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ai-md

Convert CLAUDE.md instructions into structured YAML frontmatter rules.

3|Updated Apr 3, 2026
Search Tags:#claude.md#ai-native#prompt-engineering#instruction-conversion#ai-md#multimodel-validation#toxicity-checks
One-click install
npx skills add https://github.com/ravnhq/typescript-blueprint --skill ai-md-ravnhq
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ai-md
Source: https://github.com/ravnhq/typescript-blueprint/tree/main/.cursor/skills/ai-md
Command: npx skills add https://github.com/ravnhq/typescript-blueprint --skill ai-md-ravnhq

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

AI.MD provides a framework to convert human-written CLAUDE.md instructions into AI-native, structured labels that any model can execute reliably, reducing ambiguity and token waste.

Core Features & Use Cases

  • Atomic rule decomposition: transforms dense natural-language rules into individual labeled rules that are easy to verify across models.
  • Cross-model validation: supports testing and alignment across Claude, GPT, and other LLMs to ensure consistent behavior.
  • Safe, auditable workflows: includes frontmatter requirements, toxicity checks, and a staged conversion process from understanding to testing.

Quick Start

Distill your CLAUDE.md into AI-native structured rules using the AI.MD workflow and validate results with two different models.

Frequently Asked Questions about ai-md

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

FAQPage Schema
How do I convert CLAUDE.md instructions into a structured AI-native format?▼

Converting CLAUDE.md into an AI-native format requires decomposing dense natural-language rules into atomic labeled rules, applying YAML frontmatter, and structuring instructions for token efficiency and reliable AI execution.

What is atomic rule extraction for prompt engineering?▼

Atomic rule extraction is a prompt engineering technique that transforms dense natural-language instructions into individual, labeled rules, making them easy to verify and validate consistently across different LLMs.

Does multi-model validation work with GPT and Claude system instructions?▼

Multi-model validation works with GPT and Claude by testing the converted structured instructions across models to ensure consistent behavior, alignment, and reliable cross-model execution.

When do I need YAML frontmatter and toxicity checks for system instructions?▼

You need YAML frontmatter and toxicity checks when migrating system instructions to ensure safe, auditable AI workflows, requiring explicit naming, descriptions, and safety-conscious component structuring.

What is the best way to reduce token waste in AI system instructions?▼

The best way to reduce token waste is converting human-written CLAUDE.md into AI-native structured labels, which eliminates ambiguity and ensures any model can execute the instructions reliably without excess tokens.

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