Skill UI

This skill guides the end-to-end development, deployment, and secure integration of complex AI features into Firebase applications. It covers setting up Cloud Functions, Firestore/Storage rules, Authentication, and connecting robustly with Vertex AI/Gemini for advanced AI capabilities. Focuses on building production-grade, highly secure, data-intensive AI systems.

A systematic guide for debugging complex, hard-to-diagnose issues with Firecrawl. Use this when standard troubleshooting fails, such as when scrapes return empty content, crawl jobs hang indefinitely, or webhooks fail to fire. It employs layer-by-layer isolation techniques, covering API connectivity, target URL accessibility, and content quality to pinpoint failure points.

A comprehensive guide to selecting and implementing the optimal Firecrawl architecture pattern for various data ingestion needs. Learn to choose between On-Demand, Scheduled Crawl Pipelines, and Real-Time Ingestion Pipelines based on data volume, required latency, and complexity. This is essential for designing scalable, production-grade scraping infrastructure, including RAG and knowledge base applications.

This advanced workflow executes sophisticated web data gathering tasks. It supports three primary functions: LLM-powered structured data extraction using custom JSON schemas or prompts; efficient batch scraping of multiple known URLs; and rapid site mapping to discover all reachable URLs on a domain. Use this when you require reliable, typed data structures rather than raw markdown content.

This skill provides advanced methods for load testing and scaling web scraping pipelines using Firecrawl. It manages rate limits and optimizes throughput by implementing concurrency control, batch processing, and queue-based request throttling. Essential for capacity planning, stress-testing scraping performance, or maximizing data ingestion efficiency across various service plans.

This guide details how to configure Firecrawl for reliable multi-environment operations (Development, Staging, Production). It addresses critical operational concerns, such as managing credit budgets and ensuring separation between environments. Users learn how to implement self-hosted Docker setups for development, manage environment-specific API keys, and integrate safe scraping wrappers into CI/CD pipelines.

This comprehensive guide details advanced techniques to significantly boost Firecrawl scraping speed and efficiency. Learn how to optimize performance by selecting the most efficient output formats (e.g., Markdown over HTML), implementing URL caching, utilizing batch scraping for multiple URLs, and tuning delay parameters for dynamic Single Page Applications (SPAs). Mastering these methods is crucial for building high-throughput, cost-effective web scraping pipelines.

A comprehensive guide to handling external API rate limits (429 errors) when using Firecrawl. This skill demonstrates advanced techniques like exponential backoff with jitter, request queuing using p-queue, and proactive throttling methods. It ensures resilient and efficient API integration for large-scale scraping projects.

A comprehensive guide to implementing production-grade reliability patterns for web scraping pipelines using Firecrawl. Learn techniques such as circuit breakers, crawl fallbacks, content quality validation, and robust backoff strategies to handle timeouts, API failures, and inconsistent data quality, ensuring maximum uptime and data integrity.

This skill provides deep web scraping capabilities, allowing users to extract complex content, take screenshots, and parse PDFs from web pages. It handles advanced interactions like clicking and scrolling, making it ideal for detailed data gathering, batch URL processing, and complex web content analysis.

This guide provides production-ready patterns for integrating the Firecrawl SDK in TypeScript and Python. It covers essential architectural best practices, such as implementing singleton clients, typed service wrappers, automatic retries with exponential backoff, concurrency management, and robust response validation using Zod. Ideal for building scalable, reliable web scraping services.

This guide outlines essential security best practices for integrating Firecrawl. It covers secure API key storage (using environment variables and .gitignore), verifying webhook signatures with HMAC-SHA256 for data integrity, implementing key rotation strategies across different environments (dev/staging/prod), and sanitizing scraped content to mitigate XSS and PII risks. Essential for building secure, production-grade systems.