What platforms does this skill support?

The skill is designed to support Kubernetes (Horizontal Pod Autoscaler) and general cloud infrastructure platforms that utilize metric-based scaling policies.

Does it provide deployment-ready code?

Yes, it generates production-ready configuration files, such as YAML for Kubernetes, that are formatted according to industry best practices.

How does this help with high availability?

By automating the setup of scaling policies, it ensures your infrastructure dynamically responds to load increases, preventing crashes and maintaining uptime.

Can it scale based on custom metrics?

Yes, the skill can configure scaling thresholds based on CPU, memory utilization, or specific traffic metrics like the number of incoming requests.

Does it help with cost optimization?

Yes, by configuring scaling-down policies for low-traffic periods, it helps reduce unnecessary resource consumption and cloud costs.

Auto-Scaling Configurator

Name: Auto-Scaling Configurator
Author: BbgnsurfTech

byBbgnsurfTech

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Deployment & DevOps

Configures dynamic auto-scaling policies and Horizontal Pod Autoscalers to optimize resource allocation and application availability.

About

The Auto-Scaling Configurator is a specialized Claude Code skill designed to automate the creation of production-ready scaling policies for applications and infrastructure. It analyzes specific performance requirements—such as CPU thresholds, memory utilization, or request volume—to generate precise configuration code for platforms like Kubernetes (HPA) and cloud infrastructure. By streamlining the setup of high availability and dynamic resource allocation, this skill helps developers maintain optimal performance during traffic spikes while reducing costs during idle periods through intelligent resource management.

Key Features

Infrastructure-level dynamic scaling policy creation
Best practice implementation for fault tolerance and high availability
Automated HPA configuration generation for Kubernetes
Production-ready YAML and infrastructure-as-code output
3 GitHub stars
Metric-based scaling for CPU, memory, and request volume

Use Cases

Setting up scheduled scaling for infrastructure to handle peak business hours
Configuring Horizontal Pod Autoscalers for containerized web applications
Implementing dynamic resource limits to prevent application downtime during load spikes

About

Key Features

Infrastructure-level dynamic scaling policy creation
Best practice implementation for fault tolerance and high availability
Automated HPA configuration generation for Kubernetes
Production-ready YAML and infrastructure-as-code output
3 GitHub stars
Metric-based scaling for CPU, memory, and request volume

Use Cases

Setting up scheduled scaling for infrastructure to handle peak business hours
Configuring Horizontal Pod Autoscalers for containerized web applications
Implementing dynamic resource limits to prevent application downtime during load spikes