ReddRadar
Agent skill
capacity-planning-helper
Estimates infrastructure needs based on traffic forecasts, workload analysis, and performance requirements with sizing recommendations and cost trade-offs. Use for "capacity planning", "infrastructure sizing", "resource estimation", or "scalability planning".
Install this agent skill to your Project
npx add-skill https://github.com/patricio0312rev/skills/tree/main/performance/capacity-planning-helper
SKILL.md
Capacity Planning Helper
Right-size infrastructure for current and future needs.
Traffic Forecasting
interface TrafficForecast {
current: {
dailyUsers: number;
peakRPS: number;
avgRPS: number;
};
projected: {
timeframe: "6m" | "12m" | "24m";
dailyUsers: number;
peakRPS: number;
avgRPS: number;
growthRate: number;
};
}
const forecast: TrafficForecast = {
current: {
dailyUsers: 100000,
peakRPS: 500,
avgRPS: 200,
},
projected: {
timeframe: "12m",
dailyUsers: 500000, // 5x growth
peakRPS: 2500,
avgRPS: 1000,
growthRate: 4.0, // 400% growth
},
};
Resource Estimation
interface ResourceNeeds {
compute: {
instanceType: string;
instanceCount: number;
cpu: number;
memory: number;
};
database: {
instanceType: string;
instanceCount: number;
storage: number;
iops: number;
};
cache: {
instanceType: string;
nodes: number;
memory: number;
};
}
function estimateResources(forecast: TrafficForecast): ResourceNeeds {
const { peakRPS } = forecast.projected;
// Rule of thumb: 100 RPS per instance (with headroom)
const instanceCount = Math.ceil(peakRPS / 100);
// Database: 1000 connections per 2vCPU
const dbInstances = Math.ceil((peakRPS * 2) / 1000);
return {
compute: {
instanceType: "t3.large",
instanceCount: instanceCount * 1.5, // 50% headroom
cpu: 2 * instanceCount,
memory: 8 * instanceCount,
},
database: {
instanceType: "db.r6g.xlarge",
instanceCount: dbInstances,
storage: 1000, // GB
iops: 10000,
},
cache: {
instanceType: "cache.r6g.large",
nodes: 2, // Primary + replica
memory: 12, // GB
},
};
}
Cost Estimation
interface CostEstimate {
monthly: {
compute: number;
database: number;
cache: number;
storage: number;
bandwidth: number;
total: number;
};
annual: number;
}
const pricing = {
"t3.large": 0.0832, // $/hour
"db.r6g.xlarge": 0.336,
"cache.r6g.large": 0.226,
storage: 0.1, // $/GB/month
bandwidth: 0.09, // $/GB
};
function estimateCost(
resources: ResourceNeeds,
trafficGB: number
): CostEstimate {
const hoursPerMonth = 730;
const monthly = {
compute:
resources.compute.instanceCount * pricing["t3.large"] * hoursPerMonth,
database:
resources.database.instanceCount *
pricing["db.r6g.xlarge"] *
hoursPerMonth,
cache: resources.cache.nodes * pricing["cache.r6g.large"] * hoursPerMonth,
storage: resources.database.storage * pricing.storage,
bandwidth: trafficGB * pricing.bandwidth,
total: 0,
};
monthly.total = Object.values(monthly).reduce((sum, cost) => sum + cost, 0);
return {
monthly,
annual: monthly.total * 12,
};
}
Scale Triggers
# auto-scaling-config.yml
scaling:
triggers:
- metric: cpu_utilization
threshold: 70%
action: scale_up
cooldown: 5m
- metric: cpu_utilization
threshold: 30%
action: scale_down
cooldown: 15m
- metric: request_queue_depth
threshold: 1000
action: scale_up
cooldown: 1m
limits:
min_instances: 2
max_instances: 20
schedule:
# Pre-scale for known traffic patterns
- time: "08:00"
target_instances: 10
- time: "22:00"
target_instances: 4
Cost/Performance Tradeoffs
# Infrastructure Options
## Option 1: Cost-Optimized ($2,500/mo)
- Compute: 4x t3.large
- Database: 1x db.r6g.large
- Cache: 1x cache.r6g.medium
- **Pros:** Lowest cost
- **Cons:** Limited headroom, potential latency issues
## Option 2: Balanced ($5,000/mo)
- Compute: 8x t3.large
- Database: 2x db.r6g.xlarge
- Cache: 2x cache.r6g.large
- **Pros:** Good headroom, redundancy
- **Cons:** Moderate cost
## Option 3: Performance-Optimized ($10,000/mo)
- Compute: 12x c6g.xlarge
- Database: 3x db.r6g.2xlarge
- Cache: 3x cache.r6g.xlarge
- **Pros:** Maximum performance, high availability
- **Cons:** Higher cost
## Recommendation
Start with Option 2, monitor for 1 month, adjust based on:
- Actual CPU/memory utilization
- Database query performance
- Cache hit rates
Capacity Planning Spreadsheet
| Metric | Current | 6mo Proj | 12mo Proj | Notes |
|---------------------|---------|----------|-----------|--------------------------|
| Daily Users | 100k | 250k | 500k | 5x growth expected |
| Peak RPS | 500 | 1250 | 2500 | Linear w/ users |
| DB Connections | 100 | 250 | 500 | 2 per instance |
| Storage (GB) | 100 | 300 | 1000 | User data + logs |
| Bandwidth (TB) | 1 | 3 | 10 | Images + video |
| Instance Count | 4 | 10 | 20 | Auto-scaling |
| Monthly Cost | $2k | $5k | $10k | AWS estimate |
Output Checklist
- Traffic forecast
- Resource estimates
- Cost analysis
- Scale triggers
- Performance targets
- Growth plan ENDFILE
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