Best use case
structured-data is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
JSON-LD schema markup and validation.
Teams using structured-data should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/structured-data/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How structured-data Compares
| Feature / Agent | structured-data | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
JSON-LD schema markup and validation.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Structured Data Skill
Expert assistance for JSON-LD structured data.
## Capabilities
- Implement JSON-LD schemas
- Validate structured data
- Configure rich results
- Handle dynamic data
- Test with Google tools
## Schema Examples
```tsx
// Article
<script type="application/ld+json">
{JSON.stringify({
"@context": "https://schema.org",
"@type": "Article",
"headline": title,
"author": {
"@type": "Person",
"name": author.name
},
"datePublished": publishedAt,
"image": imageUrl
})}
</script>
// Organization
{
"@context": "https://schema.org",
"@type": "Organization",
"name": "Company Name",
"url": "https://example.com",
"logo": "https://example.com/logo.png"
}
```
## Target Processes
- structured-data-implementation
- rich-results
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