Skill UI

This guide provides a set of production-grade TypeScript patterns for robust interaction with the Attio REST API. It covers essential best practices including typed clients, automatic retry mechanisms with exponential backoff, cursor-based pagination iterators, and multi-tenant factory implementation, ensuring reliable and scalable API integration. Use these patterns to handle common API challenges like rate limiting and large datasets efficiently.

Provides advanced best practices for optimizing Glean's enterprise search performance and scalability. Covers critical techniques such as implementing effective batch sizing, caching strategies, connection pooling, and robust rate limit management to significantly reduce search latency and maintain high-throughput indexing for massive, diverse datasets.

This comprehensive guide provides advanced strategies for optimizing interactions with the Klaviyo API. It covers implementing response caching (LRU Cache), efficient cursor-based pagination, request batching using DataLoader, and controlling API concurrency. Use this when dealing with slow API responses, large datasets, or high-throughput integration requirements.

This workflow provides a complete, end-to-end solution for building and deploying robust web scrapers using Crawlee on the Apify platform. It guides users through every stage: defining input schemas, setting up the Actor with a sophisticated router pattern, extracting structured data (e.g., product details), saving results to datasets, and managing the full lifecycle from local testing to cloud deployment via apify-cli.

A comprehensive guide to using the Apify Client SDK. Learn the fundamental pattern of executing remote web scraping Actors (like content crawling or general scraping), managing asynchronous run states, and retrieving structured data from resulting datasets and key-value stores. Essential for integrating powerful web scraping capabilities into applications.

This guide details how to set up and test Apify Actors locally using the Apify CLI. It emulates the full platform environment (including datasets, key-value stores, and request queues), allowing developers to run scrapers, test data handling, and debug the entire development lifecycle before deploying to the cloud. Perfect for rapid iteration and rigorous local testing.

A comprehensive guide to building robust, production-ready applications using the Apify SDK and apify-client in TypeScript. Covers advanced topics like standardized client singleton patterns, selecting the right Crawlee crawler (Cheerio, Playwright, Puppeteer), implementing robust Actor lifecycles, and managing dataset/key-value store operations.

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.

A comprehensive guide to optimizing the Finta fundraise workflow. This document addresses critical performance bottlenecks, including pagination issues with large investor datasets, latency in multi-stage round data aggregation, and slow CRM synchronization. Optimization strategies cover cursor-based fetching, strategic caching (TTL management), connection pooling, and robust rate limit handling, aiming to reduce pipeline load times by 50-70% and maintain dashboard responsiveness.

This skill provides comprehensive guidelines and implementation examples for managing rate limits when interacting with the Glean Indexing and Search APIs. It includes a custom RateLimiter class, robust retry logic (handling 429 errors and server errors), and best practices for batch processing large datasets, ensuring stable, high-volume data ingestion pipelines.

Master advanced techniques for optimizing API interactions with HubSpot. Learn to implement batch operations, utilize effective caching strategies (e.g., LRU), minimize property requests, and handle large datasets via efficient pagination and streaming. This skill dramatically improves throughput and reduces API call latency across CRM operations.