema Type | Format | Status | Issues |
|---|---|---|---|---|
| / | Organization | JSON-LD | Valid | Missing sameAs |
| /blog/post-1 | Article | JSON-LD | Valid | No author schema |
## Validation Results
[List each schema with pass/fail per property]
## Missing Recommended Schemas
[List schemas that should be present based on business type but are not]
## sameAs Audit
| Platform | URL | Status |
|---|---|---|
| Wikipedia | [URL or "Not found"] | Present/Missing |
| LinkedIn | [URL or "Not found"] | Present/Missing |
[Continue for all recommended platforms]
## Generated JSON-LD Code
[Ready-to-paste JSON-LD blocks for each missing or incomplete schema]
## Implementation Notes
- Where to place each JSON-LD block
- Server-rendering requirements
- Testing with Google Rich Results Test and Schema.org Validator
Weekly Installs
72
Repository
zubair-trabzada…o-claude
GitHub Stars
4.2K
First Seen
Feb 27, 2026
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