Agent Skill to Monitoring Expert
Agent Skill to Monitoring Expert is a verified SKILL blueprint on Promptally in Fullstack & Backend Engineering optimized for leading AI models. 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/mem. Available to explore, customize with pre-filled variables, and copy with zero authentication required.
Technical Specifications
| Specification | Value |
|---|---|
| Artifact type | SKILL (Fullstack & Backend Engineering) |
| Compatible engines | Universal / Model Agnostic |
| Input variables | None (pre-filled) |
| Approximate tokens | 1,584 |
| Source file | SKILL.md |
| Access tier | Free public (MIT) |
Pre-Filled Template
---
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
company: https://synergetic.solutions
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
Maintained by [@jeffallan](https://github.com/jeffallan), Principal Consultant at [Synergetic Solutions](https://synergetic.solutions)
[Documentation](https://jeffallan.github.io/claude-skills/skills/devops/monitoring-expert/)
How to use this skill
- Review the pre-filled example and source files above.
- Copy the template or download
SKILL.md, then use it in your model, IDE, or workflow tool. - Compare your result with the sample output when one is provided.
Frequently Asked Questions
How do I use Agent Skill to Monitoring Expert?
Open the skill on Promptally, review the pre-filled example, then copy the template or download SKILL.md and paste it into your model, IDE or workflow tool. Agent Skill to Monitoring Expert is a static blueprint with no dynamic variables. Copy it and use it as-is.
Is Agent Skill to Monitoring Expert free?
Yes. Agent Skill to Monitoring Expert is free to view, copy and download on Promptally without creating an account. It is published under the MIT license.
What variables does Agent Skill to Monitoring Expert need?
Agent Skill to Monitoring Expert is a static blueprint with no dynamic variables. Copy it and use it as-is.