coding-node
Node.js 22+: async patterns, ESM, npm/pnpm, event loop, streams. Express Fastify Hono
What this skill does
# coding-node
## Purpose
This skill provides expertise in Node.js 22 and above, covering asynchronous programming patterns, ECMAScript Modules (ESM), package managers like npm and pnpm, the event loop, streams, and frameworks such as Express, Fastify, and Hono. Use it to guide users in building efficient, modern server-side applications.
## When to Use
Apply this skill when users need to handle asynchronous operations in Node.js, manage dependencies with npm or pnpm, debug event loop issues, process streams, or set up web servers with Express, Fastify, or Hono. Use it for backend development tasks like API creation, file handling, or real-time applications in JavaScript environments.
## Key Capabilities
- **Async Patterns**: Use `async/await` for non-blocking code; e.g., `async function fetchData() { const data = await fetch('url'); return data; }`.
- **ESM Support**: Import modules with `import` syntax; e.g., `import express from 'express';` in Node.js 22+ files with `.mjs` extension or `"type": "module"` in package.json.
- **Package Managers**: Handle dependencies via npm (e.g., `npm install express --save`) or pnpm (e.g., `pnpm add fastify`); use pnpm for faster installs with workspace support.
- **Event Loop**: Explain and optimize for blocking operations; use `process.nextTick()` to schedule callbacks before the next event loop phase.
- **Streams**: Process data in chunks with Readable, Writable, or Transform streams; e.g., `const fs = require('fs'); const readStream = fs.createReadStream('file.txt'); readStream.on('data', chunk => console.log(chunk));`.
- **Frameworks**: Set up servers with Express (e.g., `app.use(express.json())`), Fastify (e.g., `fastify.get('/path', handler)`), or Hono (e.g., `app.get('/route', c => c.text('Hello'))`).
## Usage Patterns
To accomplish tasks, always specify Node.js version 22+ in project setup. For ESM, add `"type": "module"` to package.json and use import statements. For async tasks, wrap code in try-catch blocks with async functions. When using npm, run `npm init -y` to create a package.json, then install packages. For pnpm, initialize with `pnpm init` and use it as a drop-in replacement for npm commands. To handle streams, pipe them directly: e.g., `readStream.pipe(writeStream)`. For frameworks, create an HTTP server instance and define routes; e.g., start Express with `app.listen(3000)`.
## Common Commands/API
- **Npm Commands**: Initialize project: `npm init -y`. Install package: `npm install express --save-prod`. Update dependencies: `npm update`. Run scripts: `npm run start` from package.json.
- **Pnpm Commands**: Add package: `pnpm add fastify`. Remove package: `pnpm remove package`. Install all: `pnpm install`.
- **API Endpoints**: In Express, define routes like `app.get('/users', (req, res) => res.json(users))`. In Fastify, use `fastify.post('/login', async (req, reply) => { /* auth logic */ reply.send({ token: 'abc' }); })`. In Hono, set up: `const app = new Hono(); app.get('/api/data', c => c.json({ data: 'value' }));`.
- **Event Loop Interactions**: Use `setImmediate()` for I/O callbacks; e.g., `setImmediate(() => console.log('After current event loop'));`.
- **Streams API**: Create a readable stream: `const { Readable } = require('stream'); const readable = Readable.from(['line1', 'line2']); readable.pipe(process.stdout);`.
If API keys are needed (e.g., for external services), set them as environment variables: `process.env.API_KEY = 'your_key'`, and access via `$YOUR_API_KEY` in commands.
## Integration Notes
Integrate this skill with other tools by using Node.js as a runtime. For example, combine with databases via `mongoose` for MongoDB: install with `pnpm add mongoose`, then connect in code: `const mongoose = require('mongoose'); mongoose.connect(process.env.MONGO_URI);`. Use dotenv for environment variables: install `npm install dotenv`, then require and configure: `require('dotenv').config(); const key = process.env.API_KEY;`. For deployment, export servers to tools like Vercel or Heroku; e.g., set `start` script in package.json as `"start": "node server.js"`. Ensure compatibility by specifying engine in package.json: `"engines": { "node": ">=22" }`. When integrating streams, chain with other modules like `zlib` for compression: `readStream.pipe(zlib.createGzip()).pipe(writeStream)`.
## Error Handling
Always use try-catch for async functions: e.g., `try { const result = await someAsyncFunction(); } catch (error) { console.error(error.message); }`. For streams, listen for 'error' events: e.g., `readStream.on('error', err => { console.error('Stream error:', err); process.exit(1); })`. In Express, use middleware for errors: `app.use((err, req, res, next) => { res.status(500).send('Server Error'); })`. For npm/pnpm, check exit codes: e.g., in scripts, use `if [ $? -ne 0 ]; then echo "Command failed"; fi`. Validate inputs to prevent event loop blocks, and use `process.on('uncaughtException', (err) => { console.error(err); process.exit(); })` for unhandled errors.
## Concrete Usage Examples
1. **Set Up a Basic Express Server**: Create a file `server.js` with: `import express from 'express'; const app = express(); app.get('/', (req, res) => res.send('Hello World')); app.listen(3000, () => console.log('Server running'));`. Run it with `node --experimental-vm-modules server.js` if using ESM, then access http://localhost:3000.
2. **Process a File Stream with Pnpm**: First, install dependencies: `pnpm add fs`. Then, in code: `const fs = require('fs'); const readStream = fs.createReadStream('input.txt'); readStream.on('data', chunk => console.log(chunk.toString())); readStream.on('end', () => console.log('Done'));`. Execute with `node script.js` to read and log file contents in chunks.
## Graph Relationships
- Related to: coding (same cluster), as it shares JavaScript fundamentals.
- Connected to: other coding skills via tags like "nodejs" and "javascript", potentially linking to frontend skills for full-stack development.
- Integrates with: tools in the "coding" cluster, such as database or deployment skills, for end-to-end application building.
Related in Backend & APIs
jfrog
IncludedInteract with the JFrog Platform via the JFrog CLI and REST/GraphQL APIs. Use this skill when the user wants to manage Artifactory repositories, upload or download artifacts, manage builds, configure permissions, manage users and groups, work with access tokens, configure JFrog CLI servers, search artifacts, manage properties, set up replication, manage JFrog Projects, run security audits or scans, look up CVE details, query exposures scan results from JFrog Advanced Security, manage release bundles and lifecycle operations, aggregate or export platform data, or perform any JFrog Platform administration task. Also use when the user mentions jf, jfrog, artifactory, xray, distribution, evidence, apptrust, onemodel, graphql, workers, mission control, curation, advanced security, exposures, or any JFrog product name.
cupynumeric-migration-readiness
IncludedPre-migration readiness assessor for porting NumPy to cuPyNumeric. Use BEFORE substantial porting work begins when the user asks whether code will scale on GPU, whether they should migrate to cuPyNumeric, which NumPy patterns transfer cleanly, what must be refactored before porting, or mentions pre-port assessment, scaling analysis, or refactor planning. Inspect the user's source code, look up NumPy usage, cross-reference the cuPyNumeric API support manifest, and distinguish distributed-scaling-friendly patterns from blockers such as unsupported APIs, scalar synchronization, host round-trips, Python/object-heavy control flow, shape/data-dependent branching, and in-place mutation hazards. Produce a verdict of READY, LIGHT REFACTOR, SIGNIFICANT REFACTOR, or NOT RECOMMENDED, with concrete refactor pointers.
alibabacloud-data-agent-skill
IncludedInvoke Alibaba Cloud Apsara Data Agent for Analytics via CLI to perform natural language-driven data analysis on enterprise databases. Data Agent for Analytics is an intelligent data analysis agent developed by Alibaba Cloud Database team for enterprise users. It automatically completes requirement analysis, data understanding, analysis insights, and report generation based on natural language descriptions. This tool supports: discovering data resources (instances/databases/tables) managed in DMS, initiating query or deep analysis sessions, real-time progress tracking, and retrieving analysis conclusions and generated reports. Use this Skill when users need to query databases, analyze data trends, generate data reports, ask questions in natural language, or mention "Data Agent", "data analysis", "database query", "SQL analysis", "data insights".
token-optimizer
IncludedReduce OpenClaw token usage and API costs through smart model routing, heartbeat optimization, budget tracking, and native 2026.2.15 features (session pruning, bootstrap size limits, cache TTL alignment). Use when token costs are high, API rate limits are being hit, or hosting multiple agents at scale. The 4 executable scripts (context_optimizer, model_router, heartbeat_optimizer, token_tracker) are local-only — no network requests, no subprocess calls, no system modifications. Reference files (PROVIDERS.md, config-patches.json) document optional multi-provider strategies that require external API keys and network access if you choose to use them. See SECURITY.md for full breakdown.
resend-cli
IncludedUse this skill when the task is specifically about operating Resend from an AI agent, terminal session, or CI job via the official resend CLI: installing/authenticating the CLI, sending/listing/updating/cancelling emails, batch sends, domains and DNS, webhooks and local listeners, inbound receiving, contacts, topics, segments, broadcasts, templates, API keys, profiles, or debugging Resend CLI/API failures. Trigger on mentions of Resend CLI, `resend`, `resend doctor`, `resend emails send`, `resend domains`, `resend webhooks listen`, `resend emails receiving`, or agent-friendly terminal automation.
alibabacloud-odps-maxframe-coding
IncludedUse this skill for MaxFrame SDK development and documentation navigation on Alibaba Cloud MaxCompute (ODPS). Helps answer MaxFrame API, concept, official example, and supported pandas API questions; create data processing programs; read/write MaxCompute tables; debug jobs (remote or local); and build custom DPE runtime images. Trigger when users mention MaxFrame, MaxCompute with MaxFrame, ODPS table processing, DPE runtime, MaxFrame docs/examples, DataFrame/Tensor operations, or GPU runtime setup. Works for both English and Chinese queries about Alibaba Cloud data processing with MaxFrame.