What platforms does this skill support?

The skill primarily focuses on Kubernetes for Horizontal Pod Autoscaling (HPA) and general cloud infrastructure platforms for dynamic resource allocation.

Does it follow security best practices?

Yes, the skill is designed to incorporate industry-standard security and scalability best practices within the generated configuration code.

Can it scale based on custom metrics?

Yes, it can configure scaling policies based on CPU utilization, memory usage, or specific load-based metrics like the number of incoming requests.

Is the generated configuration ready for production?

The skill produces production-ready code, though it is recommended to test the configurations in a staging environment to ensure they meet specific load conditions.

Auto-Scaling Configurator

Name: Auto-Scaling Configurator
Author: intent-solutions-io

byintent-solutions-io

Deployment & DevOps

Generates production-grade auto-scaling policies and infrastructure configurations to ensure high availability and dynamic resource allocation.

About

The Auto-Scaling Configurator skill empowers Claude to architect and implement dynamic scaling policies across various platforms, including Kubernetes and cloud infrastructure. By analyzing user requirements for metrics like CPU, memory, or request counts, it generates precise configurations such as Horizontal Pod Autoscalers (HPA) and cloud scaling thresholds. This skill is essential for developers looking to maintain application performance during traffic spikes while optimizing resource costs during idle periods, all while adhering to industry best practices for security and resilience.

Key Features

Automated Kubernetes HPA manifest generation
Metric-based scaling threshold configuration for CPU and memory
Production-ready YAML and infrastructure-as-code output
0 GitHub stars
Dynamic resource allocation for cloud infrastructure
Best practice implementation for fault tolerance and high availability

Use Cases

Configuring Kubernetes deployments to scale automatically based on resource utilization
Optimizing cloud expenditure through automated dynamic resource scaling
Managing infrastructure scaling to handle predictable peak loads during business hours

About

Key Features

Automated Kubernetes HPA manifest generation
Metric-based scaling threshold configuration for CPU and memory
Production-ready YAML and infrastructure-as-code output
0 GitHub stars
Dynamic resource allocation for cloud infrastructure
Best practice implementation for fault tolerance and high availability

Use Cases

Configuring Kubernetes deployments to scale automatically based on resource utilization
Optimizing cloud expenditure through automated dynamic resource scaling
Managing infrastructure scaling to handle predictable peak loads during business hours