Skill MCP Server 🚀

<p align="center"> <strong>Turn any AI agent into a specialist — just drop in a skill folder.</strong> </p>

<p align="center"> <a href="#what-is-skill-mcp-server">📖 What is it?</a> • <a href="#why-choose-skill-mcp-server">🌟 Why Choose It?</a> • <a href="#features">✨ Features</a> • <a href="#quick-start">🚀 Quick Start</a> • <a href="#creating-skills">📝 Creating Skills</a> • <a href="#documentation">📚 Documentation</a> </p>

📖 What is Skill MCP Server?

Skill MCP Server is a standard Model Context Protocol (MCP) server that bridges Claude Skills to any AI agent that supports MCP.

<p align="center"> <img src="docs/skll_mcp.png" alt="Skill MCP Server" style="max-width: 100%; height: auto;"/> </p>

Previously, Claude Skills were mainly used in Anthropic's official tools. If your AI application doesn't support Skills, you'd have to implement your own parsing and execution logic, which is a hassle. With this project, you can simply configure it and let any MCP-compatible Agent use standard Skill files directly.

💡 Core Concepts

• 🔌 MCP (Model Context Protocol): Think of it as a "USB interface" for AI. As long as your AI assistant supports this interface, it can connect to various tools and services.
• 📦 Claude Skills: Think of them as "skill packages" for AI. They're not just documentation — they include instructions (SKILL.md), accompanying scripts (Python/JS), and reference materials.

Skill MCP Server is a "converter" that helps various agents use the Skill ecosystem, enabling plug-and-play functionality.

🌟 Why Choose Skill MCP Server?

If your Agent doesn't support Skills yet, this project can help you quickly integrate:

Dimension	Natively Supported Agents (e.g., Claude Code)	Other Agents (with this project)
Access Barrier	Deep integration, usually non-portable	Low barrier, standard MCP protocol
Development Burden	Official implementation complete	Zero code, no need to build Skill parser
Flexibility	Tied to specific clients	Cross-platform, works with any MCP-compatible agent
Feature Parity	Full script, resource & file stream support	Perfect alignment, same dynamic execution & resource access

✨ Features

• 🛠️ Highly Standardized: Strictly follows MCP protocol
• 🌍 Universal Compatibility: Not tied to any vendor, works with all MCP-compatible AI clients
• ⚡ Zero-Code Integration: Helps agents without native Skill support quickly access the Skill ecosystem
• 📦 Fully Compatible: Supports SKILL.md format and scripts/, references/ resource directories
• 📂 Workspace Isolation: Supports --workspace parameter to specify where Skill output files are stored
• 🔄 Hot Reload: Add new skills without restarting the server
• 🔒 Secure by Design: Path validation, sandboxed file operations

🚀 Quick Start

Recommended: Use uvx to run without manual installation.

📥 Installation

# Using pip
pip install skill-mcp-server

# Using uv (recommended)
uv pip install skill-mcp-server

⚙️ Configure MCP

Add Skill MCP Server to your MCP client configuration. All MCP-compatible clients use the same configuration format:

Using uvx (recommended, no installation needed):

{
  "mcpServers": {
    "skill-server": {
      "command": "uvx",
      "args": [
        "skill-mcp-server",
        "--skills-dir", "/path/to/your/skills",
        "--workspace", "/path/to/workspace"
      ]
    }
  }
}

Using local installation:

{
  "mcpServers": {
    "skill-server": {
      "command": "python",
      "args": [
        "-m", "skill_mcp_server",
        "--skills-dir", "/path/to/your/skills",
        "--workspace", "/path/to/workspace"
      ]
    }
  }
}

Configuration file locations:

• Claude Desktop: claude_desktop_config.json (location varies by OS)
• Claude Code: ~/.claude.json
• Other MCP clients: Refer to your client's documentation

Parameter Explanation:

• --skills-dir: Core parameter. Set to the root directory containing all Skill folders you want your agent to use.
• --workspace: Important parameter. Specifies where Skill execution output files (code, reports, etc.) are saved.

🛠️ Available Tools (MCP Tools)

Once connected, your AI agent can use the following tools:

1. 🔍 list_skills: List all available skills
2. 📚 skill: Load a specific skill to get detailed instructions from its SKILL.md
3. 📄 skill_resource: Read reference documents or templates from skill packages
4. ▶️ skill_script: Execute scripts bundled with skills in a secure environment
5. 📖 file_read: Read files from the specified workspace
6. ✍️ file_write: Write files to the specified workspace
7. ✏️ file_edit: Edit existing files in the workspace

📝 Creating Skills

A standard Skill structure looks like this:

my-skills/
└── deploy-helper/           # Skill folder
    ├── SKILL.md             # Core documentation (required)
    ├── scripts/             # Executable scripts
    └── references/          # Reference materials

SKILL.md Example:

---
name: deploy-helper
description: Help users deploy applications to production with one click
---

# Deploy Helper Usage Guide

When users request deployment, follow these steps:
1. Use `skill_resource` to read the deployment template.
2. Modify local configuration files.
3. Call `skill_script` to execute the deployment script.

SKILL.md Format

---
name: my-skill
description: Brief description of what this skill does and when to use it
---

# My Skill

## Overview

Explain what this skill enables the AI to do.

## Usage

Step-by-step instructions for the AI agent...

## Available Resources

- `scripts/process_data.py` - Process input data
- `assets/report_template.md` - Output template

💼 Use Cases

• 📊 Data Analysis: Enable agents to perform data analysis
• 📝 Document Generation: Enable agents to create professional documents
• 🔗 API Integration: Enable agents to integrate with specific APIs
• 🔍 Code Review: Enable agents to follow team standards
• 🚀 DevOps Tasks: Enable agents to automate deployment workflows

📚 Documentation

• 📖 Getting Started Guide
• ✨ Creating Skills
• 📋 Skill Format Reference
• 📤 Publishing Guide

🛠️ Development

# Clone the repository
git clone https://github.com/ephemeraldew/skill_mcp.git
cd skill_mcp

# Install development dependencies
uv pip install -e ".[dev]"

# Run tests
pytest

# Run linting
ruff check src/

🤝 Contributing

Contributions are welcome! Please see CONTRIBUTING.md for guidelines.

If this project helps you, please give it a ⭐️ Star.

📄 License

MIT License - see LICENSE for details.

🔗 Related Resources

• MCP Official Documentation
• Claude Skills Official Guide
• Agent Skills Open Standard

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