Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub · AI
Schema Markup Generator
Generate complete, validated Schema.org JSON-LD markup for any content type to boost AI citation rates. Creates structured data for Organization, FAQPage, Ar...
skill openclawclawhub Free
Generate complete, validated Schema.org JSON-LD markup for any content type to boost AI citation rates. Creates structured data for Organization, FAQPage, Ar...
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Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- SKILL.md, scripts/validate_schema.py, scripts/generate_schema.py, scripts/batch_generate.py, references/examples.md, references/google-guidelines.md
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.1
Provenance
- Publisher
- geoly-geo
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
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 <schema-type> [--url <page-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
TypePriorityBest ForOrganization🔴 CriticalHomepage, About page — establishes brand entityFAQPage🔴 CriticalFAQ/Support pages — feeds AI Q&A answersArticle / BlogPosting🟡 HighBlog posts, news — improves citabilityProduct🟡 HighProduct/pricing pages — enables shopping citationsHowTo🟡 HighTutorials, guides — feeds step-by-step answersWebSite🟡 HighHomepage — enables site search in AIBreadcrumbList🔵 MediumAll pages — improves navigation understandingVideoObject🔵 MediumVideo pages — enables video citationsImageObject🔵 MediumImage galleries — enables image citationsLocalBusiness🔵 MediumPhysical locations — local AI search Full schema reference: See references/schema-types.md
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
Add to Your Page
Paste the generated JSON-LD inside your HTML <head>: <head> <script type="application/ld+json"> { "@context": "https://schema.org", "@type": "Organization", ... } </script> </head>
Test Before Deploying
Schema.org Validator: https://validator.schema.org Google Rich Results Test: https://search.google.com/test/rich-results 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
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 <script> 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
Category context
Agent frameworks, memory systems, reasoning layers, and model-native orchestration.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
3 Docs3 Scripts
- SKILL.md
- references/examples.md
- references/google-guidelines.md
- scripts/batch_generate.py
- scripts/generate_schema.py
- scripts/validate_schema.py