mcpField
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Skill Mcp Server

Skill Mcp Server

Local
github.com/ephemeraldewAI & Agents21Updated 38w ago

About Skill Mcp Server

Skill MCP Server is a standard MCP server that makes Claude Skills available to any MCP-compatible AI agent. Users place skill folders in a directory and connect the server without writing their own parsing logic, which suits developers who want to reuse the Skill ecosystem.

Key features

  1. 01Reads SKILL.md plus scripts and references folders
  2. 02Skill directory set with --skills-dir
  3. 03Output location set with --workspace
  4. 04Hot reload for newly added skills
  5. 05Path validation and sandboxed file operations

Use cases

  1. 01Giving agents without native Skills support access to skill packages
  2. 02Reading, writing and editing files in a workspace
  3. 03Running skill-bundled scripts in a sandbox

MCP support

This project is an MCP server that exposes tools such as list_skills, skill, skill_resource, skill_script and file read, write and edit to MCP clients.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

mcp.json · Universal config
{
"mcpServers": {
"skill_mcp": {
"command": "uvx",
"args": [
"skill-mcp-server"
]
}
}
}
Run commanduvx skill-mcp-server

Local servers need a runtime on your machine (e.g. Node.js 18+). Config file locations differ by client; see your client’s docs.

Packages
pypiskill-mcp-server

Tools

7

Saved tool list · updated 2w ago

ParameterTypeRequiredDescription
namestringYesThe name of the skill to load

Details

From the project README

Skill MCP Server 🚀

Turn any AI agent into a specialist — just drop in a skill folder.

📖 What is it? • 🌟 Why Choose It? • ✨ Features • 🚀 Quick Start • 📝 Creating Skills • 📚 Documentation

📖 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.

Skill MCP Server

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.


🎬 Demo

Demo Video

💡 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:

DimensionNatively Supported Agents (e.g., Claude Code)Other Agents (with this project)
Access BarrierDeep integration, usually non-portableLow barrier, standard MCP protocol
Development BurdenOfficial implementation completeZero code, no need to build Skill parser
FlexibilityTied to specific clientsCross-platform, works with any MCP-compatible agent
Feature ParityFull script, resource & file stream supportPerfect 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

🛠️ 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


Built with the Model Context Protocol

Comments

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New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.