FireCrawl Load & Scale

About

The FireCrawl Load & Scale skill empowers developers to build resilient web scraping infrastructures by providing standardized patterns for performance testing and horizontal scaling. It includes pre-configured k6 scripts for stress testing, Kubernetes Horizontal Pod Autoscaler (HPA) templates for dynamic resource management, and automated capacity estimation logic. Whether you are ramping up for high-volume data extraction or optimizing cloud costs, this skill provides the benchmarks and metrics needed to maintain a stable, high-performance FireCrawl integration.

Key Features

• Kubernetes HPA configuration for CPU and queue-depth based auto-scaling
• 0 GitHub stars
• Automated k6 load testing scripts with customizable stages and thresholds
• Connection pooling patterns to optimize API client resource usage
• Capacity planning logic to estimate headroom and scaling recommendations
• Standardized performance benchmark templates for documentation

Use Cases

• Testing FireCrawl integration limits before a major production launch
• Configuring auto-scaling for scraping workloads that fluctuate in intensity
• Monitoring system health and planning infrastructure upgrades based on P95 latency