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Skill
nodejs-backend-patterns
Original text · EnglishBuild production-ready Node.js backend services with Express/Fastify, implementing middleware patterns, error handling, authentication, database integration, and API design best practices. Use when creating Node.js servers, REST APIs, GraphQL backends, or microservices architectures.
Republished in full under its license, with attribution. The source is authoritative.
name: nodejs-backend-patterns
description: Build production-ready Node.js backend services with Express/Fastify, implementing middleware patterns, error handling, authentication, database integration, and API design best practices. Use when creating Node.js servers, REST APIs, GraphQL backends, or microservices architectures.
Node.js Backend Patterns
Comprehensive guidance for building scalable, maintainable, and production-ready Node.js backend applications with modern frameworks, architectural patterns, and best practices.
When to Use This Skill
Building REST APIs or GraphQL servers
Creating microservices with Node.js
Implementing authentication and authorization
Designing scalable backend architectures
Setting up middleware and error handling
Integrating databases (SQL and NoSQL)
Building real-time applications with WebSockets
Implementing background job processing
Detailed patterns and worked examples
Detailed pattern documentation lives in references/details.md. Read that file when the navigation tier above is insufficient.
Best Practices
Use TypeScript: Type safety prevents runtime errors
Implement proper error handling: Use custom error classes
Validate input: Use libraries like Zod or Joi
Use environment variables: Never hardcode secrets
Implement logging: Use structured logging (Pino, Winston)
Add rate limiting: Prevent abuse
Use HTTPS: Always in production
Implement CORS properly: Don't use * in production
Use dependency injection: Easier testing and maintenance
Write tests: Unit, integration, and E2E tests
Handle graceful shutdown: Clean up resources
Use connection pooling: For databases
Implement health checks: For monitoring
Use compression: Reduce response size
Monitor performance: Use APM tools
Testing Patterns
See javascript-testing-patterns skill for comprehensive testing guidance.
Reference for the Claude API / Anthropic SDK — model ids, pricing, params, streaming, tool use, MCP, agents, caching, token counting, model migration. TRIGGER — read BEFORE opening the target file; don't skip because it "looks like a one-liner" — whenever: the prompt names Claude/Anthropic in any form (Claude, Anthropic, Fable, Opus, Sonnet, Haiku, `anthropic`, `@anthropic-ai`, `claude-*`, `us.anthropic.*`,…
Guide users through a structured workflow for co-authoring documentation. Use when user wants to write documentation, proposals, technical specs, decision docs, or similar structured content. This workflow helps users efficiently transfer context, refine content through iteration, and verify the doc works for readers. Trigger when user mentions writing docs, creating proposals, drafting specs, or similar…
Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).
Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.
Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.
Use after generating code, after accepting AI suggestions, or when reviewing AI-written modules. Also use when code works but feels brittle, when error handling seems thin, when orphaned resources or missing cleanup are suspected, or when the agent claims done but hidden debt may exist. Catches the specific failure patterns AI agents produce that humans would not.