Replit observability
Skill jeremylongshore/claude-code-plugins-plus-skills/skills/.curated/replit-observability
'Monitor Replit deployments with health checks, uptime tracking, resource usage, and alerting.From its SKILL.md
npx -y skills add jeremylongshore/claude-code-plugins-plus-skills --skill replit-observabilityAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
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SKILL.md
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Replit Observability
Overview
Monitor Replit deployment health, track cold starts, measure resource usage, and set up alerting. Covers Replit's built-in monitoring, external health checking, structured logging, and integration with monitoring services.
Prerequisites
- Replit app deployed (Autoscale or Reserved VM)
- Health endpoint implemented (
/health) - External monitoring service (UptimeRobot, Better Stack, or Prometheus)
Instructions
Step 1: Health Endpoint with Detailed Metrics
// src/routes/health.ts — comprehensive health check
import { Router } from 'express';
import { pool } from '../services/postgres';
const router = Router();
const startTime = Date.now();
router.get('/health', async (req, res) => {
const checks: Record<string, any> = {
status: 'ok',
uptime: process.uptime(),
bootTime: ((Date.now() - startTime) / 1000).toFixed(1) + 's ago',
timestamp: new Date().toISOString(),
repl: process.env.REPL_SLUG,
region: process.env.REPLIT_DEPLOYMENT_REGION,
env: process.env.NODE_ENV,
};
// Database check
if (process.env.DATABASE_URL) {
const dbStart = Date.now();
try {
await pool.query('SELECT 1');
checks.database = {
status: 'connected',
latencyMs: Date.now() - dbStart,
pool: { total: pool.totalCount, idle: pool.idleCount },
};
} catch (err: any) {
checks.database = { status: 'disconnected', error: err.message };
checks.status = 'degraded';
}
}
// Memory metrics
const mem = process.memoryUsage();
checks.memory = {
heapMB: Math.round(mem.heapUsed / 1024 / 1024),
totalMB: Math.round(mem.heapTotal / 1024 / 1024),
rssMB: Math.round(mem.rss / 1024 / 1024),
percent: ((mem.heapUsed / mem.heapTotal) * 100).toFixed(1),
};
// Node.js info
checks.runtime = {
node: process.version,
platform: process.platform,
pid: process.pid,
};
res.status(checks.status === 'ok' ? 200 : 503).json(checks);
});
// Lightweight ping for uptime monitors
router.get('/ping', (req, res) => res.send('pong'));
export default router;
Step 2: Structured Logging
// src/utils/logger.ts — structured JSON logging
const IS_PROD = process.env.NODE_ENV === 'production';
type LogLevel = 'debug' | 'info' | 'warn' | 'error';
function log(level: LogLevel, message: string, data?: Record<string, any>) {
if (level === 'debug' && IS_PROD) return;
const entry = {
timestamp: new Date().toISOString(),
level,
message,
repl: process.env.REPL_SLUG,
...data,
};
// JSON format for machine parsing, human-readable in dev
if (IS_PROD) {
consolelevel === 'error' ? 'error' : 'log');
} else {
consolelevel === 'error' ? 'error' : 'log'}] ${message}`,
data || ''
);
}
}
export const logger = {
debug: (msg: string, data?: any) => log('debug', msg, data),
info: (msg: string, data?: any) => log('info', msg, data),
warn: (msg: string, data?: any) => log('warn', msg, data),
error: (msg: string, data?: any) => log('error', msg, data),
};
// Request logging middleware
export function requestLogger(req: any, res: any, next: any) {
const start = Date.now();
res.on('finish', () => {
logger.info('request', {
method: req.method,
path: req.path,
status: res.statusCode,
durationMs: Date.now() - start,
userId: req.headers['x-replit-user-id'] || 'anonymous',
});
});
next();
}
Step 3: External Uptime Monitoring
Set up external monitors to detect Autoscale cold starts and outages:
UptimeRobot (free tier: 50 monitors):
1. Create new monitor: HTTP(s)
2. URL: https://your-app.replit.app/ping
3. Interval: 5 minutes
4. Alert contacts: email, Slack webhook
Better Stack / Datadog / Grafana Cloud:
- Same setup, more features
- Track response time trends
- Detect cold start patterns
- Set up PagerDuty integration
Key metrics to monitor externally:
- Uptime percentage (target: 99.9%)
- Response time P95 (target: < 2s)
- Cold start frequency (Autoscale only)
- SSL certificate expiry
Step 4: Cold Start Detection
// Track cold starts for Autoscale deployments
const COLD_START_THRESHOLD_MS = 5000;
let firstRequestTime: number | null = null;
app.use((req, res, next) => {
if (!firstRequestTime) {
firstRequestTime = Date.now();
const bootTime = process.uptime();
if (bootTime < 30) { // Just started
logger.info('cold_start_detected', {
bootTimeMs: Math.round(bootTime * 1000),
path: req.path,
});
}
}
next();
});
Step 5: Alerting Rules
// src/utils/alerts.ts — send alerts to Slack on issues
async function alertSlack(message: string, severity: 'info' | 'warning' | 'critical') {
const webhookUrl = process.env.SLACK_WEBHOOK_URL;
if (!webhookUrl) return;
const emoji = { info: 'information_source', warning: 'warning', critical: 'rotating_light' };
await fetch(webhookUrl, {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify({
text: `:${emoji[severity]}: [${severity.toUpperCase()}] ${process.env.REPL_SLUG}\n${message}`,
}),
});
}
// Monitor memory usage
setInterval(async () => {
const mem = process.memoryUsage();
const heapPercent = (mem.heapUsed / mem.heapTotal) * 100;
if (heapPercent > 90) {
await alertSlack(`Memory critical: ${heapPercent.toFixed(1)}% heap used`, 'critical');
} else if (heapPercent > 75) {
await alertSlack(`Memory warning: ${heapPercent.toFixed(1)}% heap used`, 'warning');
}
}, 60000);
// Monitor error rate
let errorCount = 0;
let requestCount = 0;
app.use((req, res, next) => {
requestCount++;
res.on('finish', () => {
if (res.statusCode >= 500) errorCount++;
});
next();
});
setInterval(async () => {
if (requestCount > 0) {
const errorRate = (errorCount / requestCount) * 100;
if (errorRate > 5) {
await alertSlack(`Error rate: ${errorRate.toFixed(1)}% (${errorCount}/${requestCount})`, 'critical');
}
}
errorCount = 0;
requestCount = 0;
}, 300000); // Check every 5 minutes
Step 6: Replit Dashboard Monitoring
Built-in monitoring in Replit:
1. Deployment Settings > Logs: real-time stdout/stderr
2. Deployment Settings > History: deploy timeline + rollbacks
3. Database pane > Settings: storage usage + connection info
4. Billing > Usage: compute, egress, and storage costs
Check deployment logs:
- Click on active deployment
- View real-time log stream
- Filter by error/warning
- Logs persist across container restarts
Error Handling
| Issue | Cause | Solution |
|---|---|---|
| Cold starts undetected | No external monitor | Set up UptimeRobot or similar |
| Deployment logs missing | Container restarted | Use external log aggregator |
| Memory leak unnoticed | No memory monitoring | Add heap tracking + alerts |
| DB pool exhaustion | Too many connections | Monitor pool.totalCount in health |
Resources
Next Steps
For incident response, see replit-incident-runbook.
What ships with it
Read from the repository
Just SKILL.md. No reference files, no scripts.
Gives 2 of the 12 instructions most monitoring observability skills give in ~1.9k tokens
Counted across 530 of the 532 authors here whose files we hold, read 2026-09-06
- Use structured JSON logginghere, and in 40 of 530, across 36 files
- Link every alert to a runbookin 29 of 530, across 27 files
- Attach correlation IDs to every log linein 19 of 530, across 16 files
- Alert on symptoms rather than causesin 19 of 530, across 17 files
- Use OpenTelemetry for distributed tracingin 15 of 530, across 14 files
- Alert on symptoms users feelin 15 of 530, across 13 files
- Implement health check endpointshere, and in 14 of 530, across 10 files
- Inspect existing dashboards firstin 12 of 530, across 4 files
- Build the minimum useful boardin 12 of 530, across 4 files
- Start from operator questionsin 12 of 530, across 4 files
- Propagate trace context across boundariesin 11 of 530, across 10 files
- Include trace id in all log entriesin 10 of 530, across 9 files
Said here and by no other author read
- Track cold starts for Autoscale deployments
Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.