Monitoring Expert

What it does

When your agent uses it

• Adding structured logging, metrics and tracing to a service
• Building Grafana dashboards with the RED or USE method
• Writing Prometheus alert rules for critical paths
• Running load tests with k6 or Artillery
• Profiling CPU and memory bottlenecks or forecasting capacity

Example prompts

• “Add Pino JSON logging with request IDs to the Express API.”
• “Expose request count and latency histograms from this Node service for Prometheus.”
• “Write an alert rule that fires when the checkout error rate stays high for several minutes.”
• “Create a k6 script that ramps up traffic on the search endpoint and checks response times.”

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

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

What it can do on your machine

Read from SKILL.md and the folder at commit 1be15d8. It shows what the files ask for, not the result of running them.

• Tool permissions

  Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

  From allowed-tools in the SKILL.md frontmatter.

• Runs code

  No scripts in the folder and no shell commands in SKILL.md (its code samples are javascript and yaml).

  From the folder's file list and the shell code blocks in SKILL.md.

• Network

  Links to these hosts (documentation or services it may open):

  • github.com
  • synergetic.solutions
  • jeffallan.github.io

  From URLs in SKILL.md, links to its own repository left out.

• Credentials

  Names no API keys, tokens, secrets or passwords.

  From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Monitoring Expert loads about 1.6k tokens when it runs, and up to ~11k if it reads all its reference files. Until then it costs about 139 tokens; SKILL.md has 228 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~139

When it runs · the whole SKILL.md, loaded when a task matches

~1.6k

With references · SKILL.md plus every file in references/, read only if the agent opens them

~11k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from Jeffallan/claude-skills at commit 1be15d8, republished under its MIT licence (© Jeffallan). 228 words, ~1,585 tokens.

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

metadata.company

https://synergetic.solutions

metadata.version

1.1.0

metadata.domain

devops

metadata.triggers

monitoring, observability, logging, metrics, tracing, alerting, Prometheus, Grafana, DataDog, APM, performance testing, load testing, profiling, capacity…

metadata.role

specialist

metadata.scope

implementation

metadata.output-format

code

metadata.related-skills

devops-engineer, debugging-wizard, architecture-designer

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}`);

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());
});

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();
  }
}

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 }}"

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);
}

© Jeffallan, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 8 other files (references) in skills/monitoring-expert of Jeffallan/claude-skills.

• SKILL.md
• references/alerting-rules.md
• references/application-profiling.md
• references/capacity-planning.md
• references/dashboards.md
• references/opentelemetry.md
• references/performance-testing.md
• references/prometheus-metrics.md
• references/structured-logging.md

Open the folder on GitHubat commit 1be15d8