Geo Schema

GEO Schema & Structured Data

Purpose

Structured data does two jobs well: earning Google rich results and keeping entity data unambiguous (who the organization is, what it offers, which profiles belong to it). That is the scope of this skill.

What schema does NOT do: lift AI citations. In the Ahrefs controlled study (1,885 pages that added JSON-LD, reported May 2026 via Search Engine Journal), citation rates moved ChatGPT +2.2%, AI Mode +2.4%, AIO -4.6% — all within noise. Adding markup alone produced no citation lift on any platform. Do not sell schema as a GEO tactic.

The nuance worth keeping (SSRN, Feb 2026): schema that carries concrete, extractable facts (dates, prices, locations, specs) can still correlate with citation — but the lift comes from the quotable data, not the markup itself. Put the facts in visible, well-structured page content first; schema is the machine-readable echo, not the signal.

With that framing, complete and accurate structured data remains worthwhile: rich results still win SERP real estate, and clean entity data (Organization, sameAs, contactPoint) removes ambiguity for every system — search engines, knowledge graphs, and AI platforms alike.

How to Use This Skill

1. Fetch the target page HTML using curl or WebFetch
2. Detect all existing structured data (JSON-LD, Microdata, RDFa)
3. Validate detected schemas against Schema.org specifications
4. Identify missing recommended schemas based on business type
5. Generate ready-to-use JSON-LD code blocks
6. Output GEO-SCHEMA-REPORT.md

Step 1: Detection

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:

1. Valid JSON: Is the JSON-LD syntactically valid? Check for trailing commas, unquoted keys, malformed strings.
2. Valid @type: Does the @type match a recognized Schema.org type? Check against https://schema.org/docs/full.html.
3. Required Properties: Does the schema include all required properties for its type? (See per-type requirements below.)
4. Recommended Properties: Does the schema include recommended properties that improve rich-result eligibility and entity clarity?
5. sameAs Links: Does the schema include sameAs properties linking to other platform presences?
6. URL Validity: Do all URLs in the schema resolve (not 404)?
7. Nesting: Is the schema properly nested (e.g., author inside Article, address inside Organization)?
8. 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)

The backbone of unambiguous entity data: it states WHAT the business is in a form every search engine and knowledge graph can parse without inference.

Required properties:

• @type: "Organization" (or subtype: Corporation, LocalBusiness, etc.)
• name: Official business name
• url: Official website URL
• logo: 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 organization
• foundingDate: ISO 8601 date
• founder: Person schema
• address: PostalAddress schema
• contactPoint: ContactPoint with telephone, email, contactType
• areaServed: Geographic area
• numberOfEmployees: QuantitativeValue
• industry: Text or DefinedTerm
• award: Array of awards received
• knowsAbout: Array of topics the organization is expert in (entity clarity signal)

LocalBusiness (for businesses with physical locations)

Extends Organization. Critical for local AI search results and Google Gemini.

Additional required properties:

• address: Full PostalAddress
• telephone: Phone number
• openingHoursSpecification: Operating hours

Recommended for GEO:

• geo: GeoCoordinates (latitude, longitude)
• priceRange: Price indicator
• aggregateRating: AggregateRating schema
• review: Array of Review schemas
• hasMap: URL to Google Maps

Article + Author (CRITICAL for publishers)

Author markup supports E-E-A-T presentation and article rich results; it also keeps byline facts consistent for any system parsing the page.

Article required:

• @type: "Article" (or NewsArticle, BlogPosting, TechArticle)
• headline: Article title
• datePublished: ISO 8601
• dateModified: ISO 8601 (critical for freshness signals)
• author: Person or Organization schema
• publisher: Organization schema with logo
• image: Representative image

Author (Person) required for GEO:

• name: Full name
• url: Author page URL on the site
• sameAs: LinkedIn, Twitter, personal site, Google Scholar, ORCID
• jobTitle: Professional title
• worksFor: Organization schema
• knowsAbout: Array of expertise areas
• alumniOf: Educational institutions
• award: Professional awards

Product (for e-commerce)

Required:

• name, description, image
• offers: Offer with price, priceCurrency, availability
• brand: Brand schema
• sku or gtin/mpn

Recommended for GEO:

• aggregateRating: AggregateRating
• review: Array of individual reviews
• category: Product category
• material, weight, width, height (where applicable)

FAQPage

Status as of 2024: Google restricts FAQ rich results to government and health sites. FAQPage schema still makes Q&A pairs trivially machine-readable, but treat it as a parsability convenience, not a citation play — the Ahrefs controlled study (May 2026) found no AI-citation lift from adding JSON-LD. Implement it where Q&A content exists; expect clean extraction, not a visibility bump.

Structure:

• @type: "FAQPage"
• mainEntity: Array of Question schemas, each with acceptedAnswer containing an Answer schema

SoftwareApplication (for SaaS)

Required:

• name, description
• applicationCategory: e.g., "BusinessApplication"
• operatingSystem: Supported platforms
• offers: Pricing

Recommended for GEO:

• aggregateRating: User ratings
• featureList: Array of features (concrete extractable facts — remember the lift comes from the data, not the markup)
• screenshot: Screenshots
• softwareVersion: Current version
• releaseNotes: 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 marks which passages are intended for text-to-speech and assistant consumption. Treat it as a hint for voice surfaces; there is no controlled evidence it changes AI citation behavior.

Step 4: Deprecated/Changed Schemas to Flag

Flag any deprecated schemas found and recommend replacements.

Step 5: sameAs Strategy (CRITICAL for Entity Clarity)

The sameAs property is the highest-value structured data property for entity clarity. It tells every consuming system: "This entity on my website is the SAME entity as these profiles elsewhere." That removes ambiguity across search engines, knowledge graphs, and AI platforms — consistent entity data is a prerequisite for being recognized at all, even though (per the Ahrefs May 2026 controlled study) the markup alone does not lift citations.

Recommended sameAs Links (in priority order)

1. Wikipedia article — highest authority entity link
2. Wikidata item — machine-readable entity identifier (e.g., https://www.wikidata.org/wiki/Q12345)
3. LinkedIn — company page or personal profile
4. YouTube — channel URL
5. Twitter/X — profile URL
6. Facebook — page URL
7. Crunchbase — company profile (for startups/tech)
8. GitHub — organization or personal profile (for tech)
9. Google Scholar — author profile (for researchers/academics)
10. ORCID — researcher identifier (for academics)
11. Instagram — profile URL
12. Apple App Store / Google Play — app listings (for software)
13. BBB — Better Business Bureau listing (for US businesses)
14. Industry directories — relevant vertical directories

sameAs Audit Process

1. Collect all known web presences for the entity
2. Check that each URL resolves (not 404 or redirected)
3. Verify the Organization/Person schema includes ALL of them
4. Check that the information on each platform is consistent (name, description, founding date, etc.)
5. 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:

1. Organization or Person (depending on entity type) — always
2. WebSite with SearchAction — always for the homepage
3. Business-type-specific — Article for publishers, Product for e-commerce, LocalBusiness for local, SoftwareApplication for SaaS
4. 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)

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