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)

```js
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)

```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)

```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

```yaml
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

```js
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