Nodejs Development logo

Nodejs Development

Community
peterbamuhigire
nodejs-development

Use when creating Node.js backend services, APIs, routing, workers, CLI tools, streams, asynchronous or real-time workflows, production logging, tests, scaling, and deployment; use online-multiplayer-and-game-backend for authoritative game servers.

Overview

Publisherpeterbamuhigire
Repositorychwezi-dev-engine
Skill namenodejs-development
Stars
29
Forks
6
Bundled files
10
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 10 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by peterbamuhigire on GitHub. Read the source before you install it.

Installation

Install the Nodejs Development AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/peterbamuhigire/chwezi-dev-engine/tree/main/skills/languages/nodejs-development
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/peterbamuhigire/chwezi-dev-engine.git /tmp/chwezi-dev-engine
mkdir -p .claude/skills
cp -r /tmp/chwezi-dev-engine/skills/languages/nodejs-development .claude/skills/nodejs-development
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Nodejs Development in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Nodejs Development on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Nodejs Development is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Node.js Development Skill

Acknowledgement: Shared by Peter Bamuhigire, techguypeter.com, +256 784 464178.

Use When

  • Production Node.js development — async patterns, streams, design patterns, HTTP APIs, testing, scaling, and deployment. Synthesised from Node.js Design Patterns (Casciaro & Mammino 3rd ed.), Node.js Recipes (Gackenheimer), Fullstack Node.js (Murray), and Node.js Fundamentals. Use when building scalable servers, REST APIs, CLI tools, real-time systems, or fullstack JavaScript applications.

Workflow

  • Pair with javascript-patterns for advanced object/module pattern selection and docker-development for containerized runtime or asset build delivery.

Evidence Produced

CategoryArtifactFormatExample
CorrectnessNode service test planMarkdown doc per skill-composition-standards/references/test-plan-template.md covering unit, integration, and stream testsdocs/node/test-plan-checkout.md
PerformanceEvent-loop and memory profile reportMarkdown doc covering p50/p95/p99 latency, memory baseline, and GC behaviourdocs/node/perf-profile-checkout.md

References

  • Use the references/ directory for deep detail after reading the core workflow below.

Core Philosophy (The Node Way)

  • Small core — minimal built-ins, rich userland via npm
  • Small modules — each module does one thing well (Unix philosophy)
  • Small surface area — expose minimal API, prefer functions over classes
  • Simplicity > perfection — ship pragmatic code, iterate fast

Node.js uses the reactor pattern via libuv: a single-threaded event loop demultiplexes async I/O events and dispatches them to callbacks. Never block the event loop with CPU-heavy synchronous work.


1. Module System

js
// CommonJS (legacy — still dominant)
module.exports = { greet }
const { greet } = require('./greet')

// ESM (modern — prefer for new code)
export function greet(name) { return `Hello ${name}` }
import { greet } from './greet.js'   // .js extension required

// Dynamic import
const { default: heavy } = await import('./heavy.js')

// ESM __dirname equivalent
import { fileURLToPath } from 'url'
import { dirname } from 'path'
const __dirname = dirname(fileURLToPath(import.meta.url))

Decision: New project → ESM ("type": "module" in package.json). Existing CJS codebase → keep CJS. Public library → dual CJS+ESM.

Singleton via module cache:

js
// db.js — exported instance is cached; importers always get the same object
export const db = new Database(process.env.DB_URL)

2. Async Patterns

Callback convention (error-first, callback last)

js
function readJSON(filename, callback) {
  fs.readFile(filename, 'utf8', (err, data) => {
    if (err) return callback(err)
    try { callback(null, JSON.parse(data)) }
    catch (e) { callback(e) }
  })
}

Anti-pattern — Zalgo: never invoke a callback sync in some paths and async in others. Use process.nextTick(() => cb(...)) to defer sync paths.

async/await (preferred)

js
async function getUser(id) {
  try {
    const user = await db.findById(id)
    if (!user) throw new AppError('Not found', 404)
    return user
  } catch (err) {
    logger.error(err); throw err
  }
}

// NEVER forEach with await — use for...of or Promise.all
for (const item of items) { await process(item) }           // sequential
await Promise.all(items.map(item => process(item)))          // parallel

Infinite loop — avoid recursive promise chains (memory leak):

js
// LEAKS
async function tick() { await delay(1); return tick() }

// SAFE
async function tick() { while (true) { await delay(1); doWork() } }

EventEmitter (Observer pattern — repeated events)

js
import { EventEmitter } from 'events'

class FileWatcher extends EventEmitter {
  watch(files) {
    for (const file of files) {
      fs.readFile(file, 'utf8', (err, data) => {
        if (err) return this.emit('error', err)
        this.emit('file', file)
        data.match(/TODO/g)?.forEach(m => this.emit('match', file, m))
      })
    }
    return this
  }
}

watcher.on('match', (file, text) => console.log(`${file}: ${text}`))
watcher.on('error', err => console.error(err))
// Always remove listeners to prevent memory leaks:
emitter.removeListener('event', handler)   // or use .once()

Choose EventEmitter when: events repeat, multiple listeners needed, or event types differ. Use callbacks for single async results.


3. Streams

js
import { pipeline } from 'stream/promises'
import { createReadStream, createWriteStream } from 'fs'
import { createGzip } from 'zlib'

// PREFER pipeline() over .pipe() — proper error propagation
await pipeline(
  createReadStream('big.csv'),
  createGzip(),
  createWriteStream('big.csv.gz')
)

// Async iterator (cleanest way to consume)
for await (const chunk of readableStream) {
  process(chunk)
}

// Custom Transform stream
import { Transform } from 'stream'
class UpperCase extends Transform {
  _transform(chunk, enc, cb) { this.push(chunk.toString().toUpperCase()); cb() }
}

Rules: Stream files > ~1 MB. Backpressure: write() returns false when buffer full — wait for drain event. Object mode: { objectMode: true }.

See references/streams.md for advanced patterns (forking, merging, mux/demux).


4. HTTP Patterns

js
// Minimal server
import { createServer } from 'http'
const server = createServer((req, res) => {
  res.writeHead(200, { 'Content-Type': 'application/json' })
  res.end(JSON.stringify({ status: 'ok' }))
})
server.listen(process.env.PORT || 3000)

// Static file via stream
const stream = createReadStream(filepath)
stream.on('error', err => { res.statusCode = 404; res.end() })
res.writeHead(200, { 'Content-Type': getMime(filepath) })
stream.pipe(res)

// HTTP client (POST)
const req = request({ hostname, path: '/api', method: 'POST',
  headers: { 'Content-Type': 'application/json' } }, handleResponse)
req.write(JSON.stringify(payload))
req.end()

Express middleware pattern:

js
app.use((req, res, next) => { req.startTime = Date.now(); next() })
app.use((err, req, res, next) => {   // error handler — 4 args
  res.status(err.statusCode || 500).json({ error: err.message })
})

5. Design Patterns

Factory — decouple creation from implementation

js
function createLogger(env) {
  return env === 'production' ? new FileLogger() : new ConsoleLogger()
}

Builder — fluent interface for complex objects

js
const query = new QueryBuilder()
  .from('users').where('active = 1').limit(10).build()

Proxy — intercept and control access

js
const observed = new Proxy(target, {
  set(obj, prop, value) {
    onChange(prop, obj[prop], value)
    obj[prop] = value; return true
  }
})

Middleware — chain of responsibility

js
class Pipeline {
  #steps = []
  use(fn) { this.#steps.push(fn); return this }
  async run(ctx) {
    const go = async (i) => this.#steps[i]?.(ctx, () => go(i + 1))
    await go(0)
  }
}

Strategy — swap algorithms at runtime

js
class Exporter {
  constructor(strategy) { this.strategy = strategy }
  export(data) { return this.strategy.serialize(data) }
}

See references/design-patterns.md for Decorator, Adapter, Singleton, DI, Revealing Constructor, Iterators/Generators.


6. Error Handling

js
class AppError extends Error {
  constructor(message, code, statusCode = 500) {
    super(message); this.code = code; this.statusCode = statusCode
  }
}

// Global handlers — always exit after uncaughtException
process.on('uncaughtException', err => { logger.fatal(err); process.exit(1) })
process.on('unhandledRejection', (reason) => { logger.error(reason); process.exit(1) })

7. Testing

js
// assert/strict (built-in)
assert.deepEqual(result, expected)
assert.rejects(async () => fn(), { code: 'NOT_FOUND' })

// Mocha + Chai (BDD)
describe('UserService', () => {
  it('creates a user', async () => {
    const user = await service.create({ name: 'Alice' })
    expect(user).to.have.property('id')
  })
})

Test layout: *.test.js collocated with source for unit tests; test/integration/ for API tests with real HTTP; test/e2e/ for end-to-end flows.


8. Scaling

js
// Cluster — one worker per CPU core
import cluster from 'cluster'
import { cpus } from 'os'

if (cluster.isPrimary) {
  cpus().forEach(() => cluster.fork())
  cluster.on('exit', (w, code) => {
    if (code !== 0 && !w.exitedAfterDisconnect) cluster.fork()  // restart crashed
  })
} else {
  createServer(handler).listen(8080)
}

// Worker threads — CPU-bound tasks
import { Worker } from 'worker_threads'
const w = new Worker('./compute.js', { workerData: input })
w.on('message', result => handleResult(result))

Scaling decisions:

  • Single machine → cluster (one worker per core)
  • Multi-machine → Nginx/HAProxy reverse proxy + multiple processes
  • Stateful sessions → shared Redis store (never sticky sessions)
  • CPU-bound → worker_threads

9. Security Essentials

js
// Path traversal prevention
import { basename } from 'path'
const safeFile = basename(req.headers['x-filename'])

// AES-256-GCM encryption
import { createCipheriv, randomBytes } from 'crypto'
const iv = randomBytes(16)
const cipher = createCipheriv('aes-256-gcm', key, iv)

// Password hashing
import bcrypt from 'bcrypt'
const hash = await bcrypt.hash(password, 12)

// Environment-based secrets
if (!process.env.JWT_SECRET && process.env.NODE_ENV === 'production') {
  throw new Error('JWT_SECRET required in production')
}

10. Production Deployment

bash
# package.json
"engines": { "node": ">=18" }
"scripts": { "start": "node src/server.js" }

# Health check endpoint
GET /health → { status: 'ok', uptime: process.uptime() }

# Process management
pm2 start src/server.js --instances max --name myapp
# OR systemd with Restart=on-failure

# Zero-downtime: SIGUSR2 → rolling restart of cluster workers

Deployment options:

OptionWhen to use
VPS + Nginx + pm2Full control, high-traffic production
Heroku / RenderFast iteration, low-ops overhead
AWS LambdaStateless APIs, spiky traffic
Docker + K8sMulti-service, horizontal scaling

Quick Reference: Decision Rules

SituationChoice
New projectESM modules
Single async resultasync/await
Repeated eventsEventEmitter
Large file I/OStreams + pipeline()
CPU-bound workworker_threads
Parallel I/OPromise.all()
Limited concurrencymapWithConcurrency()
Scale one machinecluster module
Scale multi-machineNginx reverse proxy
Password storagebcrypt (factor 12+)
EncryptionAES-256-GCM

Deep-Dive References

  • references/async-patterns.md — callbacks, promises, TaskQueue, producer-consumer
  • references/streams.md — backpressure, Transform, merge, fork, mux/demux
  • references/design-patterns.md — all creational/structural/behavioural patterns
  • references/scaling.md — cluster, worker_threads, messaging (AMQP, Redis Streams)
  • references/mongodb-mongoose.md — schema design, validation, relationships, populate
  • references/realtime.md — WebSockets, SSE, Socket.IO
  • references/testing.md — Mocha/Chai, mocking, integration testing
  • references/fastify.md — Fastify server, plugins, hooks, JWT, TypeBox, Swagger, testing
  • references/prisma.md — schema, migrations, CRUD, relations, transactions, TypeScript types
  • references/bullmq.md — queues, workers, retries, cron, FlowProducer, Bull Board, Redis

Sources

  • Node.js Design Patterns 3rd ed. — Casciaro & Mammino (Packt, 2020)
  • Node.js Recipes — Cory Gackenheimer (Apress, 2013)
  • Fullstack Node.js — Nate Murray (Leanpub, 2019)
  • Node.js Fundamentals — Machine Learning
  • Accelerating Server-Side Development with Fastify — Manuel Spigolon (Packt)
  • Next.js 13 + Prisma — Greg Lim

Decision Rules

ConditionAction
Payload is large or continuousStream with backpressure instead of buffering
Work is CPU-boundUse a worker or separate service
Operation may be retriedRequire idempotency and bounded retries

Capability Contract

Read and search are required. Editing, package installation, and execution require authorisation; network access is optional for verification.

Degraded Mode

Fallback: if dependencies or tests cannot run, return exact verification commands and mark runtime assumptions unverified.

Domain Anti-Patterns

  • Buffering an unbounded request or file.
  • Catching an error and continuing with corrupted state.
  • Performing CPU-heavy loops on the event loop.
  • Retrying non-idempotent writes without deduplication.
  • Shipping an unverified package or runtime version.

Inputs

ArtefactRequired?Purpose
Node runtime, service boundary, dependencies, and operational requirementsyesShape implementation and lifecycle controls

Outputs

  • Produce service code or review findings with tests, error handling, observability, and shutdown behaviour.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Nodejs Development AI skill do?

Use when creating Node.js backend services, APIs, routing, workers, CLI tools, streams, asynchronous or real-time workflows, production logging, tests, scaling, and deployment; use online-multiplayer-and-game-backend for authoritative game servers.

Why use Nodejs Development on TypingMind?

Because you install it once and use it with any model. Nodejs Development is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Nodejs Development in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/peterbamuhigire/chwezi-dev-engine/tree/main/skills/languages/nodejs-development. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Nodejs Development?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Nodejs Development?

As many as you like. As long as a model supports skills, you can use Nodejs Development with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Nodejs Development AI skill free?

Yes. It is published on GitHub by peterbamuhigire under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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