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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

Install
npx -y skills add jeremylongshore/claude-code-plugins-plus-skills --skill replit-observability

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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

IssueCauseSolution
Cold starts undetectedNo external monitorSet up UptimeRobot or similar
Deployment logs missingContainer restartedUse external log aggregator
Memory leak unnoticedNo memory monitoringAdd heap tracking + alerts
DB pool exhaustionToo many connectionsMonitor 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.

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