home / skills / jeremylongshore / claude-code-plugins-plus-skills / replit-load-scale
This skill helps you plan and execute Replit load tests, configure auto-scaling, and optimize capacity using k6, HPA, and metrics.
npx playbooks add skill jeremylongshore/claude-code-plugins-plus-skills --skill replit-load-scaleReview the files below or copy the command above to add this skill to your agents.
---
name: replit-load-scale
description: |
Implement Replit load testing, auto-scaling, and capacity planning strategies.
Use when running performance tests, configuring horizontal scaling,
or planning capacity for Replit integrations.
Trigger with phrases like "replit load test", "replit scale",
"replit performance test", "replit capacity", "replit k6", "replit benchmark".
allowed-tools: Read, Write, Edit, Bash(k6:*), Bash(kubectl:*)
version: 1.0.0
license: MIT
author: Jeremy Longshore <[email protected]>
---
# Replit Load & Scale
## Overview
Load testing, scaling strategies, and capacity planning for Replit integrations.
## Prerequisites
- k6 load testing tool installed
- Kubernetes cluster with HPA configured
- Prometheus for metrics collection
- Test environment API keys
## Load Testing with k6
### Basic Load Test
```javascript
// replit-load-test.js
import http from 'k6/http';
import { check, sleep } from 'k6';
export const options = {
stages: [
{ duration: '2m', target: 10 }, // Ramp up
{ duration: '5m', target: 10 }, // Steady state
{ duration: '2m', target: 50 }, // Ramp to peak
{ duration: '5m', target: 50 }, // Stress test
{ duration: '2m', target: 0 }, // Ramp down
],
thresholds: {
http_req_duration: ['p(95)<500'],
http_req_failed: ['rate<0.01'],
},
};
export default function () {
const response = http.post(
'https://api.replit.com/v1/resource',
JSON.stringify({ test: true }),
{
headers: {
'Content-Type': 'application/json',
'Authorization': `Bearer ${__ENV.REPLIT_API_KEY}`,
},
}
);
check(response, {
'status is 200': (r) => r.status === 200,
'latency < 500ms': (r) => r.timings.duration < 500,
});
sleep(1);
}
```
### Run Load Test
```bash
# Install k6
brew install k6 # macOS
# or: sudo apt install k6 # Linux
# Run test
k6 run --env REPLIT_API_KEY=${REPLIT_API_KEY} replit-load-test.js
# Run with output to InfluxDB
k6 run --out influxdb=http://localhost:8086/k6 replit-load-test.js
```
## Scaling Patterns
### Horizontal Scaling
```yaml
# kubernetes HPA
apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
name: replit-integration-hpa
spec:
scaleTargetRef:
apiVersion: apps/v1
kind: Deployment
name: replit-integration
minReplicas: 2
maxReplicas: 20
metrics:
- type: Resource
resource:
name: cpu
target:
type: Utilization
averageUtilization: 70
- type: Pods
pods:
metric:
name: replit_queue_depth
target:
type: AverageValue
averageValue: 100
```
### Connection Pooling
```typescript
import { Pool } from 'generic-pool';
const replitPool = Pool.create({
create: async () => {
return new ReplitClient({
apiKey: process.env.REPLIT_API_KEY!,
});
},
destroy: async (client) => {
await client.close();
},
max: 20,
min: 5,
idleTimeoutMillis: 30000,
});
async function withReplitClient<T>(
fn: (client: ReplitClient) => Promise<T>
): Promise<T> {
const client = await replitPool.acquire();
try {
return await fn(client);
} finally {
replitPool.release(client);
}
}
```
## Capacity Planning
### Metrics to Monitor
| Metric | Warning | Critical |
|--------|---------|----------|
| CPU Utilization | > 70% | > 85% |
| Memory Usage | > 75% | > 90% |
| Request Queue Depth | > 100 | > 500 |
| Error Rate | > 1% | > 5% |
| P95 Latency | > 1000ms | > 3000ms |
### Capacity Calculation
```typescript
interface CapacityEstimate {
currentRPS: number;
maxRPS: number;
headroom: number;
scaleRecommendation: string;
}
function estimateReplitCapacity(
metrics: SystemMetrics
): CapacityEstimate {
const currentRPS = metrics.requestsPerSecond;
const avgLatency = metrics.p50Latency;
const cpuUtilization = metrics.cpuPercent;
// Estimate max RPS based on current performance
const maxRPS = currentRPS / (cpuUtilization / 100) * 0.7; // 70% target
const headroom = ((maxRPS - currentRPS) / currentRPS) * 100;
return {
currentRPS,
maxRPS: Math.floor(maxRPS),
headroom: Math.round(headroom),
scaleRecommendation: headroom < 30
? 'Scale up soon'
: headroom < 50
? 'Monitor closely'
: 'Adequate capacity',
};
}
```
## Benchmark Results Template
```markdown
## Replit Performance Benchmark
**Date:** YYYY-MM-DD
**Environment:** [staging/production]
**SDK Version:** X.Y.Z
### Test Configuration
- Duration: 10 minutes
- Ramp: 10 → 100 → 10 VUs
- Target endpoint: /v1/resource
### Results
| Metric | Value |
|--------|-------|
| Total Requests | 50,000 |
| Success Rate | 99.9% |
| P50 Latency | 120ms |
| P95 Latency | 350ms |
| P99 Latency | 800ms |
| Max RPS Achieved | 150 |
### Observations
- [Key finding 1]
- [Key finding 2]
### Recommendations
- [Scaling recommendation]
```
## Instructions
### Step 1: Create Load Test Script
Write k6 test script with appropriate thresholds.
### Step 2: Configure Auto-Scaling
Set up HPA with CPU and custom metrics.
### Step 3: Run Load Test
Execute test and collect metrics.
### Step 4: Analyze and Document
Record results in benchmark template.
## Output
- Load test script created
- HPA configured
- Benchmark results documented
- Capacity recommendations defined
## Error Handling
| Issue | Cause | Solution |
|-------|-------|----------|
| k6 timeout | Rate limited | Reduce RPS |
| HPA not scaling | Wrong metrics | Verify metric name |
| Connection refused | Pool exhausted | Increase pool size |
| Inconsistent results | Warm-up needed | Add ramp-up phase |
## Examples
### Quick k6 Test
```bash
k6 run --vus 10 --duration 30s replit-load-test.js
```
### Check Current Capacity
```typescript
const metrics = await getSystemMetrics();
const capacity = estimateReplitCapacity(metrics);
console.log('Headroom:', capacity.headroom + '%');
console.log('Recommendation:', capacity.scaleRecommendation);
```
### Scale HPA Manually
```bash
kubectl scale deployment replit-integration --replicas=5
kubectl get hpa replit-integration-hpa
```
## Resources
- [k6 Documentation](https://k6.io/docs/)
- [Kubernetes HPA](https://kubernetes.io/docs/tasks/run-application/horizontal-pod-autoscale/)
- [Replit Rate Limits](https://docs.replit.com/rate-limits)
## Next Steps
For reliability patterns, see `replit-reliability-patterns`.This skill implements Replit-specific load testing, auto-scaling, and capacity planning practices to validate performance and keep integrations available under load. It provides k6 test scripts, Kubernetes HPA patterns, connection pooling examples, and a capacity-estimation approach. Use it to run benchmarks, verify autoscaling, and produce actionable capacity recommendations.
It generates k6 load tests to simulate realistic traffic and exports metrics to time-series backends. It recommends HPA configuration that uses CPU and custom metrics (like queue depth) and shows connection-pooling to reduce client overhead. It also provides a simple capacity-estimate algorithm and a benchmark template to document results and recommendations.
What k6 thresholds should I set for Replit integrations?
Start with p(95)<500ms and error rate <1%, then adjust based on historical performance and SLAs.
How do I choose HPA metric targets?
Use CPU target around 70% and set custom metric targets (like average queue depth) based on observed warning/critical levels; tune after observing real traffic.