Guide · Agent skill

How to use the Monitoring Expert agent skill

Updated 2026-10-29 · written with AI from the project's own documentation and checked against our catalogue data

The monitoring-expert skill is a set of instructions and reference notes that teaches an AI agent how to make a software service observable. It covers structured logging, Prometheus metrics, OpenTelemetry traces, alerting rules, Grafana dashboards, load testing with k6 or Artillery, profiling, and capacity forecasting. This guide explains what the skill does, how to install it, how to ask for help with natural requests, and what to check before deploying anything it produces.

Monitoring ExpertJeffallan/claude-skills · MIT Download the skill

Quick start

1Download and unzip the skill. Download the ZIP from this page and unzip it so you have a monitoring-expert folder containing SKILL.md.
2Place it in a skills folder. Move the folder to ~/.claude/skills/ for personal use or to .claude/skills/ in a project. Alternatively, upload the ZIP under Settings → Capabilities → Skills in the Claude apps.
3Describe your monitoring task. Ask in plain language, for example asking for structured logging in a named service or an alert for a specific failure.
4Provide context about your stack. Tell the agent which language, framework and tools you use, such as Node.js with Pino, Prometheus and Grafana.
5Review the generated code and config. Read the logging, metrics, alert and dashboard code before running or deploying it.
6Test in a non-production environment. Confirm metrics arrive in Prometheus and alerts behave as expected, and run load tests against staging with limits.

What the monitoring-expert skill makes the agent do

An agent skill is a folder containing a SKILL.md file and optional reference files. When the skill is installed and relevant to your request, the agent reads these instructions and follows them. This skill is aimed at making software observable, meaning you can see what it is doing from its logs, metrics and traces.

The skill describes a five-step workflow: assess what needs monitoring, instrument the application, collect the data and verify it arrives, visualize it in dashboards using the RED or USE methods, and define alerts on critical paths.

Structured JSON logging with Pino and request IDs for correlation
Prometheus metrics with prom-client, using counters and histograms
OpenTelemetry tracing with spans around critical operations
Prometheus alert rules and Grafana dashboards
Load test scripts for k6 or Artillery, with thresholds
CPU and memory profiling guidance and capacity forecasting

Install the monitoring-expert skill

Download the ZIP for this skill from this page and unzip it. You can install it for yourself across projects or for a single project.

For a personal install, place the unzipped monitoring-expert folder in ~/.claude/skills/. For a project-only install, place it in the project's .claude/skills/ folder. You can also upload the ZIP in the Claude apps under Settings → Capabilities → Skills.

Personal: ~/.claude/skills/monitoring-expert/
Project: .claude/skills/monitoring-expert/ inside your repository
Claude apps: Settings → Capabilities → Skills, then upload the ZIP

Trigger the skill with natural requests

You do not need a special command. Describe the task in plain language and mention the kind of work involved. The skill's description lists the situations it is meant for, so requests that match those topics are the most likely to use it.

Good triggers include setting up monitoring for a service, adding observability, debugging production issues with logs, metrics or traces, running load tests with k6 or Artillery, profiling CPU or memory bottlenecks, and forecasting capacity needs. The skill also responds to terms such as Prometheus, Grafana, alerting, tracing and APM.

Add structured logging and request IDs to my Express service.
Write a Prometheus alert for error rate above 5% on the orders API.
Build a Grafana dashboard using the RED method for these endpoints.
Write a k6 load test with a p(95) latency threshold for the checkout route.
Help me profile memory growth in this Node.js app.
Estimate when our API will need more capacity based on request growth.

Example: adding metrics to a Node.js route

The skill includes a Prometheus example that counts HTTP requests and records latency in a histogram, then exposes a /metrics endpoint for Prometheus to scrape. The example uses the prom-client package and labels metrics by method, route and status.

Review the labels before using them. Route labels should be route templates rather than raw URLs with IDs, or the number of time series can grow without limit.

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

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

Example: a Prometheus alert rule

The skill's alerting guidance includes a rule that fires when the share of 5xx responses stays above 5% for two minutes. Alerts are defined in YAML and evaluated by Prometheus.

The skill warns against alerting on every error, because that causes alert fatigue. Set thresholds and durations deliberately, and test that the rule does not flood your team before you ship it.

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

Safety notes before you deploy

The skill does not run scripts and does not use the network by itself. Its output is code and configuration written by the agent. Someone who understands your stack must read it, deploy it and check it.

Load tests and alert rules can affect live systems. A load test sends real traffic, so run it against a staging environment or with limits you understand. Generated alert rules should be checked for noise before they page anyone.

The skill's own constraints say not to log passwords, tokens or personal data, and to include request IDs for correlation. Review generated logging code to confirm that sensitive fields are excluded.

Never run load tests against production without agreed limits
Review alert thresholds and durations before enabling them
Check that logs contain no secrets or personal data

What people use it for

Adding structured logs with request IDs to a Node.js API so errors can be traced across requests
Writing Prometheus alert rules for error rate and latency on a critical service
Creating a Grafana dashboard that follows the RED method for an HTTP service
Adding OpenTelemetry spans around a slow database operation to find where time is spent
Writing a k6 load test with thresholds for p(95) latency and error rate before a release
Profiling a Node.js service's CPU or memory use with clinic.js or the built-in profiler

Frequently asked questions

How do I install the monitoring-expert skill?

Download the ZIP from this page and unzip it. Put the monitoring-expert folder in ~/.claude/skills/ for personal use, or in a project's .claude/skills/ folder. You can also upload the ZIP in the Claude apps under Settings → Capabilities → Skills.

Is the monitoring-expert skill free?

The skill is released under the MIT licence, as stated on this page. Check the licence file in the repository for the full terms before using it in a commercial product.

What do I need to use this skill?

The examples need Node.js for the Pino, prom-client and OpenTelemetry code. The full stack also uses Prometheus, Grafana and a tracing backend such as Jaeger, and load tests need k6 or Artillery.

Does the skill run scripts or access the network?

According to this page, the skill does not run scripts and does not use the network. It provides guidance and code for the agent to write, which you then deploy and verify yourself.

Can the skill set up monitoring without any coding knowledge?

Not safely. The output is code and configuration that must be deployed and checked by someone who understands it. Non-technical users who need a finished monitoring product should choose a managed tool instead.

Does it work with DataDog or other observability tools?

DataDog is listed as a trigger term, but the examples focus on Prometheus, Grafana, Pino and OpenTelemetry. If your team must use a different vendor or standard, check the output carefully against that tool's requirements.

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Our verdict

Use this skill if you are a developer or engineer adding monitoring, tracing, alerting or load tests to a Node.js or similar service and you can review the code it produces. Pick something else if you need a finished monitoring product, must use a different observability vendor, or want to apply generated alerts and load tests to live systems without a careful review.

Download the skill