firecrawl-performance-tuning

Quick Review

Excellent skill that provides comprehensive FireCrawl API performance optimization techniques. The description clearly identifies use cases and trigger phrases. Task knowledge is thorough with concrete code examples covering caching (in-memory and Redis), batching with DataLoader, connection pooling, pagination, and monitoring. Structure is well-organized with clear sections, benchmarks, and an error handling table. Novelty is moderate-to-good: while individual techniques (caching, batching, connection pooling) are standard patterns, their specific application to FireCrawl API with complete working code examples and integration reduces token usage for a CLI agent that would otherwise need to research and synthesize these patterns. The skill meaningfully consolidates domain-specific optimization knowledge into a reusable package. Minor improvement could include more FireCrawl-specific edge cases or advanced tuning parameters.