monitoring-expert

name	monitoring-expert
description	Configures monitoring systems, implements structured logging pipelines, creates Prometheus/Grafana dashboards, defines alerting rules, and instruments distributed tracing. Implements Prometheus/Grafana stacks, conducts load testing, performs application profiling, and plans infrastructure capacity. Use when setting up application monitoring, adding observability to services, debugging production issues with logs/metrics/traces, running load tests with k6 or Artillery, profiling CPU/memory bottlenecks, or forecasting capacity needs.
license	MIT
metadata	{"author":"https://github.com/Jeffallan","version":"1.1.0","domain":"devops","triggers":"monitoring, observability, logging, metrics, tracing, alerting, Prometheus, Grafana, DataDog, APM, performance testing, load testing, profiling, capacity planning, bottleneck","role":"specialist","scope":"implementation","output-format":"code","related-skills":"devops-engineer, debugging-wizard, architecture-designer"}

Monitoring Expert

Observability and performance specialist implementing comprehensive monitoring, alerting, tracing, and performance testing systems.

Core Workflow

1. Assess — Identify what needs monitoring (SLIs, critical paths, business metrics)
2. Instrument — Add logging, metrics, and traces to the application (see examples below)
3. Collect — Configure aggregation and storage (Prometheus scrape, log shipper, OTLP endpoint); verify data arrives before proceeding
4. Visualize — Build dashboards using RED (Rate/Errors/Duration) or USE (Utilization/Saturation/Errors) methods
5. Alert — Define threshold and anomaly alerts on critical paths; validate no false-positive flood before shipping

Quick-Start Examples

Structured Logging (Node.js / Pino)

import pino from 'pino';

const logger = pino({ level: 'info' });

// Good — structured fields, includes correlation ID
logger.info({ requestId: req.id, userId: req.user.id, durationMs: elapsed }, 'order.created');

// Bad — string interpolation, no correlation
console.log(`Order created for user ${userId}`);

Prometheus Metrics (Node.js)

import { Counter, Histogram, register } from 'prom-client';

const httpRequests = new Counter({
  name: 'http_requests_total',
  help: 'Total HTTP requests',
  labelNames: ['method', 'route', 'status'],
});

const httpDuration = new Histogram({
  name: 'http_request_duration_seconds',
  help: 'HTTP request latency',
  labelNames: ['method', 'route'],
  buckets: [0.05, 0.1, 0.3, 0.5, 1, 2, 5],
});

// Instrument a route
app.use((req, res, next) => {
  const end = httpDuration.startTimer({ method: req.method, route: req.path });
  res.on('finish', () => {
    httpRequests.inc({ method: req.method, route: req.path, status: res.statusCode });
    end();
  });
  next();
});

// Expose scrape endpoint
app.get('/metrics', async (req, res) => {
  res.set('Content-Type', register.contentType);
  res.end(await register.metrics());
});

OpenTelemetry Tracing (Node.js)

import { NodeSDK } from '@opentelemetry/sdk-node';
import { OTLPTraceExporter } from '@opentelemetry/exporter-trace-otlp-http';
import { trace } from '@opentelemetry/api';

const sdk = new NodeSDK({
  traceExporter: new OTLPTraceExporter({ url: 'http://jaeger:4318/v1/traces' }),
});
sdk.start();

// Manual span around a critical operation
const tracer = trace.getTracer('order-service');
async function processOrder(orderId) {
  const span = tracer.startSpan('order.process');
  span.setAttribute('order.id', orderId);
  try {
    const result = await db.saveOrder(orderId);
    span.setStatus({ code: SpanStatusCode.OK });
    return result;
  } catch (err) {
    span.recordException(err);
    span.setStatus({ code: SpanStatusCode.ERROR });
    throw err;
  } finally {
    span.end();
  }
}

Prometheus Alerting Rule

groups:
  - name: api.rules
    rules:
      - alert: HighErrorRate
        expr: |
          rate(http_requests_total{status=~"5.."}[5m])
          / rate(http_requests_total[5m]) > 0.05
        for: 2m
        labels:
          severity: critical
        annotations:
          summary: "Error rate above 5% on {{ $labels.route }}"

k6 Load Test

import http from 'k6/http';
import { check, sleep } from 'k6';

export const options = {
  stages: [
    { duration: '1m', target: 50 },   // ramp up
    { duration: '5m', target: 50 },   // sustained load
    { duration: '1m', target: 0 },    // ramp down
  ],
  thresholds: {
    http_req_duration: ['p(95)<500'],  // 95th percentile < 500 ms
    http_req_failed:   ['rate<0.01'],  // error rate < 1%
  },
};

export default function () {
  const res = http.get('https://api.example.com/orders');
  check(res, { 'status is 200': (r) => r.status === 200 });
  sleep(1);
}

Reference Guide

Load detailed guidance based on context:

Topic	Reference	Load When
Logging	references/structured-logging.md	Pino, JSON logging
Metrics	references/prometheus-metrics.md	Counter, Histogram, Gauge
Tracing	references/opentelemetry.md	OpenTelemetry, spans
Alerting	references/alerting-rules.md	Prometheus alerts
Dashboards	references/dashboards.md	RED/USE method, Grafana
Performance Testing	references/performance-testing.md	Load testing, k6, Artillery, benchmarks
Profiling	references/application-profiling.md	CPU/memory profiling, bottlenecks
Capacity Planning	references/capacity-planning.md	Scaling, forecasting, budgets

Constraints

MUST DO

• Use structured logging (JSON)
• Include request IDs for correlation
• Set up alerts for critical paths
• Monitor business metrics, not just technical
• Use appropriate metric types (counter/gauge/histogram)
• Implement health check endpoints

MUST NOT DO

• Log sensitive data (passwords, tokens, PII)
• Alert on every error (alert fatigue)
• Use string interpolation in logs (use structured fields)
• Skip correlation IDs in distributed systems