capacity-planning

Forecast system load, translate demand into resource units, and apply safety factors.

Updated Jun 5, 2026

Search Tags:#resource management #load testing #system reliability #capacity planning #load forecasting

Ready to run

npx skills add https://github.com/hung-phan/system-skills --skill capacity-planning-hung-phan

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: capacity-planning
Source: https://github.com/hung-phan/system-skills/tree/main/skills/system-review/references/reliability/capacity-planning
Command: npx skills add https://github.com/hung-phan/system-skills --skill capacity-planning-hung-phan

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill helps you forecast system load, size fleets with safety margins, and load-test before traffic arrives to prevent outages and ensure system reliability.

Core Features & Use Cases

Load Forecasting: Predict demand for resources based on historical data and growth curves.
Resource Unit Translation: Convert demand into resource units like CPU, RPS, IOPS, connections, and IP addresses.
Safety Factors and Headroom Math: Apply safety factors for variance and failure modes to ensure system stability.
Load Testing: Verify capacity planning with actual load tests against the system.
Use Case: Plan for a major product launch by forecasting peak load, sizing the fleet, and conducting load tests to ensure the system can handle the expected traffic without outages.

Quick Start

Use the capacity-planning skill to forecast and size your system's fleet for a major product launch.

Frequently Asked Questions about capacity-planning

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

FAQPage Schema

How do I forecast system load to prevent outages during a product launch?▼

System load forecasting predicts resource demand using historical data and growth curves to prevent outages. You translate this demand into resource units like CPU, RPS, and IOPS, then apply safety factors to size your fleet for peak traffic stability.

What is the best way to calculate safety factors and headroom for system capacity?▼

Calculating safety factors for system capacity involves applying headroom math to account for traffic variance and failure modes. This ensures system reliability by maintaining buffer resources beyond the forecasted peak load, preventing cascading failures during unexpected spikes.

How do I translate forecasted demand into resource units like CPU and IOPS?▼

Resource unit translation converts forecasted demand into specific metrics like CPU, RPS, IOPS, connections, and IP addresses. This process maps raw load predictions to concrete infrastructure requirements needed for accurate fleet sizing and capacity planning.

Does capacity planning work without integrating dedicated load testing tools?▼

Capacity planning requires load testing to verify fleet sizing against actual traffic. Without load testing tools, you cannot validate whether your safety factors and resource unit translations will handle peak demand, leaving system reliability unverified before production traffic arrives.

Do I need historical system metrics to predict peak traffic and size my fleet?▼

Historical system metrics and growth curves are required to predict peak traffic accurately. Without baseline data on past resource usage, load forecasting becomes guesswork, making it impossible to determine appropriate safety factors or translate demand into accurate resource units.

Why does my system crash under peak traffic despite having spare capacity?▼

Systems crash under peak traffic when capacity planning lacks proper safety factors for failure modes. Insufficient headroom math or inaccurate load forecasting causes cascading failures, meaning spare capacity exists but cannot handle variance, requiring load testing to verify actual limits.