markdown-optimizer

Generate YAML frontmatter and normalize Markdown document structure.

Updated Feb 11, 2026

Search Tags:#documentation #markdown #LLM #metadata #token optimization #frontmatter

Ready to run

npx skills add https://github.com/mvdmakesthings/skills --skill markdown-optimizer

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: markdown-optimizer
Source: https://github.com/mvdmakesthings/skills/tree/main/template-skill
Command: npx skills add https://github.com/mvdmakesthings/skills --skill markdown-optimizer

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This skill streamlines Markdown documentation by automatically generating YAML frontmatter and normalizing structure for consistent parsing by LLMs and downstream tools.

Core Features & Use Cases

Frontmatter generation: Automatically adds and validates name, description, and version.
Heading normalization: Ensures consistent heading levels and hierarchy across large docs.
Metadata enrichment: Embeds structured metadata to improve searchability and processing.
Use Case: Convert a messy API docs page into a clean, machine-friendly Markdown document with clear sections and metadata.

Quick Start

To run the Markdown optimizer, apply it to a sample Markdown file to generate frontmatter and normalized structure.

Frequently Asked Questions about markdown-optimizer

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

FAQPage Schema

How do I add YAML frontmatter to Markdown documents for LLM ingestion?▼

To add YAML frontmatter to Markdown documents, this skill automatically generates and validates metadata fields like name, description, and version. It parses your technical documentation to embed structured metadata, ensuring a consistent, machine-friendly format for LLM ingestion.

Why does my Markdown structure break when parsed by LLMs?▼

Markdown structure breaks during LLM parsing due to inconsistent heading levels and missing metadata. This skill normalizes heading hierarchy and generates YAML frontmatter across large docs, transforming messy content into a clean, machine-friendly format that prevents parsing errors.

What is the best way to normalize heading hierarchy in technical documentation?▼

The best way to normalize heading hierarchy is to apply an automated Markdown optimizer. It corrects inconsistent heading levels across large technical documents and API references, ensuring a logical structure that downstream tools and LLMs can consistently parse.

Can I use this Markdown optimizer for API references and knowledge bases?▼

Yes, you can use this Markdown optimizer for API references and knowledge bases. It is specifically designed to tidy technical documentation and process guides by normalizing structure and enriching metadata, making them fully machine-readable.

Do I need external dependencies to generate YAML frontmatter for Markdown files?▼

You do not need external dependencies to generate YAML frontmatter. The skill relies on built-in scripts and references to parse Markdown and apply YAML frontmatter tooling, transforming your content into a clean, structured format without additional setup.

What are the limitations of using automated Markdown normalization for token optimization?▼

Automated Markdown normalization for token optimization is limited to structural changes and metadata generation. It standardizes heading hierarchy and adds YAML frontmatter but does not rewrite or compress the actual content text, meaning token reduction is strictly structural.