geo-schema（地理图式）

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

1. Fetch the target page HTML using fetch_page.py (see note below)
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

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:

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 increase AI discoverability?
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)

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 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 (strong GEO 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)

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

Recommended for GEO:

• aggregateRating: User ratings
• featureList: Array of features (strong citation signal)
• 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 signals to AI assistants which passages are the best candidates for citation or reading aloud.

Step 4: Deprecated/Changed Schemas to Flag

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)

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