Skill UI

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.

Firecrawl Automation lets you use Claude Code to scrape pages, crawl sites, batch URLs, extract structured data, and monitor jobs via the Composio integration, ideal for terminal-driven web data pipelines.

This workflow provides the primary mechanism for converting complex, live websites into clean, LLM-ready markdown content. It supports single-page scraping using `scrapeUrl`, deep multi-page site crawling with `crawlUrl`, and scalable async crawling for large datasets. Ideal for building robust content ingestion pipelines and reliably extracting structured information from the web.

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 techniques to significantly reduce API costs when using Firecrawl for web scraping. It covers best practices such as setting crawl limits, utilizing batch scraping for known URLs, implementing local caching, and monitoring credit usage. Learn how to efficiently scrape large sites while managing budgets and minimizing unexpected billing spikes.

A comprehensive guide for deploying web scraping applications powered by Firecrawl to various production environments. Covers Vercel serverless functions, Google Cloud Run, and self-hosted Docker setups. Learn how to configure platform-specific secrets, implement API routes, and handle asynchronous crawl results via webhooks for scalable, reliable production service.

Firecrawl provides a comprehensive API for web data harvesting. It enables users to perform single-page scraping, multi-page crawling, site mapping (URL discovery), and advanced structured data extraction using Large Language Models (LLMs). Ideal for integrating data pipelines, building web monitoring tools, or conducting market research.

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.

This comprehensive reference architecture details a multi-stage pipeline for web scraping and content ingestion using Firecrawl. It seamlessly integrates single-page scraping, site-wide crawling, URL mapping, and structured data extraction. The subsequent processing layer handles cleaning, deduplication, and chunking, making this blueprint ideal for designing robust knowledge bases, RAG systems, and complex AI data pipelines.

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.

Comprehensive guide for upgrading the Firecrawl SDK and migrating between major API versions (v0 to v1/v2). It details breaking changes across key functions like `crawlUrl`, `scrapeUrl`, and the new extract schema format. Use this when updating dependencies or transitioning from legacy API structures to the latest v2 standard.

Provides minimal working examples and comprehensive instructions for querying the Fireflies.ai GraphQL API. Users can learn how to list workspace users, fetch recent meeting transcripts, and retrieve detailed summaries, including action items and keywords, using code examples in TypeScript, Python, and Bash. Ideal for initial API setup, integration testing, or learning GraphQL patterns for meeting data.