Skill Management MCP Server

A Model Context Protocol (MCP) server that enables Claude to manage skills stored in ~/.skill-mcp/skills. This system allows Claude to create, edit, run, and manage skills programmatically, including execution of skill scripts with environment variables.

Quick Status

Status: ✅ Production Ready Test Coverage: 86% (145/145 tests passing) Deployed: October 18, 2025 Architecture: 22-module modular Python package with unified CRUD architecture

Overview

TL;DR: Write Python code that unifies multiple skills in one execution - follows Anthropic’s MCP pattern for 98.7% more efficient agents.

This project consists of two main components:

1. MCP Server (src/skill_mcp/server.py) - A Python package providing 5 unified CRUD tools for skill management
2. Skills Directory (~/.skill-mcp/skills/) - Where you store and manage your skills

Key Advantages

🚀 Unified Multi-Skill Execution (Code Execution with MCP)

Build once, compose everywhere - Execute Python code that seamlessly combines multiple skills in a single run:

# One execution, multiple skills unified!
# Imports from calculator, data-processor, and weather skills
from math_utils import calculate_average          # calculator skill
from json_fetcher import fetch_json                # data-processor skill
from weather_api import get_forecast               # weather skill

# Fetch weather data
weather = fetch_json('https://api.weather.com/cities')

# Calculate averages using calculator utilities
temps = [city['temp'] for city in weather['cities']]
avg_temp = calculate_average(temps)

# Get detailed forecast
forecast = get_forecast('London')
print(f"Average temperature: {avg_temp}°F")
print(f"London forecast: {forecast}")

What makes this powerful:

• ✅ Context-efficient - Dependencies and env vars auto-aggregated from all referenced skills
• ✅ Composable - Mix and match utilities from any skill like building blocks
• ✅ No redundancy - Declare PEP 723 dependencies once in library skills, reuse everywhere
• ✅ Progressive disclosure - Load only the skills you need, when you need them
• ✅ Follows Anthropic’s MCP pattern - Code execution with MCP for efficient agents

Efficiency gains:

• 📉 98.7% fewer tokens when discovering tools progressively vs loading all upfront
• 🔄 Intermediate results stay in code - Process large datasets without bloating context
• ⚡ Single execution - Complex multi-step workflows in one code block instead of chained tool calls

This aligns with Anthropic’s research showing agents scale better by writing code to call tools rather than making direct tool calls for each operation.

🔓 Not Locked to Claude UI

Unlike the Claude interface, this system uses the Model Context Protocol (MCP), which is: