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X-McKay
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get-capacity-recommendations

Analyze Kubernetes cluster capacity and forecast future resource needs.

3|Updated Dec 2, 2025
Search Tags:#analytics#kubernetes#resource optimization#forecasting#scaling#capacity planning
One-click install
npx skills add https://github.com/X-McKay/kubani --skill get-capacity-recommendations
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: get-capacity-recommendations
Source: https://github.com/X-McKay/kubani/tree/main/kubani/skills/general/analytics/get-capacity-recommendations
Command: npx skills add https://github.com/X-McKay/kubani --skill get-capacity-recommendations

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill addresses the challenge of efficiently managing cloud or on-premise infrastructure by providing data-driven insights into resource utilization and forecasting future needs.

Core Features & Use Cases

  • Resource Analysis: Gathers current CPU, memory, storage, and pod usage across cluster nodes.
  • Predictive Forecasting: Projects future resource demands based on historical growth trends.
  • Capacity Planning: Identifies potential resource shortages and suggests proactive scaling actions.
  • Node Balancing: Detects and recommends solutions for uneven workload distribution across nodes.
  • Use Case: A DevOps engineer can use this skill to proactively identify if the cluster will run out of memory in the next 30 days and get a recommendation to scale up memory resources before an outage occurs.

Quick Start

Analyze the current cluster capacity and provide scaling recommendations for the next 90 days.

Frequently Asked Questions about get-capacity-recommendations

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

FAQPage Schema
How do I forecast Kubernetes cluster capacity and prevent resource shortages?▼

Kubernetes capacity planning involves collecting current CPU, memory, and pod usage data to calculate totals. By analyzing historical growth trends, you can forecast future demands and receive actionable recommendations to scale resources proactively before shortages occur.

What is the best way to identify workload imbalances across Kubernetes nodes?▼

To identify Kubernetes node imbalances, analyze current CPU, memory, and pod distribution across all nodes. This calculates resource totals and detects uneven workloads, generating actionable rebalancing recommendations to optimize cluster utilization.

Can I use historical metrics to predict future Kubernetes scaling needs?▼

Yes, predicting Kubernetes scaling needs uses historical metrics to project future resource demands. By analyzing historical usage data and growth trends against predefined thresholds, the system forecasts upcoming requirements and generates proactive resource scaling recommendations.

Do I need cluster metrics access for Kubernetes resource optimization analysis?▼

Yes, Kubernetes resource optimization requires cluster metrics access. Accurate capacity forecasting and node balancing analysis depend on collecting current CPU, memory, and storage usage data alongside historical usage records to generate actionable scaling recommendations.

How do I analyze current CPU and memory usage for Kubernetes capacity planning?▼

Kubernetes capacity planning analyzes current CPU and memory usage by gathering resource data across all cluster nodes. This calculates total available capacity, identifies current utilization rates against predefined thresholds, and establishes the baseline for forecasting future resource demands.

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