Agent skill

Schema Markup

by LeoYeAI in LeoYeAI/openclaw-master-skills

When the user wants to add or optimize structured data (Schema.org, JSON-LD).

Install Schema Markup

skills CLI

$ npx skills add LeoYeAI/openclaw-master-skills --skill schema-markup -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI

$ gh skill install LeoYeAI/openclaw-master-skills schema-markup --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy

$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/kostja94-schema-markup .claude/skills/schema-markup && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Paste into Claude Code

Install the "schema-markup" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/kostja94-schema-markup into .claude/skills/schema-markup/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "schema-markup", then confirm the skill loads.

Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

$skill-installer

$skill-installer install https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/kostja94-schema-markup

Type this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.

skills CLI

$ npx skills add LeoYeAI/openclaw-master-skills --skill schema-markup -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install LeoYeAI/openclaw-master-skills schema-markup --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/kostja94-schema-markup .agents/skills/schema-markup && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "schema-markup" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/kostja94-schema-markup into .agents/skills/schema-markup/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "schema-markup", then confirm the skill loads.

Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add LeoYeAI/openclaw-master-skills --skill schema-markup -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install LeoYeAI/openclaw-master-skills schema-markup --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/kostja94-schema-markup .cursor/skills/schema-markup && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "schema-markup" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/kostja94-schema-markup into .cursor/skills/schema-markup/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "schema-markup", then confirm the skill loads.

Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

gemini skills install

$ gemini skills install https://github.com/LeoYeAI/openclaw-master-skills.git --path skills/kostja94-schema-markup

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add LeoYeAI/openclaw-master-skills --skill schema-markup -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install LeoYeAI/openclaw-master-skills schema-markup --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/kostja94-schema-markup .gemini/skills/schema-markup && rm -rf skills-src

Use ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.

Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/

Paste into Gemini CLI

Install the "schema-markup" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/kostja94-schema-markup into .gemini/skills/schema-markup/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "schema-markup", then confirm the skill loads.

Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

GitHub CLI

$ gh skill install LeoYeAI/openclaw-master-skills schema-markup

Installs for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).

skills CLI

$ npx skills add LeoYeAI/openclaw-master-skills --skill schema-markup -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/kostja94-schema-markup .github/skills/schema-markup && rm -rf skills-src

Use ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.

GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/

Paste into GitHub Copilot

Install the "schema-markup" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/kostja94-schema-markup into .github/skills/schema-markup/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "schema-markup", then confirm the skill loads.

GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add LeoYeAI/openclaw-master-skills --skill schema-markup -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install LeoYeAI/openclaw-master-skills schema-markup --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/kostja94-schema-markup .opencode/skills/schema-markup && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "schema-markup" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/kostja94-schema-markup into .opencode/skills/schema-markup/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "schema-markup", then confirm the skill loads.

OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

Facts

Skill name: schema-markup
GitHub stars: 2.2k
Token cost: ~4.9k tokens
SKILL.md length: 1,925 words
Files: 2
Skills in repo: 1,235
Repo updated: Jul 20, 2026
First seen: Oct 7, 2026
Licence: MIT

At a glance

When the user wants to add or optimize structured data (Schema.org, JSON-LD).

Works in 3 steps: Page type: Article, Product, FAQ,… → Content: What entities to describe → Goal: Rich snippets, AI Overview…
Optimize structured data (Schema.org
SKILL.md covers Scope (On-Page SEO), Schema.org vs. Search Engine…, Rich Results: Google Support… and Schema ↔ SERP Features ↔ Rich…, plus 11 more sections
Reaches schema.org

What it does

Schema Markup is an agent skill from LeoYeAI/openclaw-master-skills. When the user wants to add or optimize structured data (Schema.org, JSON-LD). Also use when the user mentions "schema," "structured data," "JSON-LD," "rich results," "rich snippets," "Google rich snippets," "featured snippet schema," "add schema to page," "missing structured data," "schema validation error," "Schema Markup Validator," "Google Rich Results Test," "FAQ schema," "Article schema," "Organization schema," "JobPosting," "HowTo," "Event," "SoftwareApplication," "BreadcrumbList," "WebSite," "Recipe,"…

Its SKILL.md is about 4.9k tokens, which your agent loads only when the skill is triggered. The skill folder holds 1 other file (for example `_meta.json`).

It sits in Marketing & SEO, covering Schema markup. The repository describes itself as: 🧠 Curated collection of 1209+ best OpenClaw skills — weekly updated by MyClaw.ai. The licence is MIT.

When your agent uses it

Optimize structured data (Schema.org
The user mentions schema
Structured data
Google rich snippets

Example prompts

“schema,”
“structured data,”
“JSON-LD,”
“/schema-markup”

Workflow steps

3 steps, taken from the first numbered list in SKILL.md.

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

What it can do on your machine

Read from SKILL.md and the folder at commit e5199b5. It shows what the files ask for, not the result of running them.

Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md (its code samples are json, typescript and html).
From the folder's file list and the shell code blocks in SKILL.md.
Network
Hosts in commands or code, which the agent is likely to contact:
- schema.org
Also links to:
- developers.google.com
- search.google.com
- bing.com
- validator.schema.org
- aiso-hub.com
- digitalapplied.com
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Schema Markup loads about 4.9k tokens when it runs. Until then it costs about 140 tokens; SKILL.md has 1,925 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~140
When it runs · the whole SKILL.md, loaded when a task matches: ~4.9k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from LeoYeAI/openclaw-master-skills at commit e5199b5, republished under its MIT licence (© LeoYeAI). 1,925 words, ~4,937 tokens.

Download SKILL.mdSave it as .claude/skills/schema-markup/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.

name: schema-markup
description: When the user wants to add or optimize structured data (Schema.org, JSON-LD). Also use when the user mentions "schema," "structured data," "JSON-LD," "rich results," "rich snippets," "Google rich snippets," "featured snippet schema," "add schema to page," "missing structured data," "schema validation error," "Schema Markup Validator," "Google Rich Results Test," "FAQ schema," "Article schema," "Organization schema," "JobPosting," "HowTo," "Event," "SoftwareApplication," "BreadcrumbList," "WebSite," "Recipe," "Product," "Dataset," or "GEO."
metadata.version: 1.3.0

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, 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:

Page type: Article, Product, FAQ, Organization, JobPosting, Event, etc.
Content: What entities to describe
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).

Show full SKILL.md (637 more words)Show less

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):

json

{
  "@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:

json

{
  "@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": "https://example.com/category/current-page/" }
  ]
}

Multiple paths: Google supports multiple BreadcrumbList objects on the same page when a page is reachable via multiple paths (e.g., product in multiple categories). Use an array of BreadcrumbList objects.

Best Practices

Principle	Guideline
Accuracy	Data must match visible page content; never add invisible or misleading data
Completeness	Include all required properties per type
Most specific type	Use NewsArticle over Article when applicable
JSON-LD	Preferred format; place in `<script type="application/ld+json">`
@id for entities	Use @id for Organization, Person to enable entity linking; see entity-seo
Phased implementation	Add required properties first; then optional for optimization
Validation	Test with Rich Results Test and Schema Markup Validator
inLanguage (multilingual)	Add `"inLanguage": "en-US"` (IETF BCP 47) to match hreflang; localize names, descriptions, FAQs for rich snippets per locale

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

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

Common Errors and Fixes

Error	Fix
Data doesn't match visible content	Schema must describe only what users see
Missing required properties	Check Google/Schema.org docs for each type
Wrong type for page	Don't use Event on non-event pages; don't use JobPosting on product pages
Format/syntax errors	Validate JSON-LD; check quotes, brackets, commas
Over-markup	Mark only relevant content; avoid stuffing unrelated types

Implementation

Next.js (metadata)

tsx

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)

html

<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

Tool	Purpose
Google Rich Results Test	Check if Google can generate rich results
Schema Markup Validator	Validate against Schema.org spec
Search Console	Enhanced reports; monitor validity over time

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

article-page-generator: Article structure; Article/BlogPosting/NewsArticle schema; date display
serp-features: Strongly related—schema maps to SERP features; see mapping table above
faq-page-generator: FAQPage schema; FAQ content structure
breadcrumb-generator: BreadcrumbList schema implementation
featured-snippet: FAQPage, HowTo for snippets
video-optimization: VideoObject, video sitemap, thumbnail, key moments
entity-seo: Organization, Person for entity recognition; @id; Knowledge Panel
homepage-generator: Organization + WebSite schema on homepage or root layout
indexing: Google Indexing API for JobPosting, BroadcastEvent

Files

SKILL.md and 1 other file in skills/kostja94-schema-markup of LeoYeAI/openclaw-master-skills.

SKILL.md
_meta.json

Open the folder on GitHubat commit e5199b5

Compare with similar skills

Schema Markup next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Schema Markup compared with similar skills
Skill	Stars	Used in	Tokens	Auto-check	Licence	Repo updated
Schema Markup this skillLeoYeAI/openclaw-master-skills	2.2k	—	~4.9k	Automated safety check: Pass	MIT	2 mo ago
SEO GeoReScienceLab/opc-skills	1.8k	4 repos	~2.1k	Automated safety check: Pass	Apache-2.0	yesterday
GEO-First SEO Audit Toolzubair-trabzada/geo-seo-claude	11k	—	~2.8k	Automated safety check: Notes	MIT	today
SEO Optimizerailabs-393/ai-labs-claude-skills	454	1 repos	~3.2k	Automated safety check: Pass	MIT	11 mo ago
Portaljs Add Dcatdatopian/portaljs	2.4k	1 repos	~1.8k	Automated safety check: Pass	MIT	yesterday
Schema Markupfreekmurze/dotfiles	1k	15 repos	~1.2k	Automated safety check: Pass	None	yesterday

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Questions about Schema Markup

What does Schema Markup do?

When the user wants to add or optimize structured data (Schema.org, JSON-LD). Schema Markup is an agent skill from LeoYeAI/openclaw-master-skills.org, JSON-LD).

When should I use Schema Markup?

Schema Markup fits situations like: optimize structured data (Schema.org; the user mentions schema; structured data; google rich snippets.

How do I install Schema Markup in Claude Code?

Run `npx skills add LeoYeAI/openclaw-master-skills --skill schema-markup -a claude-code`. Or copy the skill folder (skills/kostja94-schema-markup in LeoYeAI/openclaw-master-skills) into .claude/skills/schema-markup in your project. Claude Code loads it when a task matches its description.

How do I install Schema Markup in Codex?

Run `npx skills add LeoYeAI/openclaw-master-skills --skill schema-markup -a codex`. Or copy the skill folder (skills/kostja94-schema-markup in LeoYeAI/openclaw-master-skills) into .agents/skills/schema-markup in your project. Codex loads it when a task matches its description.

Can I use Schema Markup in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add LeoYeAI/openclaw-master-skills --skill schema-markup -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/schema-markup, .gemini/skills/schema-markup, .github/skills/schema-markup and .opencode/skills/schema-markup in your project.

What does Schema Markup need to run?

SKILL.md names no scripts, command-line tools or credentials: Schema Markup is instructions for the agent only.

Does Schema Markup access the network?

SKILL.md names 7 domains. In commands or code: schema.org; the agent is likely to contact it when it follows the instructions. As links in the text: developers.google.com, search.google.com, bing.com, validator.schema.org, aiso-hub.com and digitalapplied.com. This is read from the text; nothing was executed.

Is Schema Markup safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Schema Markup use?

Schema Markup is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Schema Markup use?

About 4.9k tokens (SKILL.md is roughly 20k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Schema Markup?

Skills that share tags, products or a category with Schema Markup: SEO Geo (ReScienceLab/opc-skills, 1.8k stars), GEO-First SEO Audit Tool (zubair-trabzada/geo-seo-claude, 11k stars), SEO Optimizer (ailabs-393/ai-labs-claude-skills, 454 stars) and Portaljs Add Dcat (datopian/portaljs, 2.4k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Schema Markup?

LeoYeAI (a GitHub user) maintains it in LeoYeAI/openclaw-master-skills, which has 2,160 GitHub stars. The repository holds 1,235 skills in this directory. The repository was last updated on July 20, 2026.

Source: LeoYeAI/openclaw-master-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Install Schema Markup

Facts

At a glance

What it does

When your agent uses it

Example prompts

Workflow steps

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

SEO On-Page: Schema / Structured Data

Scope (On-Page SEO)

Schema.org vs. Search Engine Support

Rich Results: Google Support (2025)

Schema ↔ SERP Features ↔ Rich Results (Strongly Related)

Rich Results vs Featured Snippets

Generative Engine Optimization (GEO)

Initial Assessment

Schema Type Classification

Core Types (General Use)

Exclusive Types (Specific Scenarios)

Organization & WebSite Schema Placement

Action: Website/Product Type → Schema Mapping

Article / BlogPosting / NewsArticle: Type Selection & Implementation

BreadcrumbList

Best Practices

Multilingual Schema (inLanguage)

Implementation Workflow

Common Errors and Fixes

Implementation

Next.js (metadata)

HTML (generic)

Validation Tools

Output Format

Related Skills

Files

Compare with similar skills

Similar skills

More from LeoYeAI/openclaw-master-skills

Related

Questions about Schema Markup