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jorgealves
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k8s-resource-optimizer

Analyze Kubernetes usage metrics to generate right-sizing YAML patches.

2|1|Updated Jan 25, 2026
Search Tags:#kubernetes#cost-optimization#analyze#resource-optimization#oom#right-sizing#yaml-patch
One-click install
npx skills add https://github.com/jorgealves/agent_skills --skill k8s-resource-optimizer
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: k8s-resource-optimizer
Source: https://github.com/jorgealves/agent_skills/tree/main/k8s-resource-optimizer
Command: npx skills add https://github.com/jorgealves/agent_skills --skill k8s-resource-optimizer

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps teams optimize Kubernetes resource configurations by analyzing usage metrics and historical data to suggest appropriate CPU and memory requests and limits. The goal is to reduce cloud costs, prevent OOMKills, and improve cluster reliability.

Core Features & Use Cases

  • Resource right-sizing: Identify containers that are undersized or oversized and provide validated recommendations.
  • Historical data analysis: Correlate usage with throttling or outages to improve stability.
  • Automated guidance: Generate patch-ready YAML or manifests to apply the suggested changes.

Quick Start

Use the k8s-resource-optimizer to evaluate a deployment manifest with accompanying metrics and produce a set of container-specific recommendations. Provide a sample manifest path and a metrics input to illustrate.

Frequently Asked Questions about k8s-resource-optimizer

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I right-size Kubernetes resources to cut costs?▼

Right-size Kubernetes resources by analyzing usage metrics and historical data to propose optimal CPU and memory requests and limits. This identifies oversized or underutilized containers, reducing cloud costs while preventing OOMKills.

Why does my Kubernetes pod keep getting OOMKilled?▼

Pods get OOMKilled when memory limits are set too low for actual usage. Analyze historical metrics and correlate them with outages to adjust memory requests and limits, improving cluster stability and preventing future OOMKills.

Can I automatically generate YAML patches for Kubernetes resource optimization?▼

Automatically generate patch-ready YAML manifests for Kubernetes resource optimization. The analysis produces actionable, container-specific right-sizing recommendations that can be directly applied to your deployment configurations.

Does Kubernetes resource optimization prioritize cost savings over stability?▼

Kubernetes resource optimization prioritizes stability over aggressive cost-cutting. It correlates historical usage data with throttling or outages to ensure recommended CPU and memory adjustments improve reliability without risking performance.

When do I need to analyze Kubernetes resource usage metrics?▼

Analyze Kubernetes resource usage metrics during cloud cluster evaluations, production incidents, and pre-deployment reviews. This identifies underutilized or oversized resources to validate right-sizing recommendations before applying changes.

What is the best way to prevent Kubernetes CPU throttling?▼

Prevent Kubernetes CPU throttling by correlating historical usage data with throttling incidents. Analyzing these metrics allows you to propose validated CPU requests and limits that improve cluster reliability and performance.

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