# <img src="docs/figure/agentskills-logo.png" alt="Agent Skills MCP Logo" width="5%" style="vertical-align: middle;"> AgentSkills MCP: Bringing Anthropic's Agent Skills to Any MCP-compatible Agent
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<strong></strong>
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<a href="./README_ZH.md">简体中文</a> | English
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## 📖 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](https://github.com/flowllm-ai/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](https://www.anthropic.com/engineering/equipping-agents-for-the-real-world-with-agent-skills), 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](https://github.com/anthropics/skills)
- ✅ **MCP Support**: multiple transports (stdio/SSE/HTTP), works with any MCP-compatible agent<!-- - ✅ **Progressive Disclosure**: smart context loading, minimal overhead until skills are needed -->
- ✅ **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:
```bash
pip install mcp-agentskills
```
Or with uv:
```bash
uv pip install mcp-agentskills
```
<details>
<summary><strong>For Development (if you want to modify the code):</strong></summary>
```bash
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 .
```
</details>
---
### Load Skills
1. Create a directory to store Skills, like:
```bash
mkdir skills
```
2. Clone from open-source GitHub repositories, e.g.,
```bash
https://github.com/anthropics/skills
https://github.com/ComposioHQ/awesome-claude-skills
```
3. Add the collected Skills into the directory created in step 1. Each Skill is a folder containing a SKILL.md file.
---
### Run
<details>
<summary><strong>Local process communication (stdio)</strong></summary>
<p align="left">
<sub>This mode runs AgentSkills MCP via <code>uvx</code> and communicates through stdin/stdout, suitable for local MCP clients.</sub>
</p>
```json
{
"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"
}
}
}
}
```
</details>
<details>
<summary><strong>Remote communication (SSE/HTTP Server)</strong></summary>
<p align="left">
<sub>This mode runs AgentSkills MCP as a standalone SSE/HTTP server that can be accessed remotely.</sub>
</p>
**- Step 1:** Configure Environment Variables
Copy `example.env` to `.env` and fill in your API key:
```bash
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:
```bash
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:
```json
{
"mcpServers": {
"agentskills-mcp": {
"type": "sse",
"url": "http://0.0.0.0:8001/sse"
}
}
}
```
- You can also use the [FastMCP](https://gofastmcp.com/getting-started/welcome) Python client to directly access the server:
```python
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
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<sub>This command will start the server, connect via FastMCP client, and test all available tools automatically.</sub>
</p>
```bash
python tests/run_project_sse.py <path/to/skills>
or
python tests/run_project_http.py <path/to/skills>
```
</details>
### Demo
After starting the AgentSkills MCP server with the SSE transport, you can run the demo:
```bash
# 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](docs/tools.md).
## ⚙️ 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` |
<!-- | `llm.default.model_name` | Default LLM model name (overrides settings in config files) | `llm.default.model_name=qwen3-30b-a3b-thinking-2507` | -->
For the full set of available options and defaults, refer to [default.yaml](./agentskills_mcp/config/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:
1. Install the package in development mode:
```bash
pip install -e .
```
2. Install pre-commit hooks:
```bash
pip install pre-commit
pre-commit run --all-files
```
3. Submit a pull request with your changes.
---
## 📚 Learn More
- [Anthropic Agent Skills Documentation](https://code.claude.com/docs/zh-CN/skills)
- [Anthropic Engineering Blog](https://www.anthropic.com/engineering/equipping-agents-for-the-real-world-with-agent-skills)
- [Claude Agent Skills: A First Principles Deep Dive](https://leehanchung.github.io/blogs/2025/10/26/claude-skills-deep-dive/)
- [FlowLLM Documentation](https://flowllm-ai.github.io/flowllm/)
- [MCP Documentation](https://modelcontextprotocol.io/docs/getting-started/intro)
## ⚖️ License
This project is licensed under the Apache License 2.0 — see [LICENSE](./LICENSE) for details.
---
## 📈 Star History
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