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 bottle
Install with the open skills CLI (global, non-interactive — available in every Claude Code session):
npx skills add Jeffallan/claude-skills --skill "monitoring-expert" -g -a claude-code -yOr manually — clone and copy the skill directory (SKILL.md + companion files):
git clone --depth 1 https://github.com/Jeffallan/claude-skills /tmp/claude-skills && cp -r /tmp/claude-skills/skills/monitoring-expert ~/.claude/skills/monitoring-expertThis skill is a directory: SKILL.md is the entry point; the files below ship with it.
---
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
[Documentation](https://jeffallan.github.io/claude-skills/skills/devops/monitoring-expert/)
Use when facing 2+ independent tasks that can be worked on without shared state or sequential dependencies
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes
Use when implementing any feature or bugfix, before writing implementation code