A specialized toolkit for implementing, auditing, and validating schema.org structured data to secure rich results and AI search visibility.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install cs-schema-markup
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install cs-schema-markup using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Schema Markup Implementation?
This skill empowers developers and marketers to master structured data using JSON-LD, the industry standard recommended by Google. By integrating this capability into your workflow via Openclaw Skills, you can ensure that your website content is perfectly legible to both traditional search engines and modern AI search systems like Perplexity and ChatGPT. It focuses on earning high-impact rich snippets—such as FAQ dropdowns, product ratings, and breadcrumbs—while maintaining technical compliance with Google Search Console standards.
Beyond traditional SEO, this skill treats structured data as a bridge to the next generation of discovery. By utilizing the advanced validation and implementation patterns provided by Openclaw Skills, developers can fix persistent Search Console errors, automate the generation of complex schema blocks, and ensure that entity recognition is maximized across the web.
Schema Markup Implementation Use Cases
- Fixing structured data errors reported in Google Search Console to restore rich result eligibility.
- Implementing Product, FAQ, or HowTo schema to increase click-through rates from search engine results pages.
- Auditing existing site markup for missing required fields and schema.org compliance using automated scripts.
- Optimizing content for AI search visibility to increase the likelihood of being cited in AI Overviews and Perplexity.
- Generating copy-paste-ready JSON-LD blocks for diverse page types including LocalBusiness, Article, and Event.
How Schema Markup Implementation Works
- Context Discovery: The skill first checks for a marketing-context.md file to understand the CMS platform, site goals, and existing technical constraints.
- Schema Audit: It utilizes a schema_validator.py script to scan page HTML, identifying present, broken, or missing markup blocks.
- Type Selection: Based on page content, it identifies the primary and supporting schema types (e.g., Article + BreadcrumbList + Person).
- Pattern Implementation: It pulls validated JSON-LD patterns and populates them with real content, ensuring absolute image URLs and ISO date formats.
- Validation & Refinement: The output is cross-referenced against the Rich Results Test and Schema.org Validator to ensure 100% compliance before deployment.
Schema Markup Implementation Setup
To get started with this skill within your Openclaw Skills environment, ensure you have the validation scripts available in your local directory. You can validate any local HTML file using the following command:
python3 scripts/schema_validator.py your-page.html
Additionally, ensure your project includes a references/ directory containing implementation-patterns.md and schema-types-guide.md to provide the agent with the necessary templates and field requirements.
Schema Markup Implementation Data Schema & Taxonomy
The skill organizes its technical references and audit data using a structured taxonomy to ensure consistency across the Openclaw Skills ecosystem:
| Data Component | Format | Description |
|---|---|---|
| Implementation Patterns | Markdown | A library of JSON-LD templates for various schema.org types. |
| Schema Types Guide | Markdown | Detailed lists of required vs. recommended fields for Google eligibility. |
| Audit Reports | Markdown | Summary of schemas found, errors detected, and completeness scores (0-100). |
| Local Validator | Python Script | A tool to extract and score JSON-LD blocks from raw HTML files. |
| Marketing Context | Markdown | Centralized file for site-wide settings like CMS type and Organization details. |
Schema Markup Implementation Advanced Features
- AI Search Entity Recognition: Leverages sameAs links and Organization schema to connect digital entities across social profiles and Wikidata.
- Proactive Error Triggers: Automatically flags issues like missing image fields in Articles or dateModified timestamps that pre-date datePublished.
- Complex Schema Stacking: Provides rules for safely nesting or flattening multiple schema types on a single page without penalization.
- CMS-Specific Optimization: Tailors implementation advice for WordPress, Webflow, Shopify, and custom server-side environments.
- Rich Result Eligibility Scoring: Uses local scripts to simulate Google's validation and provide a readiness score before you go live.
SKILL.md
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