AI SummaryThis booster provides best practice guidelines for Docker, npx, uv, and package publishing workflows, intended to standardize development processes in Windsurf environments. It benefits developers seeking consistent build and deployment practices across multiple tool ecosystems.
Install
Copy this and paste it into Claude Code, Cursor, or any AI assistant:
I want to add the "mcp-prompts — Windsurf Rules" prompt rules to my project. Repository: https://github.com/sparesparrow/mcp-prompts Please read the repo to find the rules/prompt file, then: 1. Download it to the correct location (.cursorrules, .windsurfrules, .github/prompts/, or project root — based on the file type) 2. If there's an existing rules file, merge the new rules in rather than overwriting 3. Confirm what was added
Description
Model Context Protocol server for managing, storing, and providing prompts and prompt templates for LLM interactions.
Client Usage
`typescript // Client usage example import { Client } from "@modelcontextprotocol/sdk/client/index.js"; import { SSEClientTransport } from "@modelcontextprotocol/sdk/client/sse.js"; async function connectToServer() { // Initialize client const client = new Client( { name: "my-client", version: "1.0.0" }, { capabilities: {} } ); // Create SSE transport const transport = new SSEClientTransport({ url: "http://localhost:3000", connectEndpoint: "/connect", messageEndpoint: "/messages", headers: { "X-API-Key": "your-api-key-here" } }); // Connect to server await client.connect(transport); // Use the client const tools = await client.listTools(); console.log("Available tools:", tools); } connectToServer().catch(console.error); `
Docker Best Practices
• Multi-stage builds: Separate build and runtime environments • Minimal base images: Use Alpine or slim variants • Non-root users: Run processes as non-privileged users • Version pinning: Pin specific base image versions • Layer optimization: Organize Dockerfile to optimize layer caching • Health checks: Implement container health checks • Resource limits: Set memory and CPU limits
npx Best Practices
• Specify versions: Use exact versions for production • Verify packages: Use the -y flag cautiously • Registry security: Consider using private registries • Cache management: Be aware of npx caching behavior • Performance: Consider startup time for frequently used servers • Exit handling: Implement proper process exit handling • Dependency size: Keep dependencies minimal
uv Best Practices
• Virtual environments: Always use dedicated virtual environments • Lockfiles: Maintain lockfiles for dependency consistency • Offline mode: Consider offline mode for production • Caching: Configure appropriate cache locations • Integration: Ensure proper integration with existing Python tools • Activation scripts: Handle environment activation properly • Platform compatibility: Test on all target platforms
Discussion
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Works With
Any AI assistant that accepts custom rules or system prompts