Skill UI

A comprehensive expert resource for building high-performance, async-first APIs using FastAPI, SQLAlchemy 2.0, and Pydantic V2. Covers advanced topics like microservices architecture, WebSockets, advanced security (OAuth2, RBAC), robust testing strategies (pytest), performance optimization, and modern DevOps practices (Docker, Kubernetes). Ideal for mastering scalable, production-ready Python web services.

This guide provides a comprehensive pattern for creating robust and scalable FastAPI routers. It covers essential best practices, including implementing proper authentication logic (required/optional dependencies), defining structured response models using Pydantic, and correctly assigning HTTP status codes. Use this skill when building structured and secure Python backend APIs.

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.

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.

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 skill provides robust, automated guardrails for web scraping pipelines using Firecrawl. It enforces critical policies to ensure compliance and stability, including domain blocklists (preventing scraping prohibited sites), managing API credit budgets, filtering low-quality content (e.g., error pages, thin content), and implementing granular per-domain rate limiting. Use this when building highly reliable, legal, and cost-efficient scraping solutions.

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 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.

Master the implementation of Firecrawl webhooks for robust, real-time handling of asynchronous crawling and batch scraping jobs. Instead of inefficiently polling the crawl status, configure webhook endpoints to receive instant POST events (e.g., `crawl.page`, `crawl.completed`). This guide covers event types, signature verification using HMAC-SHA256, and processing streamed data for building advanced data pipelines.

Provides a robust, event-driven reference architecture for building meeting intelligence platforms using Fireflies.ai. This guide details the full production pipeline, covering multi-source meeting ingestion (Zoom, Teams), webhook processing, and subsequent data enrichment into structured action items, analytics reports, and database storage. Ideal for designing complex integrations or advanced meeting analytics systems.