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