Can it help with Kubernetes scaling?

Yes, it provides ready-to-use YAML configurations for Kubernetes Horizontal Pod Autoscalers (HPA) based on both CPU and custom metrics like queue depth.

Does it handle API rate limits?

The skill includes logic for connection pooling and specific error handling strategies designed to manage Replit API limits and prevent timeouts.

How does it calculate capacity?

It uses a built-in capacity estimator that analyzes current requests per second (RPS) and CPU utilization to provide data-driven scaling recommendations.

What load testing tool does this skill use?

This skill primarily utilizes k6, a modern, developer-centric open-source tool for performance testing and benchmarking.

Is this skill specific to Replit?

While it is optimized for Replit integrations, the underlying load testing and scaling patterns are applicable to most enterprise SaaS frontend and backend architectures.

Replit Load & Scale Manager

Name: Replit Load & Scale Manager
Author: Brmbobo

byBrmbobo

Despliegue y DevOps

Implement and manage load testing, auto-scaling, and capacity planning for high-performance Replit integrations.

Acerca de

The Replit Load & Scale skill provides developers with a comprehensive framework for performance testing and infrastructure optimization of Replit-based applications. It automates the generation of k6 load testing scripts, configures Kubernetes Horizontal Pod Autoscalers (HPA) for dynamic traffic handling, and implements efficient connection pooling strategies to prevent resource exhaustion. This skill is essential for enterprise-grade SaaS deployments that require predictable latency, high availability, and data-driven capacity planning to ensure stability during peak usage.

Características Principales

Automated k6 load testing script generation with custom latency thresholds
Kubernetes HPA configuration using CPU and custom queue depth metrics
Connection pooling implementation for optimized Replit API resource management
Real-time capacity estimation and infrastructure headroom analysis
Standardized performance benchmark templates for reporting and auditing
0 GitHub stars

Casos de Uso

Stress testing Replit API integrations before a major product or feature launch
Configuring auto-scaling rules to handle fluctuating traffic in SaaS environments
Calculating infrastructure headroom to optimize cloud costs and performance

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add brmbobo/web2podcast replit-load-scale

For use in Claude.ai and ChatGPT

Download Skill

GitHub