skill.md

Generate standardized metadata YAML from directory structure and SKILL.md content.

Updated Mar 20, 2026

Search Tags:#automation #skill creation #standardization #metadata #AI skills #directory analysis

Ready to run

npx skills add https://github.com/mrgunes/ermanprestige-website --skill skill-md-mrgunes

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: skill.md
Source: https://github.com/mrgunes/ermanprestige-website/tree/main
Command: npx skills add https://github.com/mrgunes/ermanprestige-website --skill skill-md-mrgunes

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill simplifies the process of authoring comprehensive metadata for AI skills, ensuring consistent and discoverable descriptions that improve integration and usability.

Core Features & Use Cases

Metadata Generation: Automates the creation of structured skill metadata from directory content and markdown definitions.
Standardization: Ensures metadata complies with official definitions, enhancing discoverability in repositories.
Use Case: Developers want to systematically generate metadata for skills by analyzing directory contents, frontmatter, and optional resources, maintaining uniform quality.

Quick Start

Provide the directory structure and files, along with the SKILL.md content, to generate a standardized metadata YAML block automatically.

Frequently Asked Questions about skill.md

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I generate AI skill metadata from directory content automatically?▼

AI skill metadata generation analyzes your directory structure, frontmatter details, and markdown definitions to automatically produce a standardized metadata YAML block. This ensures consistent and discoverable descriptions for repository indexing and validation.

Why does my AI skill metadata fail repository validation checks?▼

AI skill metadata validation fails when content does not conform to official definitions for dependencies, components, and safety assessments. Standardizing your metadata structure ensures compliance and enhances discoverability across repositories.

What is the best way to standardize skill metadata across multiple directories?▼

Standardizing skill metadata across multiple directories requires analyzing directory structure and markdown definitions to ensure compliance with official definitions. This process maintains uniform quality and detailed indexing for diverse tasks.

Can I use directory analysis to classify and index AI skills?▼

Directory analysis enables AI skill classification and indexing by examining directory content, structure, and frontmatter details. This ensures metadata conforms to official definitions and includes relevant dependencies and safety assessments.

Do I need to provide frontmatter details to automate skill metadata creation?▼

Providing frontmatter details is required to automate skill metadata creation. The process analyzes your frontmatter and directory files alongside markdown definitions to generate a structured and standardized metadata YAML block.

What are the limitations of automating AI skill metadata generation?▼

Automating AI skill metadata generation requires complete directory structures and markdown definitions to function accurately. Without proper frontmatter details and content, the standardization process may not produce compliant or discoverable metadata.