firecrawl-performance-tuning

Quick Review

A well-structured skill providing comprehensive performance optimization techniques for FireCrawl API including caching (in-memory and Redis), request batching with DataLoader, connection pooling, and pagination strategies. The description clearly articulates when to use the skill with appropriate trigger phrases. Task knowledge is excellent with complete TypeScript code examples covering multiple optimization patterns, benchmarks, and error handling. Structure is good with logical sections, though a bit dense for a single file. Novelty is moderate - while performance tuning is valuable, the techniques (caching, batching, connection pooling) are standard optimization patterns that a skilled CLI agent could implement, though having them pre-configured for FireCrawl specifically does save tokens and time. The skill would particularly benefit teams repeatedly optimizing FireCrawl integrations.