Skills MCP Server

A Model Context Protocol (MCP) server that enables AI agents to discover, load, and execute Agent Skills - organized folders of instructions, scripts, and resources that give agents additional capabilities.

Based on the Agent Skills specification.

What are Skills?

Skills are folders containing:

SKILL.md - Instructions and metadata (name, description)
scripts/ - Executable Python scripts
references/ - Additional documentation (loaded on demand)
assets/ - Static resources (templates, data files)

Skills use progressive disclosure to efficiently manage context:

Level 1: Name + description always visible in the skill tool description
Level 2: Full SKILL.md loaded when skill(name) is called
Level 3: Scripts/references loaded when execute_skill_script() or get_skill_resource() is called

Features

Dynamic Skill Discovery: All skill names and descriptions are embedded in the skill tool description
Progressive Loading: Load skill instructions on demand
Script Execution: Run pre-built Python scripts from skills
Resource Access: Load reference docs and assets as needed
Agent Skills Compatible: Follows the open Agent Skills specification

Getting Started

Prerequisites

Python 3.10+
An MCP-compatible client (e.g., Manus, Claude Code, Cursor)

Installation

Clone the repository:

git clone https://github.com/Livus-AI/Skills-MCP.git
cd Skills-MCP

Install dependencies:
```
pip install -e .
```
Run the server:
```
skills-mcp
```

Configuration

Skills Directory: By default, skills are stored in the skills/ directory. You can change this by setting the SKILLS_DIR environment variable.

MCP Tools

The server exposes 3 tools:

Tool	Description
`skill(name)`	Load a skill's full instructions. The tool description dynamically includes ALL skill names and descriptions.
`execute_skill_script(skill_name, script_name, params)`	Execute a Python script from a skill's `scripts/` directory.
`get_skill_resource(skill_name, resource_path)`	Load a specific resource file (reference docs, assets).

How It Works

The skill tool description is dynamically generated to always include the name and description of every available skill. This means:

Agents see all skills immediately - No need to call a "list" function
One call to load - skill("name") loads full instructions
Execute when ready - execute_skill_script() runs scripts

Example Workflow

# Agent reads skill tool description and sees:
# - hello-world: A simple example skill...
# - slack-message: Post messages to Slack...

# Step 1: Load the skill
skill("slack-message")
# Returns: full instructions, available scripts, resources

# Step 2: Execute a script
execute_skill_script("slack-message", "post.py", {"channel": "#general", "message": "Hello!"})
# Returns: script output

Creating a Skill

See SKILL_CREATION.md for the complete guide.

Quick Start

Create the directory structure:

skills/
└── my-skill/
    ├── SKILL.md              # Required: Instructions + metadata
    ├── scripts/              # Optional: Executable scripts
    │   └── main.py
    ├── references/           # Optional: Additional docs
    │   └── api.md
    └── assets/               # Optional: Static resources
        └── template.json

Create SKILL.md with frontmatter:

---
name: my-skill
description: What this skill does and when to use it. Include keywords that help agents identify relevant tasks.
license: MIT
metadata:
  author: your-name
  version: "1.0"
---

# My Skill

## Overview
Brief description of what this skill helps accomplish.

## Available Scripts
- `scripts/main.py` - Primary functionality

## How to Use
Step-by-step instructions...

Create scripts with the standard format:

import sys
import json

def run(params: dict = None) -> dict:
    params = params or {}
    # Your logic here
    return {"status": "success", "result": "..."}

if __name__ == "__main__":
    params = {}
    if len(sys.argv) > 1:
        params = json.loads(sys.argv[1])
    result = run(params)
    print(json.dumps(result))

Example Skills

This repository includes example skills in the skills/ directory:

hello-world - A simple example demonstrating the skill format
slack-message - Post messages to Slack via webhook

Roadmap

create_skill tool - Create new skills programmatically
execute_code tool - Execute arbitrary Python code with e2b sandboxing
Skill validation and linting
Skill versioning and updates

Contributing

Contributions are welcome! Please feel free to submit a pull request or open an issue.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Workflows-MCP