Skill UI

This guide details setting up automated Continuous Integration/Continuous Deployment (CI/CD) pipelines for Firecrawl scraping services. It covers configuring GitHub Actions, managing API keys, and implementing both mock unit tests and live integration tests. Use this when ensuring scraping behavior is validated automatically across pull requests and merges.

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.

A comprehensive runbook providing systematic procedures for responding to Firecrawl API outages and integration failures. It guides technical teams through triage, diagnosis, and mitigation for common issues (e.g., 401, 429, 5xx errors). Includes steps for fallback implementation, evidence collection, and postmortem documentation to ensure rapid service recovery.

This comprehensive guide identifies common anti-patterns and integration mistakes when using Firecrawl for web scraping and content extraction. It serves as a vital code review checklist, helping developers avoid pitfalls such as unbounded crawling, unspecified output formats, or aggressive API polling, ensuring robust and efficient data pipelines.

This comprehensive guide details how to set up a robust, local development environment for Firecrawl integrations. It utilizes self-hosted Docker containers and Redis to eliminate reliance on paid API credits during development. The setup supports both unit testing (with mocked SDK) and full integration testing against the local instance, ensuring a seamless and efficient workflow for scraping and web data extraction projects.

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.

This guide provides comprehensive setup for implementing CI/CD pipelines for Fireflies.ai integrations. It covers best practices for validating GraphQL queries, running mock-based unit tests (using Vitest), and executing live integration tests against the actual API. It integrates seamlessly with GitHub Actions, ensuring that code changes are automatically validated in a stable, automated testing environment before deployment. Essential for maintaining API stability and robustness.

This comprehensive guide serves as a quick reference for diagnosing and fixing common errors encountered when interacting with the Fireflies.ai GraphQL API. It covers specific error codes (e.g., authentication failure, rate limits, invalid parameters) and provides actionable fixes, diagnostic scripts, and schema introspection techniques, ensuring seamless API integration and rapid troubleshooting.

This utility collects comprehensive diagnostic evidence for Fireflies.ai integration issues. It captures environment details (Node/Python versions, OS), checks API connectivity, gathers account metadata, and pulls recent transcript metadata. The output is a safe, redacted bundle ideal for submitting support tickets or troubleshooting persistent platform problems.

A comprehensive guide for deploying Fireflies.ai integrations into production environments. This skill covers setting up the GraphQL client, implementing secure webhook receivers with signature verification, and managing multi-platform deployments across Vercel, Docker, and Google Cloud Run. Ideal for integrating AI-powered meetings data into production services.

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.

A comprehensive runbook for rapidly responding to Fireflies.ai integration failures. Provides structured triage steps, P1-P4 severity assessment, and detailed remediation guides for common issues like API key expirations, rate limiting, and webhook discrepancies. Essential for on-call engineers during platform outages.