SEO On-Page: Schema / Structured Data

Guides implementation of Schema.org structured data (JSON-LD) for rich snippets, enhanced search results, and Generative Engine Optimization (GEO).

When invoking: On first use, if helpful, open with 1–2 sentences on what this skill covers and why it matters, then provide the main output. On subsequent use or when the user asks to skip, go directly to the main output.

Scope (On-Page SEO)

• Schema markup: Schema.org types for rich results, AI search visibility, and machine-readable content
• Schema.org vs. search engines: Schema.org defines 800+ types; each search engine supports only a subset for rich results

Schema.org vs. Search Engine Support

Schema.org and Google Structured Data are not fully aligned. Schema.org is an open vocabulary (800+ types); Google, Bing, and other engines each support only a curated subset for rich results.

Practical implication: Implement Schema.org markup for your content type. If Google doesn't show rich results for that type, Bing or AI systems may still use it. Always verify against Google's developer docs for Google-specific rich result eligibility.

Rich Results: Google Support (2025)

High-impact types: Product, Review snippets, HowTo (desktop), Article/News, Video, Recipe, LocalBusiness, Event, Breadcrumb, Sitelinks searchbox, JobPosting.

Limited or context-dependent: HowTo (mobile), FAQ (government/health sites for many queries), Education Q&A, Course, SoftwareApplication, Speakable (news), DiscussionForumPosting.

Deprecated: COVID data panels, some AMP-only formats, data-vocabulary.org.

Implementation: JSON-LD preferred; include @context, @type, stable @id; ISO 8601 dates; match structured data to visible content. Validate with Rich Results Test. Rich results can increase CTR up to ~35% and improve AI citation. AISO Hub, Digital Applied

Schema ↔ SERP Features ↔ Rich Results (Strongly Related)

Schema, SERP features, and rich results are strongly related. Schema is the necessary condition for most rich results. When targeting a SERP feature, implement the corresponding schema type. See serp-features for the full SERP feature list and optimization.

Rich Results vs Featured Snippets

• Rich results: Schema-powered enhancements to standard listings (stars, breadcrumbs, FAQ dropdowns, product info). Appear within organic positions; do not require top-10 rank.
• Featured snippets: Google-extracted answer boxes at position zero. No schema required; content structure matters. Schema (FAQPage, HowTo, Article) can support extraction.

Workflow: 1) Use serp-features to identify target SERP feature; 2) Look up schema type in this table; 3) Implement and validate with Rich Results Test.

Generative Engine Optimization (GEO)

GEO = optimizing content so AI systems (Google AI Overviews, Perplexity, ChatGPT, Gemini) choose, cite, and quote your content in generated answers. Structured data makes content machine-readable; AI engines extract and cite more accurately. Key schema types for GEO: Organization, Person/Author, WebSite, WebPage, FAQPage, HowTo, Article, Product, AggregateRating. See generative-engine-optimization for full GEO strategy.

Initial Assessment

Check for product marketing context first: If .claude/product-marketing-context.md or .cursor/product-marketing-context.md exists, read it for product type and content.

Identify:

1. Page type: Article, Product, FAQ, Organization, JobPosting, Event, etc.
2. Content: What entities to describe
3. Goal: Rich snippets, AI Overview visibility, Knowledge Panel

Schema Type Classification

Core Types (General Use)

Exclusive Types (Specific Scenarios)

Rule: Use core types for most sites. Use exclusive types only when page content matches (e.g., don't use Event on a blog; don't use JobPosting on a product page).

Action: Website/Product Type → Schema Mapping

Use this table to recommend which exclusive schema types fit a site. Match the site's content and product type to the most relevant schema. When in doubt, start with core types (Organization, WebSite, Article); add exclusive types only when content clearly matches.

Examples:

• AI meal planner (e.g., generates weekly meal plans with recipes) → Add Recipe schema to each recipe/meal page; Article or WebPage for landing pages
• AI writing tool → SoftwareApplication on product page; Article on blog
• Recruitment SaaS → JobPosting on job listing pages; SoftwareApplication on product page
• Recipe blog → Recipe on each recipe post; Article for non-recipe posts

Output: When recommending schema, state: (1) which exclusive types fit the site/product, (2) which page types get which schema, (3) core types to add site-wide (Organization, WebSite, BreadcrumbList).

Best Practices

Multilingual Schema (inLanguage)

For multilingual sites, add inLanguage to JSON-LD to reinforce language targeting. Align with hreflang values (e.g. "inLanguage": "zh-CN" with hreflang="zh-CN").

Localize schema data: Translate structured data fields (name, description, FAQ acceptedAnswer, etc.) for each locale to improve rich snippet CTR in that language.

Types that support inLanguage: Article, BlogPosting, WebApplication, FAQPage, HowTo, Product, Organization.

Implementation Workflow

1. Analyze page type and content; choose matching Schema type
2. Select format — JSON-LD recommended (Google, Bing, AI tools support it)
3. Write structured data; start with required properties
4. Validate with Rich Results Test, Schema Markup Validator
5. Deploy and monitor via Search Console enhanced reports

Common Errors and Fixes

Implementation

Next.js (metadata)

export const metadata = {
  other: {
    'script:ld+json': JSON.stringify({
      "@context": "https://schema.org",
      "@type": "Article",
      "headline": "...",
      "description": "...",
      "inLanguage": "en-US",
      "image": "https://example.com/image.jpg",
      "datePublished": "2024-01-01T00:00:00Z",
      "dateModified": "2024-01-15T00:00:00Z",
      "author": { "@type": "Person", "name": "..." },
      "publisher": { "@type": "Organization", "name": "...", "logo": { "@type": "ImageObject", "url": "..." } }
    }),
  },
};

HTML (generic)

<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "Article",
  "headline": "...",
  "description": "...",
  "inLanguage": "en-US",
  "author": { "@type": "Person", "name": "..." },
  "publisher": { "@type": "Organization", "name": "...", "logo": { "@type": "ImageObject", "url": "..." } }
}
</script>

Validation Tools

Output Format

• Action first: Use the Website/Product Type → Schema Mapping table to recommend which exclusive schema fits the site (e.g., AI meal planner → Recipe; SaaS tool → SoftwareApplication)
• Schema type recommendation (core vs. exclusive)
• Page-level mapping: Which pages get which schema
• JSON-LD structure with required properties
• Validation steps
• References: Schema.org, Google Structured Data, Bing Markup

Related Skills

• article-page-generator: Article page structure; Article/BlogPosting/NewsArticle schema implementation
• faq-page-generator: FAQPage schema; FAQ content structure
• title-tag, meta-description, page-metadata: Metadata complements schema
• heading-structure: Article schema uses headline (often H1)
• indexing: Google Indexing API for JobPosting, BroadcastEvent
• generative-engine-optimization: GEO strategy and AI search visibility
• breadcrumb-generator: BreadcrumbList schema implementation
• serp-features: Strongly related—schema maps to SERP features; use with schema-markup when targeting rich results
• featured-snippet: Featured Snippet optimization; FAQPage, HowTo for snippets