Agent skill · marketing growth · borghei
schema-markup
Structured data implementation, validation, and optimization. Covers JSON-LD patterns for 20+ schema types, rich snippet eligibility, AI search visibility, Knowledge Graph optimization, and CMS-specific deployment guides.
Why this skill is useful
Provides detailed implementation patterns and validation processes for structured data that the AI wouldn't reliably generate on its own.
What it needs
About 12k tokens when loaded. Last updated 2026-07-21. 462 stars on the source repository.
What this skill does
Schema Markup Implementation Production-grade structured data implementation covering 20+ schema types, rich result eligibility rules, AI search optimization, and CMS-specific deployment patterns. Handles auditing existing markup, implementing new schema, and fixing validation errors. --- Table of Contents Operating Modes Schema Type Selection Matrix Implementation Patterns Rich Result Eligibility Rules AI Search Optimization Knowledge Graph Strategy CMS Deployment Guide Validation and Testing Common Errors and Fixes Audit Framework Output Artifacts Related Skills --- Clarify First Before generating the schema, confirm these inputs. If any is unknown or vague, ASK — do not assume: [ ] Page type & operating mode — audit existing, implement new, or fix errors; and on what page type (Article, Product, FAQPage, LocalBusiness…) — selects the schema type and workflow [ ] Target rich result or AI-search goal — which rich result or citation you want to win — sets the required-field checklist and eligibility rules [ ] CMS / deployment platform — WordPress, Webflow, Shopify, Next.js, or static — drives the deployment method and known warnings (e.g. GTM injection) [ ] Source page content — the real headline/author/price/Q&A to populate — schema must match visible content or Google penalizes it Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact. Operating Modes Mode 1: Audit Existing Markup 1. Extract all JSON-LD blocks from the page source 2. Validate required vs recommended fields per schema type 3. Cross-reference with Google's current rich result requirements 4. Score completeness 0-100 per schema block 5. Deliver prioritized fix list with corrected JSON-LD Mode 2: Implement New Schema 1. Identify page type and matching schema types 2. Select primary + supporting schema combination 3. Generate complete, copy-paste-ready JSON-LD populated with page content 4. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills borghei/schema-markup