What is SKILL.md? The Open Standard for AI Agent Skills
You've probably seen .md files that document code. SKILL.md takes that same markdown approach and applies it to AI agent instructions. Instead of documenting what code does, SKILL.md packages what an AI agent should do.
The format emerged from a simple problem: every AI agent platform has its own way of storing instructions. ChatGPT uses custom instructions. Claude has system prompts. Local agents have their own formats. You write instructions for one, they don't work anywhere else.
SKILL.md fixes this by creating a standard format. Write once, run anywhere that supports the spec.
How SKILL.md works
A SKILL.md file contains three main sections wrapped in a simple markdown structure:
# Skill name
Brief description of what this skill does.
## Instructions
The actual prompt or system instructions that tell the AI how to behave.
## Examples
Sample inputs and expected outputs showing the skill in action.
That's it. No complex syntax. No proprietary formats. Just markdown with a specific structure that any agent can parse and execute.
The instructions section holds the core logic. This is where you define the AI's behavior, personality, constraints, and capabilities. Think of it as the system prompt, but portable across platforms.
Examples demonstrate how the skill should work in practice. They serve as both documentation for humans and training data for agents that learn from demonstrations.
Which agents support SKILL.md
The standard is gaining adoption across different agent platforms. Some parse SKILL.md files directly, while others convert them to their native format during import.
Claude Desktop and Claude.ai can import SKILL.md files through the CLAUDE.md spec, which extends the base format with Claude-specific features. The conversion happens automatically when you upload a SKILL.md file.
Many local agent frameworks support the format natively. If your agent can read markdown and parse YAML frontmatter, adding SKILL.md support takes minimal code.
The format intentionally stays simple to encourage broad adoption. Complex features that only work on one platform defeat the purpose of having a standard.
Creating your first skill
Start with a problem you want to solve. Maybe you want an AI that writes better commit messages, or one that explains code in simple terms.
Here's a basic SKILL.md file for code explanation:
# Code explainer
Explains code snippets in plain English for beginners.
## Instructions
You are a programming tutor. When given code, explain what it does in simple terms.
- Break down complex logic into steps
- Define technical terms when first used
- Use analogies when helpful
- Keep explanations concise but complete
## Examples
**Input:** `for i in range(10): print(i)`
**Output:** This code uses a loop to print numbers 0 through 9. The `range(10)` creates a sequence of numbers starting at 0 and ending at 9. The loop runs once for each number, storing it in the variable `i`, then prints that number.
Save this as code-explainer.skill.md and you have a portable AI skill.
Why the open standard matters
Proprietary formats create vendor lock-in. You invest time writing custom instructions for one platform, then can't move them elsewhere without rewriting everything.
SKILL.md prevents this by establishing a neutral ground. The format doesn't belong to any single company or platform. It's designed for interoperability.
This approach follows successful precedents. Markdown itself succeeded because it was simple and platform-agnostic. Docker containers work everywhere because the format is standardized. SKILL.md applies the same principle to AI instructions.
The standard also enables a marketplace of reusable skills. Instead of everyone writing the same instructions from scratch, you can browse skills others have created, test them locally, and adapt them to your needs.
Technical structure
Under the hood, SKILL.md files can include YAML frontmatter for metadata:
---
version: 1.0
tags: [coding, education]
author: username
license: MIT
---
The frontmatter is optional but useful for categorization and attribution. The SKILL.md spec defines the complete format, including optional sections for configuration, dependencies, and platform-specific adaptations.
Skills can reference other skills or external resources. This enables composition where complex behaviors build from simpler components.
Skills vs other approaches
SKILL.md differs from MCP servers in scope and complexity. MCP (Model Context Protocol) handles real-time tool integration and function calling. SKILL.md packages static instructions and behavioral patterns.
Think of MCP as giving an AI new senses and abilities, while SKILL.md shapes how it thinks and responds. They're complementary technologies that solve different problems.
Traditional prompt engineering often happens in isolation. You craft prompts in a text editor or platform-specific interface, then manually copy them between tools. SKILL.md makes prompts portable and shareable from the start.
The format also encourages better practices. Having to write examples forces you to think through edge cases. The structured format makes complex instructions easier to debug and maintain.
What's next
The SKILL.md ecosystem is expanding rapidly. New agent platforms are adding native support. Existing platforms are building import tools.
The specification continues evolving based on real-world usage. Recent additions include better support for multi-step workflows and integration with external APIs.
If you're building AI agents, consider supporting SKILL.md. The implementation is straightforward, and it opens your platform to a growing library of community-created skills.
For users, creating skills and sharing them helps establish the format as the de facto standard. The more platforms that support SKILL.md, the more valuable your skill library becomes.
The goal isn't to replace every existing format immediately. It's to provide a common language that makes AI skills as portable as any other piece of software. Write once, run anywhere.
