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-scale

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---
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`.

Overview

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.

How this skill works

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.

When to use it

  • Before shipping or releasing a Replit integration to production
  • When validating horizontal scaling and autoscaler behavior
  • During incident postmortems to reproduce load-related failures
  • When planning capacity or budgeting for expected traffic
  • To benchmark API changes or SDK upgrades

Best practices

  • Include ramp-up and ramp-down phases in k6 to avoid false positives from cold starts
  • Export k6 metrics to InfluxDB/Prometheus for long-term analysis and dashboarding
  • Use HPA with both CPU and custom metrics (e.g., request queue depth) to capture varied load patterns
  • Employ a connection pool for Replit clients to limit concurrent connections and reduce latency
  • Document each run with the benchmark template and capture environment, SDK version, and thresholds

Example use cases

  • Run a k6 stress test to validate P95 latency under peak traffic and confirm HPA scales as expected
  • Estimate headroom from current metrics and produce a scale recommendation for capacity planning
  • Configure a Kubernetes HPA that scales from 2 to 20 replicas based on CPU and queue-depth metrics
  • Use connection pooling to stabilize latency when many concurrent requests target Replit APIs
  • Automate a CI job that runs a short k6 smoke test on every release and stores results in InfluxDB

FAQ

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.