brightdata-reference-architecture
Implement Bright Data reference architecture with best-practice project layout. Use when designing new Bright Data integrations, reviewing project structure, or establishing architecture standards for Bright Data applications. Trigger with phrases like "brightdata architecture", "brightdata best practices", "brightdata project structure", "how to organize brightdata", "brightdata layout".
Best use case
brightdata-reference-architecture is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Implement Bright Data reference architecture with best-practice project layout. Use when designing new Bright Data integrations, reviewing project structure, or establishing architecture standards for Bright Data applications. Trigger with phrases like "brightdata architecture", "brightdata best practices", "brightdata project structure", "how to organize brightdata", "brightdata layout".
Teams using brightdata-reference-architecture should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/brightdata-reference-architecture/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How brightdata-reference-architecture Compares
| Feature / Agent | brightdata-reference-architecture | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Implement Bright Data reference architecture with best-practice project layout. Use when designing new Bright Data integrations, reviewing project structure, or establishing architecture standards for Bright Data applications. Trigger with phrases like "brightdata architecture", "brightdata best practices", "brightdata project structure", "how to organize brightdata", "brightdata layout".
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
Related Guides
Best AI Skills for Claude
Explore the best AI skills for Claude and Claude Code across coding, research, workflow automation, documentation, and agent operations.
AI Agents for Coding
Browse AI agent skills for coding, debugging, testing, refactoring, code review, and developer workflows across Claude, Cursor, and Codex.
ChatGPT vs Claude for Agent Skills
Compare ChatGPT and Claude for AI agent skills across coding, writing, research, and reusable workflow execution.
SKILL.md Source
# Bright Data Reference Architecture
## Overview
Production-ready architecture for Bright Data scraping systems. Covers project layout, data pipeline design, and integration patterns for Web Unlocker, Scraping Browser, SERP API, and Datasets API.
## Prerequisites
- Understanding of layered architecture
- Node.js/TypeScript project setup
- Database for storing scraped data
## Project Structure
```
my-scraper/
├── src/
│ ├── brightdata/
│ │ ├── proxy.ts # Proxy config helper (zone, country, session)
│ │ ├── client.ts # Axios client with proxy + retry
│ │ ├── browser.ts # Scraping Browser connection manager
│ │ ├── api.ts # REST API client (trigger, snapshot)
│ │ ├── cache.ts # Response cache (LRU + optional Redis)
│ │ └── types.ts # Shared TypeScript interfaces
│ ├── scrapers/
│ │ ├── product-scraper.ts # Domain-specific scraper
│ │ ├── serp-scraper.ts # Search result collector
│ │ └── parser.ts # HTML → structured data (cheerio)
│ ├── pipeline/
│ │ ├── scheduler.ts # Cron-based scraping scheduler
│ │ ├── processor.ts # Raw HTML → clean data
│ │ └── storage.ts # Database/file output
│ ├── webhooks/
│ │ └── brightdata.ts # Webhook delivery handler
│ └── api/
│ ├── health.ts # Health check endpoint
│ └── scrape.ts # On-demand scrape endpoint
├── tests/
│ ├── unit/ # Mocked tests (no proxy needed)
│ ├── integration/ # Live proxy tests
│ └── fixtures/ # Cached HTML for testing
├── config/
│ ├── zones.json # Zone configuration per environment
│ └── targets.json # Target URLs and scraping schedules
└── .env.example
```
## Architecture Diagram
```
┌──────────────────────────────────────────────────────┐
│ API / Scheduler │
│ (On-demand scrape, cron jobs, webhooks) │
├──────────────────────────────────────────────────────┤
│ Scraper Layer │
│ (Product scraper, SERP scraper, custom parsers) │
├────────────┬─────────────────┬───────────────────────┤
│ Web │ Scraping │ SERP / Datasets │
│ Unlocker │ Browser │ API │
│ (Proxy) │ (WebSocket) │ (REST) │
├────────────┴─────────────────┴───────────────────────┤
│ Bright Data Infrastructure Layer │
│ (Proxy config, retry, cache, session management) │
├──────────────────────────────────────────────────────┤
│ Storage / Pipeline │
│ (Database, file output, webhook delivery) │
└──────────────────────────────────────────────────────┘
```
## Key Components
### Step 1: Multi-Product Client
```typescript
// src/brightdata/client.ts
import axios, { AxiosInstance } from 'axios';
import https from 'https';
import { chromium } from 'playwright';
export class BrightDataClient {
private proxyClient: AxiosInstance;
private apiToken: string;
constructor(private config: {
customerId: string;
zone: string;
zonePassword: string;
apiToken: string;
}) {
this.apiToken = config.apiToken;
this.proxyClient = axios.create({
proxy: {
host: 'brd.superproxy.io',
port: 33335,
auth: {
username: `brd-customer-${config.customerId}-zone-${config.zone}`,
password: config.zonePassword,
},
},
httpsAgent: new https.Agent({ keepAlive: true, rejectUnauthorized: false }),
timeout: 60000,
});
}
// Web Unlocker — simple HTTP through proxy
async scrape(url: string, country?: string): Promise<string> {
const response = await this.proxyClient.get(url);
return response.data;
}
// Scraping Browser — Playwright over CDP
async scrapeWithBrowser(url: string, extract: (page: any) => Promise<any>) {
const auth = `brd-customer-${this.config.customerId}-zone-scraping_browser1:${this.config.zonePassword}`;
const browser = await chromium.connectOverCDP(`wss://${auth}@brd.superproxy.io:9222`);
try {
const page = await browser.newPage();
await page.goto(url, { waitUntil: 'domcontentloaded', timeout: 60000 });
return await extract(page);
} finally {
await browser.close();
}
}
// Web Scraper API — async bulk collection
async triggerCollection(datasetId: string, inputs: any[]) {
const response = await fetch(
`https://api.brightdata.com/datasets/v3/trigger?dataset_id=${datasetId}&format=json`,
{
method: 'POST',
headers: { 'Authorization': `Bearer ${this.apiToken}`, 'Content-Type': 'application/json' },
body: JSON.stringify(inputs),
}
);
return response.json();
}
}
```
### Step 2: Scraping Pipeline
```typescript
// src/pipeline/scheduler.ts
import cron from 'node-cron';
interface ScrapeJob {
name: string;
urls: string[];
product: 'web_unlocker' | 'scraping_browser' | 'datasets_api';
schedule: string; // cron expression
parser: (html: string) => any;
}
export function startScheduler(jobs: ScrapeJob[], client: BrightDataClient) {
for (const job of jobs) {
cron.schedule(job.schedule, async () => {
console.log(`Running job: ${job.name}`);
if (job.product === 'datasets_api') {
await client.triggerCollection('dataset_id', job.urls.map(url => ({ url })));
} else {
for (const url of job.urls) {
const html = await client.scrape(url);
const data = job.parser(html);
await saveToDatabase(job.name, data);
}
}
});
}
}
```
### Step 3: Environment Configuration
```json
// config/zones.json
{
"development": {
"web_unlocker": "web_unlocker_dev",
"scraping_browser": "scraping_browser_dev",
"api_datasets": true
},
"production": {
"web_unlocker": "web_unlocker_prod",
"scraping_browser": "scraping_browser_prod",
"api_datasets": true
}
}
```
## Decision Matrix
| Scenario | Product | Why |
|----------|---------|-----|
| Simple HTML pages | Web Unlocker | Cheapest, fastest |
| JavaScript SPA | Scraping Browser | Needs browser rendering |
| Search results | SERP API | Pre-parsed JSON output |
| 1000+ URLs one-time | Web Scraper API | Async, handles parallelism |
| Amazon/LinkedIn/etc. | Pre-built Datasets | No code needed |
| Login-required pages | Scraping Browser + sticky session | Session persistence |
## Output
- Multi-product Bright Data client
- Domain-specific scrapers with parsers
- Cron-based scraping pipeline
- Environment-isolated zone configuration
## Error Handling
| Issue | Cause | Solution |
|-------|-------|----------|
| Mixed product confusion | Wrong zone for task | Use decision matrix above |
| Circular dependencies | Tight coupling | Keep scraper layer separate from proxy layer |
| Test pollution | Shared mocks | Use dependency injection |
| Config mismatch | Wrong environment | Load zone config from `zones.json` |
## Resources
- [Bright Data Products Overview](https://brightdata.com/products)
- [Scraping Browser](https://docs.brightdata.com/scraping-automation/scraping-browser/overview)
- [Web Scraper API](https://docs.brightdata.com/scraping-automation/web-data-apis/web-scraper-api/overview)
- [SERP API](https://docs.brightdata.com/scraping-automation/serp-api/overview)
## Next Steps
For multi-environment setup, see `brightdata-deploy-integration`.Related Skills
workhuman-reference-architecture
Workhuman reference architecture for employee recognition and rewards API. Use when integrating Workhuman Social Recognition, or building recognition workflows with HRIS systems. Trigger: "workhuman reference architecture".
wispr-reference-architecture
Wispr Flow reference architecture for voice-to-text API integration. Use when integrating Wispr Flow dictation, WebSocket streaming, or building voice-powered applications. Trigger: "wispr reference architecture".
windsurf-reference-architecture
Implement Windsurf reference architecture with optimal project structure and AI configuration. Use when designing workspace configuration for Windsurf, setting up team standards, or establishing architecture patterns that maximize Cascade effectiveness. Trigger with phrases like "windsurf architecture", "windsurf project structure", "windsurf best practices", "windsurf team setup", "optimize for cascade".
windsurf-architecture-variants
Choose workspace architectures for different project scales in Windsurf. Use when deciding how to structure Windsurf workspaces for monorepos, multi-service setups, or polyglot codebases. Trigger with phrases like "windsurf workspace strategy", "windsurf monorepo", "windsurf project layout", "windsurf multi-service", "windsurf workspace size".
webflow-reference-architecture
Implement Webflow reference architecture — layered project structure, client wrapper, CMS sync service, webhook handlers, and caching layer for production integrations. Trigger with phrases like "webflow architecture", "webflow project structure", "how to organize webflow", "webflow integration design", "webflow best practices".
vercel-reference-architecture
Implement a Vercel reference architecture with layered project structure and best practices. Use when designing new Vercel projects, reviewing project structure, or establishing architecture standards for Vercel applications. Trigger with phrases like "vercel architecture", "vercel project structure", "vercel best practices layout", "how to organize vercel project".
vercel-architecture-variants
Choose and implement Vercel architecture blueprints for different scales and use cases. Use when designing new Vercel projects, choosing between static, serverless, and edge architectures, or planning how to structure a multi-project Vercel deployment. Trigger with phrases like "vercel architecture", "vercel blueprint", "how to structure vercel", "vercel monorepo", "vercel multi-project".
veeva-reference-architecture
Veeva Vault reference architecture for REST API and clinical operations. Use when working with Veeva Vault document management and CRM. Trigger: "veeva reference architecture".
vastai-reference-architecture
Implement Vast.ai reference architecture for GPU compute workflows. Use when designing ML training pipelines, structuring GPU orchestration, or establishing architecture patterns for Vast.ai applications. Trigger with phrases like "vastai architecture", "vastai design pattern", "vastai project structure", "vastai ml pipeline".
twinmind-reference-architecture
Production architecture for meeting AI systems using TwinMind: transcription pipeline, memory vault, action item workflow, and calendar integration. Use when implementing reference architecture, or managing TwinMind meeting AI operations. Trigger with phrases like "twinmind reference architecture", "twinmind reference architecture".
together-reference-architecture
Together AI reference architecture for inference, fine-tuning, and model deployment. Use when working with Together AI's OpenAI-compatible API. Trigger: "together reference architecture".
techsmith-reference-architecture
TechSmith reference architecture for Snagit COM API and Camtasia automation. Use when working with TechSmith screen capture and video editing automation. Trigger: "techsmith reference architecture".