| name | schema-generator |
| description | Generate JSON-LD structured data markup for any website. Supports HowTo, FAQPage, Article, BlogPosting, Organization, WebSite, BreadcrumbList, Product, LocalBusiness, and MedicalWebPage types. |
| user-invocable | true |
| allowed-tools | Bash, Read, Grep, Glob, WebFetch |
| context | fork |
Schema Generator Skill
You generate production-ready JSON-LD structured data for any website to maximize AI engine comprehension and rich result eligibility.
Input
The user may provide:
- A URL to generate schema for
- A content file path to generate schema for
- A schema type to generate (e.g., "HowTo for onboarding guide")
- "site-wide" to generate Organization + WebSite + BreadcrumbList
If a brand.md exists in the project root, read it for publisher details (name, url, logo). Otherwise, infer from the target page.
Schema Selection Logic
| Page Type | Schema Types to Generate |
|---|
| Step-by-step guide / tutorial | HowTo + FAQPage + BreadcrumbList |
| Blog post / article | BlogPosting + FAQPage + BreadcrumbList |
| Long-form research article | Article + FAQPage + BreadcrumbList |
| Category / collection page | CollectionPage + ItemList + BreadcrumbList |
| Homepage | WebSite + Organization |
| About / company page | AboutPage + Organization |
| Product page | Product + FAQPage + BreadcrumbList |
| Local business | LocalBusiness + FAQPage |
| Health / medical content | MedicalWebPage + FAQPage + BreadcrumbList |
Generation Process
Step 1: Run Schema Check
python3 scripts/check-schema.py <url>
Review the output: what schema types are already present, what's missing, what's incomplete. Use this as the baseline — only generate what's needed.
Step 2: Analyze Content
Read the page/content and identify:
- Primary topic and @type
- All steps (for HowTo)
- All Q&A pairs (for FAQ)
- Key entities and relationships
- Dates (published, modified)
- Authors and citations
Step 3: Generate JSON-LD
Produce each schema block as a separate <script> tag:
<script type="application/ld+json">
{
"@context": "https://schema.org",
"@type": "...",
...
}
</script>
Step 4: Validate
echo '<json-ld-content>' | python3 -m json.tool > /dev/null && echo "VALID" || echo "INVALID"
Step 5: Cross-Reference
Ensure consistency:
- Organization name matches across all blocks
- URLs are absolute and correct
- Dates are in ISO 8601 format (YYYY-MM-DD)
- No placeholder text remaining
Output
For each page, produce:
- All applicable JSON-LD blocks
- Implementation notes (where to place in HTML
<head>)
- Rich result eligibility assessment
- Validation status
Save to schema/[page-slug]-schema.json.
