Can I validate my existing markdown files?

Yes, the skill provides scripts like check_doc_quality.py and lint_markdown.sh that analyze your existing files for heading hierarchy, code block formatting, and vague language patterns.

Why is semantic proximity important for AI documentation?

AI tools often split documents into chunks of 500-1000 tokens. Keeping definitions near their applications and prerequisites near procedures ensures that the AI doesn't lose critical context during retrieval.

How do the documentation templates help?

The skill includes 17 templates (like api-endpoint and how-to-guide) that automatically include correct heading hierarchies, required frontmatter fields, and placeholders to ensure best practices are followed from the start.

What makes documentation 'AI-ready'?

AI-ready documentation utilizes specific frontmatter, semantic chunking, and self-contained sections so AI models can retrieve and understand content accurately even when sections are processed as discrete text chunks.

Writing AI-Ready Docs

Name: Writing AI-Ready Docs
Author: Mission42-ai

byMission42-ai

学習とドキュメンテーション

Creates structured, AI-optimized documentation with standardized frontmatter and built-in validation scripts.

概要

The Writing AI-Ready Docs skill enables developers to generate high-quality documentation specifically designed for effective processing by AI coding assistants. By enforcing semantic chunking rules, strict heading hierarchies, and self-contained sections, it ensures that content remains contextually rich even when indexed for Retrieval-Augmented Generation (RAG). The skill provides a library of 17 pre-defined templates—ranging from API references to troubleshooting guides—alongside automated tools for quality checks, markdown linting, and link validation.

主な機能

Internal and external broken link validation
0 GitHub stars
AI-optimized semantic chunking and structural rules
Automated documentation quality and linting scripts
17 specialized templates for APIs, tutorials, and guides
Standardized YAML frontmatter for improved metadata discovery

ユースケース

Standardizing documentation structure across modular development teams
Validating existing markdown files for AI readability and link integrity
Optimizing technical documentation for RAG-based AI search and retrieval

概要

主な機能

Internal and external broken link validation
0 GitHub stars
AI-optimized semantic chunking and structural rules
Automated documentation quality and linting scripts
17 specialized templates for APIs, tutorials, and guides
Standardized YAML frontmatter for improved metadata discovery

ユースケース

Standardizing documentation structure across modular development teams
Validating existing markdown files for AI readability and link integrity
Optimizing technical documentation for RAG-based AI search and retrieval