pm-skills

PM-Skills MCP exposes the PM-Skills skill library as an MCP server, allowing AI agents to access 25 PM skills, 5 workflow bundles, and 8 utility tools programmatically via the Model Context Protocol. Once the server is running, clients can query and invoke individual tools or bundles to perform product management tasks such as evaluating roadmaps, generating user stories, prioritizing features, defining success metrics, and building multi-skill workflows. The system is designed to be universally accessible from any MCP-compatible client, so you can plug it into your preferred agent, chat interface, or automation layer without platform-specific integration steps. Expect a consistent set of endpoints, prompts, and templates that are versioned with the underlying pm-skills library, along with optional persona resources when enabled.

Prerequisites:

• Node.js (>= 18) and npm installed on your machine
• Internet access to fetch the pm-skills-mcp package

Installation steps:

1. Ensure Node.js and npm are installed. Check versions: node -v npm -v

2. Install the MCP server globally using npm (quick start): npm install -g pm-skills-mcp

3. Run the MCP server with npx (no global install required): npx pm-skills-mcp

4. (Optional) If you need a custom configuration, create a config file as needed and pass environment variables or use your MCP client to supply parameters.

5. Verify the server is running and accessible through your MCP client by invoking one of the exposed skills or prompts.

Notes:

• The README emphasizes quick access via npx pm-skills-mcp for immediate use without global installs.
• You can also install the package globally (npm install -g pm-skills-mcp) and run it as a standalone command.

Tips and considerations:

• The server exposes 38 MCP tools: the 25 core PM skills, 5 pre-built workflow bundles, and 8 utility tools. Familiarize yourself with the Tools section of the README to know what endpoints are available and how to call them via your MCP client.
• Compatibility: Works with any MCP client that adheres to the Model Context Protocol. If you plan to run multiple instances (e.g., testing vs. production), consider isolating configurations and managing environment variables per instance.
• Performance and testing: The project mentions Vitest-based tests; running the test suite locally can help validate client compatibility before deployment.
• Security: If deploying in production, consider restricting access to the MCP server and reviewing any security prompts or templates that may be exposed via pm-skills.
• Customization: You can override skills by forking pm-skills or supplying your own skill modifications; the MCP server is designed to be customizable while preserving core tooling.