CapacityPlanning

Forecast future CPU, memory, and storage needs from historical usage data.

Updated Aug 27, 2026
One-click install
npx skills add https://github.com/cavanli/aiops --skill capacityplanning
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: CapacityPlanning
Source: https://github.com/cavanli/aiops/tree/main/skills/capacity-planning
Command: npx skills add https://github.com/cavanli/aiops --skill capacityplanning

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

容量规划工具帮助团队基于历史数据预测未来资源需求,优化扩容决策,避免资源浪费或服务瓶颈。

Core Features & Use Cases

  • 资源需求预测:覆盖 CPU、内存、存储等关键资源,输出未来趋势。
  • 趋势与季节性分析:识别日/周/月周期对容量的影响,帮助制定应对策略。
  • 自动扩容建议:给出头部空间、最小/最大节点数等具体扩缩容建议,适用于云环境与混合环境。
  • Use case: 针对一个 Web 服务在未来 90 天生成扩容计划并提出可执行的节点调整方案。

Quick Start

输入历史资源使用数据并请求对目标服务的 90 天容量预测和扩展建议。

Frequently Asked Questions about CapacityPlanning

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

FAQPage Schema
How do I forecast future resource needs for cloud-native and on-prem services?

Forecast future resource needs by ingesting historical usage data to predict CPU, memory, and storage requirements. This approach analyzes trend and seasonal patterns to output actionable scaling recommendations for cloud-native, on-prem, and hybrid deployments.

What is the best way to plan capacity for seasonal workload fluctuations?

Plan capacity for seasonal workload fluctuations by analyzing historical metrics to identify daily, weekly, and monthly cycles. This seasonal analysis helps formulate specific scaling strategies to handle predictable peaks and troughs in resource demand.

How do I generate an auto-scaling plan for a web service over the next 90 days?

Generate a 90-day auto-scaling plan by inputting historical resource usage data to request future capacity predictions. The output provides executable node adjustment strategies, including headroom, minimum, and maximum node counts for the target web service.

Does capacity planning work for hybrid deployments without external dependencies?

Capacity planning works for hybrid deployments without external dependencies. It directly ingests historical metrics to perform trend and seasonality forecasting, outputting scaling recommendations for CPU, memory, and storage across cloud and on-prem environments.

How does trend analysis help optimize scaling decisions and prevent resource waste?

Trend analysis optimizes scaling decisions by predicting future resource requirements from historical usage data. This prevents resource waste and service bottlenecks by generating precise headroom and node adjustment recommendations based on identified long-term patterns.