Ops monitoring

Skill christopherlouet/claude-base/.claude/skills/ops-monitoring

Opinionated Claude Code foundation — Explore → TDD → Audit workflow, auto-detected stack presets (nextjs, fastapi, astro, ...), curl | bash install. MIT.

Install

npx -y skills add christopherlouet/claude-base --skill ops-monitoring

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

5 stars5 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

Application instrumentation for monitoring. Trigger when the user wants to add logs, metrics, or traces.

SKILL.md

3.6 KB, as published. Nobody here has run it

Monitoring Instrumentation

3 Pillars of Observability

Logs - Discrete events
Metrics - Numerical measurements
Traces - Request paths

Structured Logs (Node.js)

import pino from 'pino';

const logger = pino({
  level: process.env.LOG_LEVEL || 'info',
  base: { service: 'api', env: process.env.NODE_ENV },
});

logger.info({ userId: '123', action: 'login' }, 'User logged in');
logger.error({ err, requestId }, 'Request failed');

Prometheus Metrics

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

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

const httpDuration = new Histogram({
  name: 'http_request_duration_seconds',
  help: 'Request duration',
  labelNames: ['method', 'path'],
  buckets: [0.1, 0.5, 1, 2, 5],
});

OpenTelemetry Traces

import { trace } from '@opentelemetry/api';

const tracer = trace.getTracer('my-service');

async function processOrder(orderId: string) {
  return tracer.startActiveSpan('processOrder', async (span) => {
    span.setAttribute('orderId', orderId);
    try {
      // ... processing
    } finally {
      span.end();
    }
  });
}

Health Checks

app.get('/health', (req, res) => res.json({ status: 'ok' }));

app.get('/ready', async (req, res) => {
  const dbOk = await db.query('SELECT 1');
  res.status(dbOk ? 200: 503).json({ db: dbOk });
});

Deploying the observability stack

Once the code is instrumented, deploy the backing stack (Prometheus + Grafana + Loki + Alertmanager).

Mode: Docker Compose for dev/staging, Kubernetes + Helm for production (or Victoria Metrics / managed).
Prometheus: prometheus.yml scrape configs + alert.rules.yml.
Alertmanager: alertmanager.yml routes + receivers (Slack / PagerDuty / email).
Grafana: provision datasources + dashboards.
Loki + Promtail: log aggregation; add node-exporter + cAdvisor for system metrics.
Persistent storage for metrics data; never expose Prometheus/Alertmanager without auth in production; configure alerts before going to prod and test the stack in staging first.

Gives 0 of the 12 instructions most monitoring observability skills give

Counted across 481 of the 483 authors here whose files we hold, read 2026-08-06

link every alert to a runbookin 43 of 481, across 35 files
use structured json loggingin 36 of 481, across 31 files
alert on user-facing symptomsin 20 of 481, across 15 files
emit structured JSON logs with stable event namesin 18 of 481, across 13 files
propagate trace context across boundariesin 16 of 481
use histograms for latency trackingin 14 of 481, across 9 files
use OpenTelemetry for distributed tracingin 13 of 481, across 8 files
include a correlation ID on every log linein 13 of 481, across 8 files
Define service level objectivesin 10 of 481, across 7 files
Call useAzureMonitor before importing other modulesin 9 of 481, across 2 files
stop and ask for clarification if inputs are missingin 9 of 481, across 2 files
define on-call questions before adding telemetryin 9 of 481, across 4 files

Said here and by no other author read

use prometheus counters and histograms
deploy prometheus grafana loki and alertmanager
add persistent storage for metrics
test the stack in staging

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once.