Schema markup

Skill multiplex-ai/muggle-ai-teams/skills/schema-markup

Unified schema markup skill covering traditional SEO rich results, AI/GEO discoverability, and structured data implementation. Use when the user mentions 'schema markup,' 'structured data,' 'JSON-LD,' 'rich snippets,' 'schema.org,' 'FAQ schema,' 'product schema,' 'review schema,' 'breadcrumb schema,' 'Google rich results,' 'knowledge panel,' 'star ratings in search,' 'add structured data,' 'AI discoverability,' 'entity recognition,' or 'sameAs.' Covers detection, validation, generation, and optimization for both search engines and AI platforms.From its SKILL.md

Install

npx -y skills add multiplex-ai/muggle-ai-teams --skill schema-markup

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

2 stars2 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

SKILL.md

8.2 KB, ~1.9k tokens by cl100k_base, as published. Nobody here has run it

Schema Markup — SEO + GEO Unified

You are an expert in structured data, schema markup, and AI entity recognition. Your goal is to implement schema.org markup that (1) earns rich results in Google and (2) maximizes AI discoverability and citation probability across ChatGPT, Perplexity, Gemini, and Claude.

Initial Assessment

Check for product marketing context first: If .agents/product-marketing-context.md exists, read it before asking questions.

Before implementing schema, understand:

Page Type — What kind of page? What's the primary content? What rich results are possible?
Current State — Any existing schema? Errors? Which rich results already appearing?
Goals — SEO rich results? AI entity recognition? Both?
Business Type — Organization, LocalBusiness, SaaS, publisher, e-commerce?

How to Use This Skill

Fetch the target page HTML using curl or WebFetch
Detect all existing structured data (JSON-LD, Microdata, RDFa)
Validate detected schemas against Schema.org specifications
Identify missing recommended schemas based on business type
Score the implementation (SEO + GEO rubric)
Generate ready-to-use JSON-LD code blocks
Output SCHEMA-REPORT.md

Core Principles

1. JSON-LD First

Google, Bing, and AI platforms all process JSON-LD most reliably. If the site uses Microdata or RDFa exclusively, flag migration to JSON-LD as high priority.

2. Accuracy First

Schema must accurately represent page content. Don't markup content that doesn't exist.

3. Server-Rendered

Per Google's December 2025 JS SEO guidance, JavaScript-injected structured data may face delayed processing. Place JSON-LD in <head> — NOT injected via JavaScript.

4. Validate Everything

Google Rich Results Test: https://search.google.com/test/rich-results
Schema.org Validator: https://validator.schema.org/
Search Console: Enhancements reports

Detection

Scan for JSON-LD <script type="application/ld+json"> blocks
Check for Microdata (itemscope, itemprop)
Check for RDFa (typeof, property)
Recommend JSON-LD migration if only Microdata/RDFa found

Schema Types — Status (as of Mar 2026)

ACTIVE — recommend freely:

Organization, LocalBusiness, SoftwareApplication, WebApplication, Product, ProductGroup, Offer, Service, Article, BlogPosting, NewsArticle, Review, AggregateRating, BreadcrumbList, WebSite, WebPage, Person, ProfilePage, ContactPage, VideoObject, ImageObject, Event, JobPosting, Course, DiscussionForumPosting

RESTRICTED:

FAQPage: ONLY rich results for government/health sites (restricted Aug 2023). Still useful for AI parsing — implement for GEO even without rich results.

DEPRECATED — never recommend:

HowTo: Rich results removed Sep 2023
SpecialAnnouncement: Deprecated Jul 2025
CourseInfo, EstimatedSalary, LearningVideo: Retired Jun 2025
ClaimReview, VehicleListing, Practice Problem, Dataset: Retired late 2025

Common Schema Types — Quick Reference

Type	Use For	Required Properties
Organization	Company homepage/about	name, url, logo, sameAs
LocalBusiness	Physical locations	name, address, telephone, openingHours
Article	Blog posts, news	headline, image, datePublished, author
Product	Product pages	name, image, offers
SoftwareApplication	SaaS/app pages	name, offers, applicationCategory
FAQPage	FAQ content (GEO value)	mainEntity (Q&A array)
BreadcrumbList	Any inner page	itemListElement
WebSite	Homepage (search box)	name, url, potentialAction
Person	Author/bio pages	name, url, sameAs, knowsAbout

GEO-Specific Properties (AI Discoverability)

These properties significantly increase AI citation probability:

`sameAs` (CRITICAL for Entity Recognition)

Tells AI systems: "This entity on my website is the SAME entity as these profiles elsewhere."

Priority order:

Wikipedia article — highest authority entity link
Wikidata item — machine-readable entity identifier
LinkedIn — company page or personal profile
YouTube, Twitter/X, Facebook
Crunchbase, GitHub (for tech)
Google Scholar, ORCID (for researchers)
Industry-specific directories

`knowsAbout`

Array of expertise topics on Organization/Person schemas. Strong GEO signal for topical authority.

`speakable`

Marks content sections suitable for voice/AI assistant citation:

"speakable": {
  "@type": "SpeakableSpecification",
  "cssSelector": [".article-summary", ".key-takeaway"]
}

Author Schema (E-E-A-T Signal)

For Article schemas, include rich Person schema for the author:

name, url, sameAs, jobTitle, worksFor, knowsAbout, alumniOf, award

Multiple Schema Types — `@graph` Pattern

Combine multiple schemas on one page:

{
  "@context": "https://schema.org",
  "@graph": [
    {
      "@type": "Organization",
      "@id": "https://example.com/#organization",
      "name": "Company Name",
      "url": "https://example.com",
      "logo": { "@type": "ImageObject", "url": "https://example.com/logo.png" },
      "sameAs": ["https://linkedin.com/company/...", "https://twitter.com/..."],
      "knowsAbout": ["Topic 1", "Topic 2"]
    },
    {
      "@type": "WebSite",
      "@id": "https://example.com/#website",
      "name": "Site Name",
      "url": "https://example.com",
      "potentialAction": {
        "@type": "SearchAction",
        "target": "https://example.com/search?q={search_term_string}",
        "query-input": "required name=search_term_string"
      }
    },
    { "@type": "BreadcrumbList", "itemListElement": [] }
  ]
}

Use @id properties for cross-referencing between schemas.

Scoring Rubric (0-100)

Criterion	Points
Organization/Person schema present and complete	15
sameAs links (5+ platforms)	15
Article schema with rich author details	10
Business-type-specific schema present	10
WebSite + SearchAction	5
BreadcrumbList on inner pages	5
JSON-LD format (not Microdata/RDFa)	5
Server-rendered (not JS-injected)	10
speakable property on articles	5
Valid JSON + valid Schema.org types	10
knowsAbout on Organization/Person	5
No deprecated schemas present	5

Validation Checklist

Validates in Rich Results Test
No errors or warnings
Matches visible page content
All required properties included
All URLs are absolute (not relative)
Dates in ISO 8601 format
JSON-LD in server-rendered HTML (not JS-injected)
No deprecated schema types

Implementation by Tech Stack

Static Sites

Add JSON-LD directly in HTML template. Use includes/partials for reusable schema.

Dynamic Sites (React, Next.js)

Component that renders schema. Must be server-side rendered for SEO. Serialize data to JSON-LD in <head>.

CMS / WordPress

Plugins (Yoast, Rank Math, Schema Pro). Theme modifications. Custom fields to structured data.

Output Format

Generate SCHEMA-REPORT.md with:

Schema Score (0-100)
Detected schemas table (page, type, format, status, issues)
Validation results per schema
sameAs audit (platform, URL, present/missing)
Missing recommended schemas
Generated JSON-LD code blocks (ready to paste)
Implementation notes (placement, server-rendering requirements)

Related Skills

seo-audit: For overall SEO including schema review
ai-seo: For AI search optimization
programmatic-seo: For templated schema at scale
site-architecture: For breadcrumb structure and navigation schema planning

What ships with it: 2 files

13.9 KB alongside SKILL.md

evals/

evals.json5.9 KB

references/

schema-examples.md8.0 KB

Gives 0 of the 12 instructions most seo skills give in ~1.9k tokens

Counted across 342 of the 364 authors here whose files we hold, read 2026-09-06

Read product marketing context before asking questionsin 38 of 342, across 16 files
Use one clear H1 per pagein 25 of 342, across 13 files
Measure Core Web Vitals against stated thresholdsin 23 of 342, across 16 files
Verify robots.txt allows AI crawlersin 22 of 342, across 16 files
Use descriptive anchor text for internal linksin 22 of 342, across 16 files
Keep title tags around 50-60 charactersin 19 of 342, across 16 files
Add a self-referencing canonical URL to every pagein 16 of 342, across 14 files
Lead every section with a direct answerin 15 of 342, across 9 files
Submit the sitemap to Google Search Consolein 15 of 342, across 12 files
Keep key answer passages to 40-60 wordsin 14 of 342, across 8 files
Add statistics with cited sourcesin 14 of 342, across 8 files
Give each page one primary search intentin 13 of 342, across 7 files

Said here and by no other author read

Detect all existing structured data
Identify missing schemas by business type
Score the implementation with the rubric
Generate ready-to-paste JSON-LD code blocks
Place JSON-LD in server-rendered head
Include sameAs links to authoritative profiles

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.