What can you do with this server?

The AgentSkills MCP server brings Anthropic's Agent Skills to any MCP-compatible agent through a Progressive Disclosure architecture that efficiently manages context windows by loading capabilities only when needed. Core Capabilities: * Discover Available Skills - Load metadata (names and descriptions) for all skills in your configured directory at startup to understand available capabilities * Load Skill Instructions - Dynamically access full instructions from SKILL.md files for specific skills when triggered * Access Reference Files - Read supporting documentation and reference files (e.g., forms.md, reference.md, ooxml.md) from within skills for detailed guidance * Execute Shell Commands - Run shell commands within skill directories to execute scripts and tools, with commands executed in the skill's context * Multiple Transport Modes - Communicate via stdio (local), SSE (Server-Sent Events), or HTTP (RESTful) protocols for flexible deployment Key Benefits: * Universal compatibility with any MCP-compatible agent, not just Claude * Automatic detection and parsing of skills from customizable directories * Progressive context loading preserves context window space * Compatible with Anthropic's official Agent Skills format and community-created skills

Which integrations are available for this server?

Supports streaming deep research capabilities inspired by LangChain's open_deep_research, providing real-time comprehensive analysis through HTTP endpoints.

How do I use AgentSkills MCP?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@AgentSkills MCP analyze the latest financial report for Alibaba" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

AgentSkills MCP: Bringing Anthropic's Agent Skills to Any MCP-compatible Agent

📖 Project Overview

Agent Skills is a new function recently introduced by Anthropic. By packaging specialized skills into modular resources, it allows Claude to transform on demand into a “tailored expert” suited to any scenario. AgentSkills MCP, built on the FlowLLM framework, unlocks Claude’s proprietary Agent Skills for any MCP-compatible agent. It implements the Progressive Disclosure architecture proposed in Anthropic’s official Agent Skills engineering blog, enabling agents to load necessary skills as needed, thereby efficiently utilizing limited context windows.

💡 Why Choose AgentSkills MCP?

✅ Zero-Code Configuration: one-command install (pip install mcp-agentskills)
✅ Out-of-the-Box: uses official Skill format and fully compatible with Anthropic’s Agent Skills
✅ MCP Support: multiple transports (stdio/SSE/HTTP), works with any MCP-compatible agent
✅ Flexible Skill Path: custom skill directories with automatic detection, parsing, and loading

🔥 Latest Updates

[2025-12] 🎉 Released mcp-agentskills v0.1.1

🚀 Quick Start

Installation

Install AgentSkills MCP with pip:

pip install mcp-agentskills

Or with uv:

uv pip install mcp-agentskills

git clone https://github.com/zouyingcao/agentskills-mcp.git cd agentskills-mcp conda create -n agentskills-mcp python==3.10 conda activate agentskills-mcp pip install -e .

Load Skills

Create a directory to store Skills, like:

mkdir skills

Clone from open-source GitHub repositories, e.g.,

https://github.com/anthropics/skills https://github.com/ComposioHQ/awesome-claude-skills

Add the collected Skills into the directory created in step 1. Each Skill is a folder containing a SKILL.md file.

Run

{ "mcpServers": { "agentskills-mcp": { "command": "uvx", "args": [ "agentskills-mcp", "config=default", "mcp.transport=stdio", "metadata.skill_dir=\"./skills\"" ], "env": { "FLOW_LLM_API_KEY": "xxx", "FLOW_LLM_BASE_URL": "https://dashscope.aliyuncs.com/compatible-mode/v1" } } } }

- Step 1: Configure Environment Variables

Copy example.env to .env and fill in your API key:

cp example.env .env # Edit the .env file and fill in your API key

- Step 2: Start the Server

Start the AgentSkills MCP server with SSE transport:

agentskills-mcp \ config=default \ mcp.transport=sse \ mcp.host=0.0.0.0 \ mcp.port=8001 \ metadata.skill_dir="./skills"

The service will be available at: http://0.0.0.0:8001/sse

- Step 3: Connect from MCP Client

Add this configuration to your MCP client (Cursor, Gemini Code, Cline, etc.) to connect to the remote SSE server:

{ "mcpServers": { "agentskills-mcp": { "type": "sse", "url": "http://0.0.0.0:8001/sse" } } }

You can also use the FastMCP Python client to directly access the server:

import asyncio from fastmcp import Client async def main(): async with Client("http://0.0.0.0:8001/sse") as client: tools = await client.list_tools() for tool in tools: print(tool) result = await client.call_tool( name="load_skill", arguments={ "skill_name"="pdf" } ) print(result) asyncio.run(main())

One-Command Test

python tests/run_project_sse.py <path/to/skills> or python tests/run_project_http.py <path/to/skills>

Demo

After starting the AgentSkills MCP server with the SSE transport, you can run the demo:

# Enable Agent Skills for the Qwen model. # Since Qwen supports function calling, you can implement Agent Skills by passing the MCP tools registered by the AgentSkills MCP service to the tools parameter. cd tests python run_skill_agent.py

🔧 MCP Tools

This service provides four tools to support Agent Skills:

load_skill_metadata_op — Loads the names and descriptions of all Skills into the agent context at startup (always called)
load_skill_op — When a specific skill is needed, loads the SKILL.md content by skill name (invoked when triggering the Skill)
read_reference_file_op — Reads specific files from a skill, such as scripts or reference documents (on demand)
run_shell_command_op — Executes shell commands to run executable scripts included in the skill (on demand)

For detailed parameters and usage examples, see the documentation.

⚙️ Server Configuration Parameters

Parameter	Description	Example
`config`	Configuration files to load (comma-separated). Default: `default` (core workflow)	`config=default`
`mcp.transport`	Transport mode: `stdio` (stdin/stdout, good for local), `sse` (Server-Sent Events, good for online apps), `http` (RESTful, good for lightweight remote calls)	`mcp.transport=stdio`
`mcp.host`	Host address (for sse/http transport only)	`mcp.host=0.0.0.0`
`mcp.port`	Port number (for sse/http transport only)	`mcp.port=8001`
`metadata.skill_dir`	Skills Directory (required)	`metadata.skill_dir=./skills`

For the full set of available options and defaults, refer to default.yaml.

Environment Variables

Variable Name	Required	Description
`FLOW_LLM_API_KEY`	✅ Yes	API key for OpenAI-compatible LLM Service
`FLOW_LLM_BASE_URL`	✅ Yes	Base URL for OpenAI-compatible LLM Service

🤝 Contributing

We welcome community contributions! To get started:

Install the package in development mode:

pip install -e .

Install pre-commit hooks:

pip install pre-commit pre-commit run --all-files

Submit a pull request with your changes.

📚 Learn More

⚖️ License

This project is licensed under the Apache License 2.0 — see LICENSE for details.

AgentSkills MCP