Individual skill
configuring-auto-scaling-policies
Configure use when you need to work with auto-scaling. This skill provides auto-scaling configuration with comprehensive guidance and automation. Trigger with phrases like "configure auto-scaling", "set up elastic scaling", or "implement scaling".
plugins/devops/auto-scaling-configurator/skills/configuring-auto-scaling-policies/SKILL.md
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- ai Repository topic
- automation Repository topic
- claude-code Repository topic
- devops Repository topic
- mcp Repository topic
- ai-agents Repository topic
- anthropic Repository topic
- claude-code-plugins Repository topic
- developer-tools Repository topic
- llm Repository topic
- devops Declared skill tag
- scaling Declared skill tag
- auto-scaling Declared skill tag
- devops Derived category
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SKILL.mdcore · 4,215 bytesSKILL.mdcore · 4,215 bytesassets/README.mddoc · 369 bytesassets/config_template.yamlasset · 3,240 bytesassets/example_aws_scaling_policy.jsonasset · 2,169 bytesassets/example_hpa.yamlasset · 2,372 bytesreferences/README.mddoc · 68 bytesscripts/README.mddoc · 423 bytesscripts/generate_config.pyruns · 3,700 bytes
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# Configuring Auto-Scaling Policies
## Overview
Configure auto-scaling policies for cloud workloads across AWS Auto Scaling Groups, GCP Managed Instance Groups, Azure VMSS, and Kubernetes Horizontal Pod Autoscaler (HPA). Generate scaling configurations based on CPU, memory, request rate, or custom metrics with appropriate thresholds, cooldown periods, and scale-in protection.
## Prerequisites
- Cloud provider CLI installed and authenticated (`aws`, `gcloud`, or `az`)
- For Kubernetes HPA: `kubectl` configured with cluster access and metrics-server deployed
- Baseline performance data for the target workload (average CPU, memory, request rate)
- Understanding of traffic patterns (steady, bursty, scheduled)
- IAM permissions to create/modify scaling policies and CloudWatch/Stackdriver alarms
## Instructions