Skill UI

A comprehensive quick-reference guide for diagnosing and fixing common Firecrawl API errors and response codes. Covers critical issues such as API key validation (401), rate limiting (429), credit exhaustion (402), and handling dynamic content rendering failures in Single Page Applications (SPA). Use this when debugging failed web scraping or crawl job runs.

This utility collects comprehensive diagnostic data for troubleshooting Firecrawl issues. It automatically checks environment variables, verifies API connectivity, captures SDK versions, gathers recent error logs, and runs diagnostic scrapes. This is essential for generating complete, redacted support tickets when encountering persistent or hard-to-reproduce bugs.

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 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 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.

A comprehensive guide and implementation for managing rate limits, exponential backoff, and request queuing when interacting with the Fireflies.ai GraphQL API. This skill ensures robust API calling by detecting 429 errors, handling per-plan quotas, and implementing reliable retry mechanisms.

This resource provides production-ready, type-safe GraphQL client patterns for integrating with the Fireflies.ai API in both TypeScript and Python. It addresses critical engineering concerns such as robust error handling, managing singletons, implementing multi-tenant support, and ensuring structured data validation using best practices.

This guide teaches developers how to achieve type-safe and predictable error handling in TypeScript using functional programming concepts from fp-ts, specifically focusing on the Either and TaskEither patterns. Instead of relying on disruptive try/catch exceptions, this pattern treats errors as explicit data values, forcing the calling code to handle failure paths, which is crucial for building robust, maintainable, and functional APIs and services.

A comprehensive reference guide for developers to debug and resolve common errors encountered when integrating with the Gamma API. Covers specific error codes such as authentication failures (401/403), rate limit issues (429), content generation failures, and timeouts. Provides detailed solutions, best practices, and code snippets for robust error handling and reliable API usage.

A comprehensive runbook providing systematic, step-by-step procedures for responding to and resolving critical incidents related to Gamma integration. It covers diagnostics for full outages, 5xx errors, rate limit exceedances, and high latency, ensuring rapid and structured incident handling during production failures.

A comprehensive solution for implementing robust observability for Gamma API integrations. This pattern provides structured logging, detailed metrics tracking (including request counts, error rates, latency, and credit consumption), and health check endpoints. It is essential for monitoring the reliability and performance of your application when using the Gamma API in a production environment.

A comprehensive, multi-stage checklist designed to ensure that any third-party or internal integration is robust, secure, and fully functional before being deployed to a live production environment. It covers critical areas including API security, error handling, performance optimization, monitoring setup, and disaster recovery planning.