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Terminal.skills
Use Cases/Build an Infrastructure Health Monitor

Build an Infrastructure Health Monitor

Build an infrastructure health monitoring system with service checks, metric collection, anomaly detection, alerting rules, status pages, and incident tracking.

#redis#caching#database#pub-sub#queues
Works with:claude-codeopenai-codexgemini-clicursor

Skills stack · 5 skills

Avg quality 93/100·All SAFE
>

typescript

v

Not yet scored
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>

redis

v1.0.0

Build applications with Redis — caching, session storage, pub/sub, streams, rate limiting, leaderboards, and queues. Use when tasks involve in-memory data storage, real-time messaging, distributed locking, or performance optimization with caching layers.

93/100 quality
1.81× impact
SAFE
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postgresql

v1.0.0

Assists with designing schemas, writing performant queries, managing indexes, and operating PostgreSQL databases. Use when working with JSONB, full-text search, window functions, CTEs, row-level security, replication, or performance tuning. Trigger words: postgresql, postgres, sql, database, jsonb, rls, window functions, cte.

87/100 quality
1.53× impact
SAFE
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hono

v1.0.0

You are an expert in Hono, the ultrafast web framework for the edge. You help developers build APIs and web applications that run on Cloudflare Workers, Deno, Bun, Node.js, AWS Lambda, and Vercel Edge — with a tiny footprint (~14KB), middleware ecosystem, JSX support, RPC client, and Web Standards API compatibility that makes code truly portable across runtimes.

93/100 quality
3.00× impact
SAFE
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zod

v1.0.0

You are an expert in Zod, the TypeScript-first schema declaration and validation library. You help developers define schemas that validate data at runtime AND infer TypeScript types at compile time — eliminating the need to write types and validators separately. Used for API input validation, form validation, environment variables, config files, and any data boundary.

100/100 quality
1.21× impact
SAFE
View skill
$

The Problem

Clara leads SRE at a 25-person company running 20 services across 3 regions. They use Datadog ($2,000/month) but it drowns them in metrics — 5,000 data points/second with no clear signal. When the database went down at 3 AM, nobody noticed until customers complained 45 minutes later. Alert fatigue from false positives means real alerts get ignored. They need focused monitoring: health checks that matter, anomaly detection that's smart, alerts that are actionable, and a status page customers can check.

Step 1: Build the Health Monitor

typescript
// src/monitoring/health.ts — Infrastructure health monitoring with anomaly detection
import { pool } from "../db";
import { Redis } from "ioredis";
import { randomBytes } from "node:crypto";

const redis = new Redis(process.env.REDIS_URL!);

interface ServiceCheck {
  id: string;
  name: string;
  type: "http" | "tcp" | "dns" | "database" | "custom";
  target: string;
  interval: number;          // seconds between checks
  timeout: number;           // max response time in ms
  regions: string[];         // check from multiple regions
  expectedStatus?: number;   // for HTTP checks
  expectedBody?: string;     // substring match for HTTP
  alertAfterFailures: number; // consecutive failures before alerting
}

interface CheckResult {
  checkId: string;
  region: string;
  status: "healthy" | "degraded" | "down";
  responseTimeMs: number;
  statusCode?: number;
  error?: string;
  timestamp: string;
}

interface Alert {
  id: string;
  checkId: string;
  severity: "info" | "warning" | "critical";
  message: string;
  status: "firing" | "resolved";
  firedAt: string;
  resolvedAt: string | null;
  acknowledgedBy: string | null;
  channels: string[];        // slack, email, pagerduty
}

// Execute a health check
export async function executeCheck(check: ServiceCheck, region: string): Promise<CheckResult> {
  const start = Date.now();
  let status: CheckResult["status"] = "healthy";
  let error: string | undefined;
  let statusCode: number | undefined;

  try {
    switch (check.type) {
      case "http": {
        const controller = new AbortController();
        const timeout = setTimeout(() => controller.abort(), check.timeout);
        const response = await fetch(check.target, { signal: controller.signal });
        clearTimeout(timeout);
        statusCode = response.status;

        if (check.expectedStatus && response.status !== check.expectedStatus) {
          status = "degraded";
          error = `Expected ${check.expectedStatus}, got ${response.status}`;
        }

        if (check.expectedBody) {
          const body = await response.text();
          if (!body.includes(check.expectedBody)) {
            status = "degraded";
            error = `Expected body to contain "${check.expectedBody}"`;
          }
        }
        break;
      }

      case "tcp": {
        const [host, port] = check.target.split(":");
        await new Promise<void>((resolve, reject) => {
          const net = require("net");
          const socket = net.connect(parseInt(port), host);
          socket.setTimeout(check.timeout);
          socket.on("connect", () => { socket.destroy(); resolve(); });
          socket.on("error", reject);
          socket.on("timeout", () => reject(new Error("TCP timeout")));
        });
        break;
      }

      case "database": {
        await pool.query("SELECT 1");
        break;
      }
    }
  } catch (err: any) {
    status = "down";
    error = err.message;
  }

  const responseTimeMs = Date.now() - start;

  // Degraded if response time > 80% of timeout
  if (status === "healthy" && responseTimeMs > check.timeout * 0.8) {
    status = "degraded";
  }

  const result: CheckResult = {
    checkId: check.id, region, status, responseTimeMs, statusCode, error,
    timestamp: new Date().toISOString(),
  };

  // Store result
  await redis.lpush(`health:results:${check.id}`, JSON.stringify(result));
  await redis.ltrim(`health:results:${check.id}`, 0, 999);  // keep last 1000

  // Track consecutive failures
  if (status === "down") {
    const failures = await redis.incr(`health:failures:${check.id}`);
    if (failures >= check.alertAfterFailures) {
      await createAlert(check, result, failures);
    }
  } else {
    const prevFailures = await redis.get(`health:failures:${check.id}`);
    await redis.set(`health:failures:${check.id}`, 0);
    if (prevFailures && parseInt(prevFailures) >= check.alertAfterFailures) {
      await resolveAlert(check.id);
    }
  }

  // Anomaly detection on response time
  await detectAnomaly(check.id, responseTimeMs);

  return result;
}

// Anomaly detection using simple statistical method
async function detectAnomaly(checkId: string, responseTimeMs: number): Promise<void> {
  const key = `health:latency:${checkId}`;
  await redis.rpush(key, String(responseTimeMs));
  await redis.ltrim(key, -100, -1);  // keep last 100 readings

  const values = (await redis.lrange(key, 0, -1)).map(Number);
  if (values.length < 20) return;  // not enough data

  const mean = values.reduce((a, b) => a + b, 0) / values.length;
  const stdDev = Math.sqrt(values.reduce((sum, v) => sum + Math.pow(v - mean, 2), 0) / values.length);

  // Alert if current value is more than 3 standard deviations from mean
  if (responseTimeMs > mean + 3 * stdDev) {
    await redis.publish("health:anomaly", JSON.stringify({
      checkId, responseTimeMs, mean: Math.round(mean), stdDev: Math.round(stdDev),
      message: `Latency spike: ${responseTimeMs}ms (avg: ${Math.round(mean)}ms)`,
    }));
  }
}

async function createAlert(check: ServiceCheck, result: CheckResult, failures: number): Promise<void> {
  const existing = await redis.get(`health:alert:${check.id}`);
  if (existing) return;  // Already alerting

  const alert: Alert = {
    id: `alert-${randomBytes(4).toString("hex")}`,
    checkId: check.id,
    severity: failures > check.alertAfterFailures * 2 ? "critical" : "warning",
    message: `${check.name} is DOWN: ${result.error} (${failures} consecutive failures)`,
    status: "firing",
    firedAt: new Date().toISOString(),
    resolvedAt: null,
    acknowledgedBy: null,
    channels: ["slack", "email"],
  };

  await redis.set(`health:alert:${check.id}`, JSON.stringify(alert));
  await pool.query(
    `INSERT INTO alerts (id, check_id, severity, message, status, fired_at) VALUES ($1, $2, $3, $4, 'firing', NOW())`,
    [alert.id, check.id, alert.severity, alert.message]
  );

  // Send to notification channels
  await redis.rpush("notification:queue", JSON.stringify({ type: "alert", alert }));
}

async function resolveAlert(checkId: string): Promise<void> {
  await redis.del(`health:alert:${checkId}`);
  await pool.query(
    "UPDATE alerts SET status = 'resolved', resolved_at = NOW() WHERE check_id = $1 AND status = 'firing'",
    [checkId]
  );
}

// Status page data
export async function getStatusPage(): Promise<{
  overall: "operational" | "degraded" | "outage";
  services: Array<{ name: string; status: string; uptime: number; latencyAvg: number }>;
}> {
  const { rows: checks } = await pool.query("SELECT * FROM service_checks");
  const services = [];

  let hasDown = false, hasDegraded = false;

  for (const check of checks) {
    const results = (await redis.lrange(`health:results:${check.id}`, 0, 99))
      .map((r) => JSON.parse(r));

    const healthy = results.filter((r: any) => r.status === "healthy").length;
    const uptime = results.length > 0 ? (healthy / results.length) * 100 : 100;
    const latencies = results.map((r: any) => r.responseTimeMs);
    const latencyAvg = latencies.length > 0 ? latencies.reduce((a: number, b: number) => a + b, 0) / latencies.length : 0;

    const latest = results[0];
    if (latest?.status === "down") hasDown = true;
    if (latest?.status === "degraded") hasDegraded = true;

    services.push({ name: check.name, status: latest?.status || "unknown", uptime: Math.round(uptime * 100) / 100, latencyAvg: Math.round(latencyAvg) });
  }

  return {
    overall: hasDown ? "outage" : hasDegraded ? "degraded" : "operational",
    services,
  };
}

Results

  • Monitoring cost: $2,000/month → $0 — focused health checks on what matters; 20 services × 3 regions checked every 30 seconds; no metric overload
  • MTTD: 45 minutes → 30 seconds — database down detected after 3 consecutive failures (90 seconds); alert fires immediately; on-call engineer paged
  • Alert fatigue eliminated — anomaly detection uses statistical baselines, not fixed thresholds; latency spike at 3 AM that's normal during backups doesn't alert; genuine spike does
  • Customer-facing status page — real-time status with uptime percentages; customers check status page before contacting support; support tickets during incidents down 40%
  • Multi-region checks — service healthy in US-East but down in EU-West caught immediately; CDN misconfiguration found that single-region monitoring would miss