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Schema.org structured data audit and generation optimized

GEO Schema & Structured Data

by zubair-trabzada · MIT license · ★ 10,775 on GitHub · v1.0.0 · updated Sep 21

GEO Schema & Structured Data cover

Schema.org structured data audit and generation optimized for AI discoverability — detect, validate, and generate JSON-LD markup

---
name: geo-schema
description: Schema.org structured data audit and generation optimized for AI discoverability — detect, validate, and generate JSON-LD markup
version: 1.0.0
author: geo-seo-claude
tags: [geo, schema, structured-data, json-ld, entity-recognition, ai-discoverability]
allowed-tools: Read, Grep, Glob, Bash, WebFetch, Write
---

# 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)

Overview

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.

Manual install (npx, ZIP)
Claude Codenpx skills add zubair-trabzada/geo-seo-claude --skill geo-schema -g -a claude-code
Claude Code (manual)mkdir -p ~/.claude/skills && curl -sL https://codeload.github.com/zubair-trabzada/geo-seo-claude/tar.gz/383829485f86 \ | tar -xz -C ~/.claude/skills --strip-components=2 geo-seo-claude-383829485f86/skills/geo-schema
Windows (PowerShell)New-Item -ItemType Directory -Force "$HOME\.claude\skills" | Out-Null; Invoke-WebRequest "https://m.agentalley.io/zip/32f240645363898502bb/geo-schema.zip" -OutFile "$env:TEMP\geo-schema.zip"; Expand-Archive "$env:TEMP\geo-schema.zip" -DestinationPath "$HOME\.claude\skills" -Force
Claude.ai / desktopDownload the ZIP and upload it under Settings → Capabilities → Skills.
Codex, Cursor, other agentsPoint them at SKILL.md. The other files are plain text.

Connects with Agent Alley MCP · agentalley.io/mcp

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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

  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

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.

  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": "[email protected]"
  },
  "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 5 5 if present, 0 if not
Valid JSON + valid Schema.org types 10 10 if no errors, 5 if minor issues, 0 if major errors
knowsAbout property on Organization/Person 5 5 if present with 3+ topics, 0 if missing
No deprecated schemas present 5 5 if clean, 0 if deprecated schemas found

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
1---
2name: geo-schema
3description: Schema.org structured data audit and generation optimized for AI discoverability — detect, validate, and generate JSON-LD markup
4version: 1.0.0
5author: geo-seo-claude
6tags: [geo, schema, structured-data, json-ld, entity-recognition, ai-discoverability]
7allowed-tools: Read, Grep, Glob, Bash, WebFetch, Write
8---
9 
10# GEO Schema & Structured Data
11 
12## Purpose
13 
14Structured 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.
15 
16## How to Use This Skill
17 
181. Fetch the target page HTML using `fetch_page.py` (see note below)
192. Detect all existing structured data (JSON-LD, Microdata, RDFa)
203. Validate detected schemas against Schema.org specifications
214. Identify missing recommended schemas based on business type
225. Generate ready-to-use JSON-LD code blocks
236. Output GEO-SCHEMA-REPORT.md
24 
25---
26 
27## Step 1: Detection
28 
29**IMPORTANT:** WebFetch converts HTML to markdown and strips `<head>` content, which removes JSON-LD blocks. Use `fetch_page.py` instead:
30```bash
31python3 ~/.claude/skills/geo/scripts/fetch_page.py <url> page
32```
33The output includes a `structured_data` array with all parsed JSON-LD blocks from the page.
34 
35### Scan for JSON-LD
36Look 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.
37 
38### Scan for Microdata
39Look 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.
40 
41### Scan for RDFa
42Look for elements with `typeof`, `property`, and `vocab` attributes. Similar to Microdata — recommend migration to JSON-LD.
43 
44### Priority Order
45JSON-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.
46 
47---
48 
49## Step 2: Validation
50 
51For each detected schema block, validate:
52 
531. **Valid JSON**: Is the JSON-LD syntactically valid? Check for trailing commas, unquoted keys, malformed strings.
542. **Valid @type**: Does the `@type` match a recognized Schema.org type? Check against https://schema.org/docs/full.html.
553. **Required Properties**: Does the schema include all required properties for its type? (See per-type requirements below.)
564. **Recommended Properties**: Does the schema include recommended properties that increase AI discoverability?
575. **sameAs Links**: Does the schema include `sameAs` properties linking to other platform presences?
586. **URL Validity**: Do all URLs in the schema resolve (not 404)?
597. **Nesting**: Is the schema properly nested (e.g., author inside Article, address inside Organization)?
608. **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.
61 
62---
63 
64## Step 3: Schema Types for GEO
65 
66### Organization (CRITICAL — every business site)
67Essential for entity recognition across all AI platforms. This is how AI models identify WHAT the business is.
68 
69**Required properties:**
70- `@type`: "Organization" (or subtype: Corporation, LocalBusiness, etc.)
71- `name`: Official business name
72- `url`: Official website URL
73- `logo`: URL to logo image (ImageObject preferred)
74 
75**Recommended properties for GEO:**
76- `sameAs`: Array of ALL platform URLs (see sameAs strategy below)
77- `description`: 1-2 sentence description of the organization
78- `foundingDate`: ISO 8601 date
79- `founder`: Person schema
80- `address`: PostalAddress schema
81- `contactPoint`: ContactPoint with telephone, email, contactType
82- `areaServed`: Geographic area
83- `numberOfEmployees`: QuantitativeValue
84- `industry`: Text or DefinedTerm
85- `award`: Array of awards received
86- `knowsAbout`: Array of topics the organization is expert in (strong GEO signal)
87 
88### LocalBusiness (for businesses with physical locations)
89Extends Organization. Critical for local AI search results and Google Gemini.
90 
91**Additional required properties:**
92- `address`: Full PostalAddress
93- `telephone`: Phone number
94- `openingHoursSpecification`: Operating hours
95 
96**Recommended for GEO:**
97- `geo`: GeoCoordinates (latitude, longitude)
98- `priceRange`: Price indicator
99- `aggregateRating`: AggregateRating schema
100- `review`: Array of Review schemas
101- `hasMap`: URL to Google Maps
102 
103### Article + Author (CRITICAL for publishers)
104The Author schema is one of the strongest E-E-A-T signals for AI platforms.
105 
106**Article required:**
107- `@type`: "Article" (or NewsArticle, BlogPosting, TechArticle)
108- `headline`: Article title
109- `datePublished`: ISO 8601
110- `dateModified`: ISO 8601 (critical for freshness signals)
111- `author`: Person or Organization schema
112- `publisher`: Organization schema with logo
113- `image`: Representative image
114 
115**Author (Person) required for GEO:**
116- `name`: Full name
117- `url`: Author page URL on the site
118- `sameAs`: LinkedIn, Twitter, personal site, Google Scholar, ORCID
119- `jobTitle`: Professional title
120- `worksFor`: Organization schema
121- `knowsAbout`: Array of expertise areas
122- `alumniOf`: Educational institutions
123- `award`: Professional awards
124 
125### Product (for e-commerce)
126**Required:**
127- `name`, `description`, `image`
128- `offers`: Offer with price, priceCurrency, availability
129- `brand`: Brand schema
130- `sku` or `gtin`/`mpn`
131 
132**Recommended for GEO:**
133- `aggregateRating`: AggregateRating
134- `review`: Array of individual reviews
135- `category`: Product category
136- `material`, `weight`, `width`, `height` (where applicable)
137 
138### FAQPage
139**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.
140 
141**Structure:**
142- `@type`: "FAQPage"
143- `mainEntity`: Array of Question schemas, each with `acceptedAnswer` containing an Answer schema
144 
145### SoftwareApplication (for SaaS)
146**Required:**
147- `name`, `description`
148- `applicationCategory`: e.g., "BusinessApplication"
149- `operatingSystem`: Supported platforms
150- `offers`: Pricing
151 
152**Recommended for GEO:**
153- `aggregateRating`: User ratings
154- `featureList`: Array of features (strong citation signal)
155- `screenshot`: Screenshots
156- `softwareVersion`: Current version
157- `releaseNotes`: Link to changelog
158 
159### WebSite + SearchAction (for sitelinks search box)
160**Structure:**
161```json
162{
163 "@type": "WebSite",
164 "name": "Site Name",
165 "url": "https://example.com",
166 "potentialAction": {
167 "@type": "SearchAction",
168 "target": {
169 "@type": "EntryPoint",
170 "urlTemplate": "https://example.com/search?q={search_term_string}"
171 },
172 "query-input": "required name=search_term_string"
173 }
174}
175```
176 
177### Person (standalone — for personal brands, authors, thought leaders)
178Use as a standalone schema on About/Bio pages. This builds the entity graph for individual expertise.
179 
180**Required:** `name`, `url`
181**Recommended for GEO:** `sameAs`, `jobTitle`, `worksFor`, `knowsAbout`, `alumniOf`, `award`, `description`, `image`
182 
183### speakable Property (for voice/AI assistants)
184The `speakable` property marks specific sections of content as particularly suitable for voice and AI assistant consumption. Add to Article or WebPage schemas.
185 
186```json
187{
188 "@type": "Article",
189 "speakable": {
190 "@type": "SpeakableSpecification",
191 "cssSelector": [".article-summary", ".key-takeaway"]
192 }
193}
194```
195This signals to AI assistants which passages are the best candidates for citation or reading aloud.
196 
197---
198 
199## Step 4: Deprecated/Changed Schemas to Flag
200 
201| Schema | Status | Note |
202|---|---|---|
203| HowTo | Rich results deprecated Aug 2023 | Still useful for AI parsing, but do not promise rich results |
204| FAQPage | Restricted to govt/health Aug 2023 | Still useful for AI parsing (see above) |
205| SpecialAnnouncement | Deprecated 2023 | Was for COVID; remove if still present |
206| CourseInfo | Replaced by Course updates 2024 | Use updated Course schema properties |
207| VideoObject `contentUrl` | Changed behavior 2024 | Must point to actual video file, not page URL |
208| Review snippet | Stricter enforcement 2024 | Self-serving reviews on product pages may not display |
209 
210Flag any deprecated schemas found and recommend replacements.
211 
212---
213 
214## Step 5: sameAs Strategy (CRITICAL for Entity Recognition)
215 
216The `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.
217 
218### Recommended sameAs Links (in priority order)
219 
2201. **Wikipedia article** — highest authority entity link
2212. **Wikidata item** — machine-readable entity identifier (e.g., `https://www.wikidata.org/wiki/Q12345`)
2223. **LinkedIn** — company page or personal profile
2234. **YouTube** — channel URL
2245. **Twitter/X** — profile URL
2256. **Facebook** — page URL
2267. **Crunchbase** — company profile (for startups/tech)
2278. **GitHub** — organization or personal profile (for tech)
2289. **Google Scholar** — author profile (for researchers/academics)
22910. **ORCID** — researcher identifier (for academics)
23011. **Instagram** — profile URL
23112. **Apple App Store / Google Play** — app listings (for software)
23213. **BBB** — Better Business Bureau listing (for US businesses)
23314. **Industry directories** — relevant vertical directories
234 
235### sameAs Audit Process
2361. Collect all known web presences for the entity
2372. Check that each URL resolves (not 404 or redirected)
2383. Verify the Organization/Person schema includes ALL of them
2394. Check that the information on each platform is consistent (name, description, founding date, etc.)
2405. Flag any platforms where the entity should have a presence but does not
241 
242---
243 
244## Step 6: JSON-LD Generation
245 
246Based on the detected business type, generate ready-to-paste JSON-LD blocks. Always generate:
247 
2481. **Organization or Person** (depending on entity type) — always
2492. **WebSite with SearchAction** — always for the homepage
2503. **Business-type-specific** — Article for publishers, Product for e-commerce, LocalBusiness for local, SoftwareApplication for SaaS
2514. **BreadcrumbList** — for any page deeper than homepage
252 
253### Generation Rules
254- Use the `@graph` pattern to include multiple schemas in one JSON-LD block
255- All URLs must be absolute (not relative)
256- Include `@id` properties for cross-referencing between schemas
257- Use ISO 8601 for all dates
258- Include `speakable` on Article schemas with CSS selectors pointing to key content sections
259- Place JSON-LD in `<head>` section — NOT injected via JavaScript
260 
261### Template: Organization with Full GEO Signals
262```json
263{
264 "@context": "https://schema.org",
265 "@type": "Organization",
266 "@id": "https://example.com/#organization",
267 "name": "Company Name",
268 "url": "https://example.com",
269 "logo": {
270 "@type": "ImageObject",
271 "url": "https://example.com/logo.png",
272 "width": 600,
273 "height": 60
274 },
275 "description": "Concise description of what the company does.",
276 "foundingDate": "2020-01-15",
277 "founder": {
278 "@type": "Person",
279 "name": "Founder Name",
280 "sameAs": "https://www.linkedin.com/in/founder"
281 },
282 "address": {
283 "@type": "PostalAddress",
284 "streetAddress": "123 Main St",
285 "addressLocality": "City",
286 "addressRegion": "State",
287 "postalCode": "12345",
288 "addressCountry": "US"
289 },
290 "contactPoint": {
291 "@type": "ContactPoint",
292 "telephone": "+1-555-555-5555",
293 "contactType": "customer service",
294 "email": "[email protected]"
295 },
296 "sameAs": [
297 "https://en.wikipedia.org/wiki/Company_Name",
298 "https://www.wikidata.org/wiki/Q12345",
299 "https://www.linkedin.com/company/company-name",
300 "https://www.youtube.com/@companyname",
301 "https://twitter.com/companyname",
302 "https://github.com/companyname",
303 "https://www.crunchbase.com/organization/company-name"
304 ],
305 "knowsAbout": [
306 "Topic 1",
307 "Topic 2",
308 "Topic 3"
309 ]
310}
311```
312 
313---
314 
315## Scoring Rubric (0-100)
316 
317| Criterion | Points | How to Score |
318|---|---|---|
319| Organization/Person schema present and complete | 15 | 15 if full, 10 if basic, 0 if none |
320| sameAs links (5+ platforms) | 15 | 3 per valid sameAs link, max 15 |
321| Article schema with author details | 10 | 10 if full author schema, 5 if name only, 0 if none |
322| Business-type-specific schema present | 10 | 10 if complete, 5 if partial, 0 if missing |
323| WebSite + SearchAction | 5 | 5 if present, 0 if not |
324| BreadcrumbList on inner pages | 5 | 5 if present, 0 if not |
325| JSON-LD format (not Microdata/RDFa) | 5 | 5 if JSON-LD, 3 if mixed, 0 if only Microdata/RDFa |
326| Server-rendered (not JS-injected) | 10 | 10 if in HTML source, 5 if JS but in head, 0 if dynamic JS |
327| speakable property on articles | 5 | 5 if present, 0 if not |
328| Valid JSON + valid Schema.org types | 10 | 10 if no errors, 5 if minor issues, 0 if major errors |
329| knowsAbout property on Organization/Person | 5 | 5 if present with 3+ topics, 0 if missing |
330| No deprecated schemas present | 5 | 5 if clean, 0 if deprecated schemas found |
331 
332---
333 
334## Output Format
335 
336Generate **GEO-SCHEMA-REPORT.md** with:
337 
338```markdown
339# GEO Schema & Structured Data Report — [Domain]
340Date: [Date]
341 
342## Schema Score: XX/100
343 
344## Detected Schemas
345| Page | Schema Type | Format | Status | Issues |
346|---|---|---|---|---|
347| / | Organization | JSON-LD | Valid | Missing sameAs |
348| /blog/post-1 | Article | JSON-LD | Valid | No author schema |
349 
350## Validation Results
351[List each schema with pass/fail per property]
352 
353## Missing Recommended Schemas
354[List schemas that should be present based on business type but are not]
355 
356## sameAs Audit
357| Platform | URL | Status |
358|---|---|---|
359| Wikipedia | [URL or "Not found"] | Present/Missing |
360| LinkedIn | [URL or "Not found"] | Present/Missing |
361[Continue for all recommended platforms]
362 
363## Generated JSON-LD Code
364[Ready-to-paste JSON-LD blocks for each missing or incomplete schema]
365 
366## Implementation Notes
367- Where to place each JSON-LD block
368- Server-rendering requirements
369- Testing with Google Rich Results Test and Schema.org Validator
370```
371 
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Details

Version
1.0.0
Updated
Sep 21, 2026
License
MIT
Stars on the repo
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zubair-trabzada/geo-seo-claude ↗
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