What does the validate-md skill actually check?

It validates the YAML frontmatter section located at the top of Markdown files against a predefined schema.yaml file using the JSON Schema standard.

How do I define the rules for my Markdown files?

You define your rules in a file named 'schema.yaml' placed within the directory of the Markdown files you wish to validate.

Do I need to install additional software for this skill?

Yes, you must have Python installed along with the 'pyyaml' and 'jsonschema' packages to execute the validation scripts.

Does it support nested YAML objects?

Yes, since it uses JSON Schema, it can validate deeply nested objects, arrays, and complex data types within your Markdown metadata.

Markdown Schema Validator

Name: Markdown Schema Validator
Author: luebken

byluebken

Content Management

Validates Markdown files and YAML frontmatter against JSON Schema definitions to ensure data integrity.

About

The Markdown Schema Validator (validate-md) is a specialized tool for Claude Code designed to automate the verification of structured metadata within Markdown files. By comparing YAML frontmatter against a local schema.yaml file, it ensures that all required properties, data types, and formatting constraints are met. This is particularly useful for developers managing static site generators, documentation-as-code, or flat-file databases where inconsistent metadata can lead to build failures or data corruption. It provides clear, actionable error messages that pinpoint exact file locations and missing fields.

Key Features

0 GitHub stars
Automated YAML frontmatter validation using JSON Schema standards
Lightweight Python integration using pyyaml and jsonschema
Support for complex metadata structures in Markdown-based workflows
Directory-wide batch processing for rapid content auditing
Detailed error reporting with specific field paths and file locations

Use Cases

Validating mandatory metadata for static site generators like Jekyll, Hugo, or Docusaurus
Enforcing schema compliance for CRM data or leads stored in Markdown format
Maintaining structural consistency across large technical documentation repositories

About

Key Features

0 GitHub stars
Automated YAML frontmatter validation using JSON Schema standards
Lightweight Python integration using pyyaml and jsonschema
Support for complex metadata structures in Markdown-based workflows
Directory-wide batch processing for rapid content auditing
Detailed error reporting with specific field paths and file locations

Use Cases

Validating mandatory metadata for static site generators like Jekyll, Hugo, or Docusaurus
Enforcing schema compliance for CRM data or leads stored in Markdown format
Maintaining structural consistency across large technical documentation repositories