schema-markup
π―SkillA Claude Code skill for implementing schema.org structured data in JSON-LD format, covering common schema types like Organization, Article, Product, and FAQPage with validation workflows and rich results targeting.
Installation
This skill is no longer in its GitHub repo
It may have been renamed or removed, so no install command is shown. Check the repo for the skills it has now.
Open the GitHub repo βWhat this skill does
Overview
Schema Markup is a Claude Code skill for implementing and optimizing structured data on websites using schema.org markup. It focuses on JSON-LD format as recommended by Google, covering common schema types like Organization, Article, Product, SoftwareApplication, FAQPage, HowTo, BreadcrumbList, and more. The skill ensures that schema markup accurately represents page content and meets Google's eligibility requirements for rich results.
The skill provides a systematic approach to structured data implementation, from initial assessment of page type and goals through to validation and testing. It covers combining multiple schema types on a single page using @graph, handles different tech stacks (static sites, React/Next.js, CMS/WordPress), and emphasizes validation through Google Rich Results Test and Schema.org Validator.
Key Features
- JSON-LD Implementation: Generates accurate JSON-LD markup following Google's recommended format with complete required and recommended properties
- Common Schema Types Coverage: Provides quick reference and implementation guidance for Organization, Article, Product, FAQPage, HowTo, BreadcrumbList, LocalBusiness, and Event schemas
- Multi-Schema Pages: Supports combining multiple schema types on one page using the @graph structure
- Platform-Specific Guidance: Covers implementation for static sites, React/Next.js applications, and CMS platforms like WordPress
- Validation Workflow: Includes testing checklist with Google Rich Results Test and Schema.org Validator integration
- Rich Results Targeting: Helps identify which rich results are achievable for each page type and ensures eligibility requirements are met
Who is this for?
This skill is designed for web developers, SEO specialists, and marketers who want to implement structured data to enable rich results in Google Search. It is particularly useful for teams managing SaaS product pages, blogs, or e-commerce sites that want to improve search visibility through proper schema markup.
Summarized by AI from this skill's SKILL.md. Suggest a better one
More from this repository10
Helps you write or rewrite the text on website pages such as homepages, landing pages, and pricing pages. It keeps your own voice, writes only claims you can back up, and avoids phrases that sound like AI.
A skill that finds out why your website does not show up well in Google search and tells you what to fix first. It checks how search engines find your pages, then speed, titles and content.
Acts as a content strategist to plan content that drives traffic and leads, using topic clusters, buyer-stage keyword research and editorial priorities tied to customers and search. Helps decide what to write and why posts are not bringing customers.
Applies psychological principles and mental models to marketing, explaining why buyers behave as they do and how to influence decisions ethically. Covers biases like anchoring, social proof, scarcity, loss aversion and framing, plus strategic thinking models.
Helps brands get cited and recommended in AI-generated answers from Google AI Overviews, ChatGPT, Perplexity, Claude, Gemini and Copilot. Covers AEO and GEO, AI visibility audits, content structure, llms.txt and agent-readable sites.
Edits, tightens and proofreads existing marketing copy without rewriting it from scratch, using a Seven Sweeps framework and an AI-tell check. Requests to fix a single line get a focused point edit to that line only.
Acts as a marketing strategist that draws on a library of 139 marketing ideas to suggest the most relevant growth tactics for a software product based on its stage, budget and goals.
Writes and evaluates ad creative such as headlines, primary text, and variations for Google, Meta, LinkedIn, TikTok, and X, checked against each platform's character limits. It can iterate from performance data, produce grounded static batches, and plan which ads to make next.
Helps create B2B sales collateral that reps actually use, including decks, one-pagers, objection handling, demo scripts, battle cards, proposals and win-loss analysis. It emphasizes scannable, outcome-focused content and claims reps can defend.
Helps run and synthesize customer research, from analyzing transcripts, surveys and reviews to mining Reddit and review sites and running interviews. It extracts jobs to be done, pains, triggers and customer language, and labels each insight with a confidence level.