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schema-markup▌

kostja94/marketing-skills · updated Apr 8, 2026

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$npx skills add https://github.com/kostja94/marketing-skills --skill schema-markup
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summary

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

skill.md

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.

Engine Support Notes
Google Subset only Only types in Google's search gallery generate rich results. Valid Schema.org markup not in Google's list won't produce enhanced snippets—even if technically correct.
Bing Subset; different Supports JSON-LD, Microdata, RDFa, Open Graph. Some types (e.g., Product, Offer) have format-specific support. Check Bing Webmaster docs.
Other engines Varies Yandex, DuckDuckGo, AI search tools (Perplexity, etc.) may use Schema.org for understanding even when they don't display 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.
Schema Type SERP Feature / Rich Result Notes
FAQPage PAA, Featured Snippet FAQ dropdown; Q&A-style snippet. Eligibility restricted for many sites (e.g. government/health)
BreadcrumbList Breadcrumbs Path display in result
AggregateRating, Review Reviews / Stars Star ratings
HowTo Featured Snippet (list) Step-based snippet; desktop support; mobile may be limited
Article In-Depth Articles, Snippet Article rich result
VideoObject Video Video thumbnail; see video-optimization
Product, Offer Shopping, Product Product/shopping results
Recipe Recipe Recipe rich result
JobPosting Google Jobs Job listings
Event Event Event rich result
WebSite + SearchAction Sitelinks searchbox Site links for brand queries
Organization, Person Knowledge Panel Entity info; see entity-seo

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 project context first: If .claude/project-context.md or .cursor/project-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)

Type Use case
Organization Site-wide; company info, logo, sameAs; see placement below
WebSite Site-wide; search action, site name; pair with Organization on homepage
Article Blog posts, news, tool intros
BreadcrumbList Breadcrumb navigation
FAQPage FAQ sections; triggers PAA-style results
Person Author info; pairs with Article
ImageObject Image metadata for rich results
HowTo Tutorials, step-by-step guides. Note: Google may have deprecated HowTo rich results (2023–2024); Schema.org still supports it; Bing/AI may use it

Exclusive Types (Specific Scenarios)

Type Use case
JobPosting Recruitment sites, AI Job Matching
Product E-commerce product pages
Event Event pages, ticketing (not general blogs)
SoftwareApplication App pages, tool pages
LocalBusiness Local business pages
Dataset Data platforms, datasets
DiscussionForumPosting Forums, community posts
Quiz Education, flashcards
MathSolver Math tools
CaseStudy Case study pages
Recipe Recipes, meal plans, cooking instructions

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).

Organization & WebSite Schema Placement

Where Organization WebSite Notes
Homepage Minimum Minimum Add both Organization and WebSite to homepage at least. Organization describes the entity that owns the site; WebSite enables sitelinks searchbox and site identity.
Root layout / global Optimal Optimal Place in site-wide layout (e.g. layout.tsx, _document, global header/footer) so schema appears on every page. Google uses the first instance found; one instance per site is sufficient.
About page No No About page uses AboutPage schema (page-specific: headline, description, author, about). Organization is entity-level, not page-level—do not confine it to About. See about-page-generator.

Implementation: JSON-LD in <head>; use @id (e.g. https://example.com/#organization) to link Organization ↔ WebSite ↔ WebPage for entity graph. See entity-seo for @id and Knowledge Panel.

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.

Website / Product type Recommended exclusive schema Why
AI meal planner, recipe site, food blog, cooking app Recipe Ingredients, instructions, cook time, servings—highly relevant for food/meal content. Google supports Recipe rich results.
Job board, recruitment site, careers page JobPosting Title, company, location, salary, employment type. Required for Google Jobs.
Event platform, ticketing, webinar, conference Event Date, location, price. Use only on actual event pages.
SaaS, app, Chrome extension, tool, software product page SoftwareApplication App name, category, rating, price, OS. Fits product/feature pages.
E-commerce product page Product Price, availability, brand, reviews. Use with Offer, AggregateRating.
Forum, community, Reddit-style, Q&A DiscussionForumPosting Post content, author, comments. For user-generated discussion.
Data platform, dataset repository, Scale AI / Surge AI Dataset Dataset name, creator, license, distribution format. For data catalog pages.
Education site, flashcards, Quizlet-style Quiz Question-answer pairs. For educational Q&A content.
Math solver, calculator, equation tool MathSolver Math problem input, solution output. For math tools.
Restaurant, local service, store locator LocalBusiness Address, hours, NAP. For local SEO.
Case study, customer story page CaseStudy Client, outcome, methodology. For B2B case studies.
FAQ page, product FAQ, support FAQ FAQPage Question + acceptedAnswer pairs. Triggers PAA-style results.
Tutorial, how-to guide, step-by-step HowTo Steps, tools, time. Note: Google may have deprecated rich results; Bing/AI may still use.
News article, press release NewsArticle Use instead of Article for news.
Video page, podcast episode VideoObject / PodcastEpisode For video/audio content. See video-optimization for VideoObject, thumbnail, key moments.

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).

Article / BlogPosting / NewsArticle: Type Selection & Implementation

Choose the most specific type that matches content:

Type Use case
BlogPosting Informal blog posts; individual authors; regularly updated
Article Formal, evergreen content; tool intros; encyclopedic
NewsArticle Time-sensitive news; recognized publishers

Required properties: headline (max 110 chars), image (min 1200px wide; absolute URL), datePublished (ISO 8601), author (Person or Organization), publisher (Organization with logo).

Recommended: dateModified, description, mainEntityOfPage (canonical URL).

Date display for CTR: Google recommends showing only one date on the page. If both datePublished and dateModified are visible, Google may pick the wrong date for SERP display—Search Engine Land saw ~22% CTR drop. Best practice: show dateModified if it exists, otherwise datePublished. Keep both in JSON-LD; the rule applies to visible date only.

JSON-LD example (BlogPosting):

{
  "@context": "https://schema.org",
  "@type": "BlogPosting",
  "headline": "The Ultimate SEO Checklist for 2025",
  "description": "A complete guide to optimizing blog posts for search and AI.",
  "image": "https://example.com/image.jpg",
  "datePublished": "2025-01-15T09:00:00Z",
  "dateModified": "2025-02-01T14:30:00Z",
  "author": { "@type": "Person", "name": "Jane Doe", "url": "https://example.com/author/jane" },
  "publisher": { "@type": "Organization", "name": "Example", "logo": { "@type": "ImageObject", "url": "https://example.com/logo.png" } }
}

Place in <head> via <script type="application/ld+json">. For article pages, use og:type: article with og:article:published_time, og:article:modified_time, og:article:author. See article-page-generator, open-graph.

BreadcrumbList

For breadcrumb navigation. Schema must match visible breadcrumbs exactly. See breadcrumb-generator for UI, placement, and semantic HTML.

Requirement Guideline
Format JSON-LD in <script type="application/ld+json">
URLs Absolute URLs with https:// for each item
Position Sequential integers starting from 1
Match Schema must match visible breadcrumbs exactly

JSON-LD example:

{
  "@context": "https://schema.org",
  "@type": "BreadcrumbList",
  "itemListElement": [
    { "@type": "ListItem", "position": 1, "name": "Home", "item": "https://example.com/" },
    { "@type": "ListItem", "position": 2, "name": "Category", "item": "https://example.com/category/" },
    { "@type": "ListItem", "position": 3, "name": "Current Page", "item"
how to use schema-markup
How to use schema-markup on Cursor
AI-first code editor with Composer
1
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
  • ›Cursor installed and configured on your development machine
  • ›Node.js version 16.0+ with npm package manager (verify with node --version)
  • ›Active project directory or workspace where you want to add schema-markup
2
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
$npx skills add https://github.com/kostja94/marketing-skills --skill schema-markup
The skills CLI fetches schema-markup from GitHub repository kostja94/marketing-skills and configures it for Cursor.
3
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
◆ Which agents do you want to install to?
│
│ ── Universal (.agents/skills) ── always included ────
│   • Amp
│   • Antigravity
│   • Cline
│   • Codex
│ ●Cursor(selected)
│   • Cursor
│   • Windsurf
4
Verify installation
Confirm successful installation by checking the skill directory location:
.cursor/skills/schema-markup
Reload or restart Cursor to activate schema-markup. Access the skill through slash commands (e.g., /schema-markup) or your agent's skill management interface.
⚠
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
Additional Resources
›View source code on GitHub›Skills CLI documentation›Learn more about Cursor›What are agent skills?

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Use Cases▌

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

✓

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

✓

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

✓

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

✓

Save 3-5 hours/week on communication overhead

Implementation Guide▌

Prerequisites

  • ›Claude Desktop or compatible AI client
  • ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • ›Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • ⚠Not validating competitive research—verify facts before sharing
  • ⚠Accepting user stories without involving engineering team
  • ⚠Over-relying on frameworks without qualitative judgment
  • ⚠Not customizing outputs to company culture and communication style
  • ⚠Skipping stakeholder validation of generated requirements

Best Practices▌

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • −Don't publish competitive analysis without fact-checking
  • −Don't finalize user stories without engineering review
  • −Don't make prioritization decisions solely on AI scoring
  • −Don't skip customer validation of generated requirements
  • −Don't ignore company-specific context and culture

💡 Pro Tips

  • ★Provide context: company goals, constraints, customer feedback
  • ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • ★Use skill for 70% generation + 30% customization to company needs

When to Use This▌

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path▌

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.7★★★★★73 reviews
  • ★★★★★Sophia Sanchez· Dec 28, 2024

    Registry listing for schema-markup matched our evaluation — installs cleanly and behaves as described in the markdown.

  • ★★★★★Olivia Farah· Dec 28, 2024

    schema-markup is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • ★★★★★Pratham Ware· Dec 24, 2024

    I recommend schema-markup for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • ★★★★★Kwame Dixit· Dec 24, 2024

    schema-markup fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • ★★★★★Charlotte Menon· Dec 16, 2024

    I recommend schema-markup for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • ★★★★★Noor Khan· Nov 27, 2024

    schema-markup has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • ★★★★★Henry Torres· Nov 19, 2024

    schema-markup reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • ★★★★★Yash Thakker· Nov 15, 2024

    Useful defaults in schema-markup — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • ★★★★★Charlotte Tandon· Nov 11, 2024

    Registry listing for schema-markup matched our evaluation — installs cleanly and behaves as described in the markdown.

  • ★★★★★Jin Thompson· Nov 7, 2024

    Useful defaults in schema-markup — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

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