ProteinMCP — Design skill for Claude Code
An agentic framework for autonomous protein design.
How to install ProteinMCP
This entry records only its repository, not the path inside it, so there is no
exact command to give. Open charlesxu90/ProteinMCP and copy the folder into
~/.claude/skills/, or the file into ~/.claude/agents/.
What ProteinMCP does
An agentic framework for autonomous protein design.
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README
ProteinMCP: An Agentic AI Framework for Autonomous Protein Engineering
[](https://charlesxu90.github.io/ProteinMCP/) [](./LICENSE)
**[Documentation](https://charlesxu90.github.io/ProteinMCP/)** | **[Installation](https://charlesxu90.github.io/ProteinMCP/installation)** | **[Quick Start](https://charlesxu90.github.io/ProteinMCP/quickstart)** | **[MCP Catalog](https://charlesxu90.github.io/ProteinMCP/mcps/)** | **[Workflows](https://charlesxu90.github.io/ProteinMCP/workflows/)**
Prerequisites
The following tools must be installed on your system:
| Tool | Purpose | Install Guide |
|---|---|---|
| Python 3.10+ | Core runtime | python.org |
| Conda/Mamba | Environment management | miniforge |
| Node.js / npm | Claude Code CLI | nodejs.org |
| Docker (with GPU support) | Containerized MCP servers | docs.docker.com |
| NVIDIA drivers + nvidia-container-toolkit | GPU access in Docker | NVIDIA Container Toolkit |
Verify your setup:
python --version # >= 3.10
conda --version # or mamba --version
npm --version
docker --version
nvidia-smi # GPU available
docker run --rm --gpus all nvidia/cuda:12.1.0-base-ubuntu22.04 nvidia-smi # GPU in DockerInstallation
Step 1 — Create the Python environment
mamba env create -f environment.yml
mamba activate protein-mcp
pip install -r requirements.txt
pip install -e .Step 2 — Install Claude Code CLI
npm install -g @anthropic-ai/claude-codeStep 3 — Verify the installation
pmcp avail # List aRelated Skills
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