Skill UI

This skill provides comprehensive access to Bright Data's SERP API and Web Scraper API. It allows users to collect structured search engine results (Google, Bing, Yahoo) synchronously, or trigger large-scale, asynchronous data collections with webhooks. Ideal for market research, SEO analysis, and large-scale data scraping projects.

This comprehensive guide demonstrates how to scrape live webpages using Bright Data's Web Unlocker proxy. It details how to bypass advanced anti-scraping mechanisms, such as CAPTCHAs and fingerprinting, automatically. Users are provided with full code examples in both Node.js and Python, showing methods for scraping via the proxy endpoint and through the dedicated REST API. Ideal for starting new scraping projects or integrating Bright Data into existing workflows.

This comprehensive guide details the end-to-end process for integrating Bright Data into development projects. It covers gathering zone credentials, setting up environment variables, downloading necessary SSL certificates, and configuring both Node.js and Python environments for robust and reliable web scraping proxy authentication.

This comprehensive checklist guides the secure and systematic deployment of web scraping integrations using Bright Data. It covers critical stages including production zone verification, code quality checks (e.g., rate limiting, error logging), infrastructure setup, robust monitoring configuration, and safe gradual rollout procedures, ensuring reliable, mission-critical data collection processes into production.

This technical guide provides patterns for handling rate limits, throttling, and concurrency issues when scraping data using Bright Data services. It demonstrates implementing concurrency control using queuing, applying exponential backoff for transient errors (like 429 or 503), and managing specific API trigger rate limits (e.g., 20/min, 60/hr). Essential for building robust, large-scale web scrapers.

A comprehensive, production-ready reference architecture for building robust data scraping systems using Bright Data's full suite of tools (Web Unlocker, Scraping Browser, SERP API). This template provides best practices for project layout, managing proxies, designing data pipelines, and integrating scheduling/storage components in a Node.js/TypeScript environment.

This guide provides production-ready patterns for integrating with Bright Data proxy services. It covers critical aspects like setting up a proxy client, implementing robust retry logic for transient errors (e.g., 502, 503), managing sticky sessions for consistent IPs, and parsing complex HTML responses. Essential for building reliable, high-scale web scrapers using TypeScript and Python.

This guide outlines security best practices for managing Bright Data zone credentials, API tokens, and webhooks. It covers crucial steps like credential inventory, environment variable security, zone isolation across environments, scheduled credential rotation, and implementing pre-commit hooks to prevent sensitive data leakage, maximizing data security and compliance.

This comprehensive guide provides developers with step-by-step instructions for migrating between different Bright Data services and API versions. It covers complex transitions, such as upgrading from raw proxies to Web Unlocker, moving from Puppeteer to Scraping Browser, and updating datasets API endpoints (v2 to v3). Includes prerequisites, code examples, and rollback procedures for seamless integration.

This comprehensive guide details how to implement robust webhook endpoints to receive, validate, and process data deliveries from Bright Data's Web Scraper and Datasets APIs. Learn best practices for handling large JSON/NDJSON payloads, verifying authorization headers, processing status notifications, and ensuring data deduplication in automated scraping pipelines.

This guide details how to configure webhook notifications and utilize audit logs from CAST AI. It allows users to set up real-time alerts for critical cluster events like node scaling, cost threshold breaches, and security findings. By integrating with Slack, PagerDuty, and custom HTTP endpoints, users can achieve comprehensive monitoring and automated response for their infrastructure.

Utilize Anthropic Message Batches API for high-volume, asynchronous bulk processing of AI tasks. This method allows processing up to 10,000 requests per batch, ideal for summarizing large document sets or running repetitive tasks where immediate, synchronous results are not required. The process involves creating the batch, polling for status completion (within 24 hours SLA), and then retrieving detailed results, including success and error statuses for each item.