What load testing tools does this skill support?

This skill primarily utilizes k6 for load testing, providing ready-to-use JavaScript test scripts and CLI commands for stress and performance testing.

What metrics are most important for FireCrawl performance?

Crucial metrics include CPU and memory utilization, request queue depth, API error rates, and P95 latency to ensure stable data extraction.

Can I use this for cloud-hosted FireCrawl integrations?

Yes, while it includes Kubernetes templates for self-hosted instances, the load testing logic and capacity planning metrics apply to both cloud-hosted and self-hosted FireCrawl integrations.

Does this skill help with resource management?

Yes, it includes connection pooling patterns in TypeScript to help manage client connections efficiently and prevent resource exhaustion during peak loads.

How does the auto-scaling implementation work?

The skill provides YAML templates for Kubernetes Horizontal Pod Autoscalers (HPA) that dynamically scale your infrastructure based on CPU utilization or custom queue-depth metrics.

FireCrawl Load & Scale

Name: FireCrawl Load & Scale
Author: Brmbobo

byBrmbobo

분석 및 모니터링

Optimizes FireCrawl performance through automated load testing, Kubernetes auto-scaling, and proactive capacity planning.

소개

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.

주요 기능

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

사용 사례

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

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add brmbobo/web2podcast firecrawl-load-scale

For use in Claude.ai and ChatGPT

Download Skill

GitHub

소개