Capacity Planning

Forecast resource needs and design scaling strategies based on growth projections

16 stars

Best use case

Capacity Planning is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Forecast resource needs and design scaling strategies based on growth projections

Teams using Capacity Planning should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/capacity-planning/SKILL.md --create-dirs "https://raw.githubusercontent.com/diegosouzapw/awesome-omni-skill/main/skills/devops/capacity-planning/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/capacity-planning/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How Capacity Planning Compares

Feature / AgentCapacity PlanningStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Forecast resource needs and design scaling strategies based on growth projections

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# Capacity Planning Skill

Forecast resource needs and design scaling strategies based on growth projections.

## Trigger Conditions
- Traffic spikes or utilization exceeds 75% threshold
- Quarterly capacity review cycle
- User invokes with "capacity plan" or "scaling strategy"

## Input Contract
- **Required:** Current resource utilization metrics
- **Required:** Growth forecast (user acquisition, traffic patterns)
- **Optional:** Budget constraints, SLA requirements

## Output Contract
- Capacity model with projections
- Scaling recommendations (horizontal/vertical, auto/manual)
- Cost forecast per growth scenario
- Headroom analysis for failover scenarios

## Tool Permissions
- **Read:** Metrics, infrastructure configs, billing data
- **Write:** Capacity planning documents
- **Search:** Historical usage patterns

## Execution Steps
1. Collect current utilization across compute, storage, network
2. Model demand from business metrics, not just historical extrapolation
3. Define target utilization bands (60-75%)
4. Calculate headroom for failover (lose 1 of N replicas)
5. Project costs for 3, 6, 12 month horizons
6. Recommend scaling triggers and strategies
7. Document the capacity model

## Success Criteria
- Utilization targets defined per resource type
- Cost projections for multiple growth scenarios
- Scaling triggers calibrated for scale-up latency
- Failover headroom verified

## Escalation Rules
- Escalate if projected costs exceed budget by >20%
- Escalate if current utilization exceeds 85%
- Escalate if no scaling strategy can meet SLA targets

## Example Invocations

**Input:** "Plan capacity for Black Friday (expected 5x normal traffic)"

**Output:** Current: 3 pods, 45% CPU avg. For 5x: need 12 pods minimum (15 recommended for headroom). Auto-scaler trigger at 60% CPU. Pre-scale 2 hours before peak. Estimated additional cost: $2,400 for 48h burst. DB read replicas: add 2 in us-east, 1 in eu-west.

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