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k8s-pod-rightsizer

Analyze Kubernetes workload metrics to generate policy-constrained CPU and memory rightsizing recommendations.

186|10|Updated Feb 21, 2026
Search Tags:#kubernetes#prometheus#cost-optimization#rightsizing#resource-management#capacity-planning
One-click install
npx skills add https://github.com/initializ/forge --skill k8s-pod-rightsizer
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: k8s-pod-rightsizer
Source: https://github.com/initializ/forge/tree/main/forge-skills/local/embedded/k8s-pod-rightsizer
Command: npx skills add https://github.com/initializ/forge --skill k8s-pod-rightsizer

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires kubectl, jq, curl, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill helps you reduce cloud costs and improve application performance by analyzing Kubernetes workload resource usage and providing precise recommendations for CPU and memory requests and limits.

Core Features & Use Cases

  • Analyze Resource Usage: Collects metrics from Prometheus or the metrics-server to understand actual pod resource consumption.
  • Generate Rightsizing Recommendations: Computes optimal CPU and memory requests/limits based on collected metrics and configurable policies.
  • Patch Generation & Application: Can generate strategic merge patches for applying changes and includes rollback capabilities for safety.
  • Use Case: Identify and fix over-provisioned deployments in your production environment to save on infrastructure costs, ensuring applications have sufficient resources without waste.

Quick Start

Analyze the resource usage for all workloads in the 'staging' namespace and report recommendations.

Frequently Asked Questions about k8s-pod-rightsizer

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

FAQPage Schema
How do I optimize Kubernetes resource requests and limits to reduce cloud costs?▼

To optimize Kubernetes resource requests, this Skill analyzes workload metrics from Prometheus or metrics-server to compute policy-constrained CPU and memory rightsizing recommendations, generating strategic merge patches to safely apply changes and reduce waste.

Can I use Prometheus metrics for Kubernetes capacity planning and rightsizing?▼

Yes, Kubernetes capacity planning and rightsizing supports Prometheus metrics. The Skill collects actual pod resource consumption data from Prometheus or metrics-server to calculate optimal CPU and memory requests and limits based on your configured policies.

How do I generate and apply patches for Kubernetes resource rightsizing?▼

Kubernetes resource rightsizing generates strategic merge patches for applying CPU and memory adjustments. The Skill includes dry-run analysis, safe application of changes, and rollback capabilities to ensure safe patch generation and deployment without service disruption.

Do I need kubectl and jq installed to analyze Kubernetes workload resource usage?▼

Yes, you need kubectl, jq, and curl installed to analyze Kubernetes workload resource usage. These dependencies are required to execute the scripts that collect metrics, compute recommendations, and generate the strategic merge patches for resource rightsizing.

What is the best way to fix over-provisioned deployments in a Kubernetes production environment?▼

The best way to fix over-provisioned Kubernetes deployments is using policy-constrained rightsizing analysis. This Skill computes optimal CPU and memory requests based on actual workload metrics, ensuring applications have sufficient resources while eliminating infrastructure waste.

Can I perform a dry-run analysis before applying Kubernetes resource rightsizing changes?▼

Yes, you can perform a dry-run analysis before applying Kubernetes resource rightsizing changes. The Skill supports dry-run mode to review CPU and memory recommendations, generate strategic merge patches, and verify policy constraints prior to safe application with rollback capabilities.

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