Agent skill · software engineering · aj-geddes
autoscaling-configuration
Configure autoscaling for Kubernetes, VMs, and serverless workloads based on metrics, schedules, and custom indicators.
Why this skill is useful
Provides specific Kubernetes and AWS autoscaling configurations and best practices that the AI wouldn't reliably generate on its own.
What it needs
Requires aws-cli, kubectl installed locally. Requires aws account access. About 1k tokens when loaded. Last updated 2026-03-04. 307 stars on the source repository.
What this skill does
Autoscaling Configuration Table of Contents Overview When to Use Quick Start Reference Guides Best Practices Overview Implement autoscaling strategies to automatically adjust resource capacity based on demand, ensuring cost efficiency while maintaining performance and availability. When to Use Traffic-driven workload scaling Time-based scheduled scaling Resource utilization optimization Cost reduction High-traffic event handling Batch processing optimization Database connection pooling Quick Start Minimal working example: Reference Guides Detailed implementations in the references/ directory: Guide Contents --- --- Kubernetes Horizontal Pod Autoscaler Kubernetes Horizontal Pod Autoscaler AWS Auto Scaling AWS Auto Scaling Custom Metrics Autoscaling Custom Metrics Autoscaling Autoscaling Script Autoscaling Script Monitoring Autoscaling Monitoring Autoscaling Best Practices ✅ DO Set appropriate min/max replicas Monitor metric aggregation window Implement cooldown periods Use multiple metrics Test scaling behavior Monitor scaling events Plan for peak loads Implement fallback strategies ❌ DON'T Set min replicas to 1 Scale too aggressively Ignore cooldown periods Use single metric only Forget to test scaling Scale below resource needs Neglect monitoring Deploy without capacity tests
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aj-geddes/autoscaling-configuration