monitoring-observability

Q: How do I install monitoring-observability?

Run npx skills add https://github.com/rohitg00/awesome-claude-code-toolkit --skill monitoring-observability in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/monitoring-observability/ (personal) or .claude/skills/monitoring-observability/ (project).

by rohitg00DevOps & Cloud

Monitoring and observability with OpenTelemetry, Prometheus, Grafana dashboards, and structured logging

Installs: 191
Last 24h: —
Trending rank: —
Security: Not audited

Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/rohitg00/awesome-claude-code-toolkit --skill monitoring-observability

01
Install
Run the command in your project. The CLI asks which agents to install for.
02
Describe the task
Ask your agent for something this skill covers. It loads monitoring-observability automatically when relevant.
03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

Monitoring & Observability

OpenTelemetry Setup

import { NodeSDK } from "@opentelemetry/sdk-node";
import { OTLPTraceExporter } from "@opentelemetry/exporter-trace-otlp-http";
import { OTLPMetricExporter } from "@opentelemetry/exporter-metrics-otlp-http";
import { HttpInstrumentation } from "@opentelemetry/instrumentation-http";
import { PgInstrumentation } from "@opentelemetry/instrumentation-pg";
import { PeriodicExportingMetricReader } from "@opentelemetry/sdk-metrics";

const sdk = new NodeSDK({
  serviceName: "order-service",
  traceExporter: new OTLPTraceExporter({
    url: "http://otel-collector:4318/v1/traces",
  }),
  metricReader: new PeriodicExportingMetricReader({
    exporter: new OTLPMetricExporter({
      url: "http://otel-collector:4318/v1/metrics",
    }),
    exportIntervalMillis: 15000,
  }),
  instrumentations: [
    new HttpInstrumentation(),
    new PgInstrumentation(),
  ],
});

sdk.start();
process.on("SIGTERM", () => sdk.shutdown());

Custom Spans and Metrics

import { trace, metrics, SpanStatusCode } from "@opentelemetry/api";

const tracer = trace.getTracer("order-service");
const meter = metrics.getMeter("order-service");

const orderCounter = meter.createCounter("orders.created", {
  description: "Number of orders created",
});

const orderDuration = meter.createHistogram("orders.processing_duration_ms", {
  description: "Order processing duration in milliseconds",
  unit: "ms",
});

async function createOrder(input: CreateOrderInput) {
  return tracer.startActiveSpan("createOrder", async (span) => {
    try {
      span.setAttributes({
        "order.customer_id": input.customerId,
        "order.item_count": input.items.length,
      });

      const start = performance.now();
      const order = await db.order.create({ data: input });

      orderCounter.add(1, { status: "success" });
      orderDuration.record(performance.now() - start);

      span.setStatus({ code: SpanStatusCode.OK });
      return order;
    } catch (error) {
      span.setStatus({ code: SpanStatusCode.ERROR, message: error.message });
      orderCounter.add(1, { status: "error" });
      throw error;
    } finally {
      span.end();
    }
  });
}

Prometheus Metrics

# prometheus.yml
global:
  scrape_interval: 15s

scrape_configs:
  - job_name: "api-servers"
    static_configs:
      - targets: ["api-1:9090", "api-2:9090"]
    metrics_path: /metrics

  - job_name: "node-exporter"
    static_configs:
      - targets: ["node-exporter:9100"]

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

const registry = new Registry();
collectDefaultMetrics({ register: registry });

const httpRequestDuration = new Histogram({
  name: "http_request_duration_seconds",
  help: "HTTP request duration in seconds",
  labelNames: ["method", "route", "status"],
  buckets: [0.01, 0.05, 0.1, 0.5, 1, 5],
  registers: [registry],
});

app.use((req, res, next) => {
  const end = httpRequestDuration.startTimer();
  res.on("finish", () => {
    end({ method: req.method, route: req.route?.path ?? req.path, status: res.statusCode });
  });
  next();
});

app.get("/metrics", async (req, res) => {
  res.set("Content-Type", registry.contentType);
  res.end(await registry.metrics());
});

Structured Logging

import pino from "pino";

const logger = pino({
  level: process.env.LOG_LEVEL ?? "info",
  formatters: {
    level: (label) => ({ level: label }),
  },
  redact: ["req.headers.authorization", "password", "token"],
});

function requestLogger(req, res, next) {
  const start = Date.now();
  res.on("finish", () => {
    logger.info({
      method: req.method,
      url: req.url,
      status: res.statusCode,
      duration_ms: Date.now() - start,
      trace_id: req.headers["x-trace-id"],
    });
  });
  next();
}

Alerting Rules

groups:
  - name: api-alerts
    rules:
      - alert: HighErrorRate
        expr: rate(http_request_duration_seconds_count{status=~"5.."}[5m]) / rate(http_request_duration_seconds_count[5m]) > 0.05
        for: 5m
        labels:
          severity: critical
        annotations:
          summary: "Error rate above 5% for {{ $labels.route }}"

      - alert: HighLatency
        expr: histogram_quantile(0.99, rate(http_request_duration_seconds_bucket[5m])) > 2
        for: 10m
        labels:
          severity: warning

Anti-Patterns

Logging sensitive data (passwords, tokens, PII) without redaction
Using string interpolation in log messages instead of structured fields
Creating unbounded cardinality in metric labels (e.g., user IDs as labels)
Not correlating logs and traces with a shared trace ID
Alerting on symptoms (high CPU) without understanding root cause
Missing SLO definitions before building dashboards

Checklist

OpenTelemetry SDK initialized with auto-instrumentation for HTTP, DB, and messaging
Custom spans added for business-critical operations
Metrics use bounded label cardinality
Structured logging with JSON output and secret redaction
Trace context propagated across service boundaries
Alerting rules based on SLOs (error rate, latency percentiles)
Dashboards show RED metrics (Rate, Errors, Duration) per service
Log retention and rotation policies configured

FAQ

What does the monitoring-observability skill do?

Monitoring and observability with OpenTelemetry, Prometheus, Grafana dashboards, and structured logging

How do I install monitoring-observability?

Run npx skills add https://github.com/rohitg00/awesome-claude-code-toolkit --skill monitoring-observability in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/monitoring-observability/ (personal) or .claude/skills/monitoring-observability/ (project).

Which AI agents can use monitoring-observability?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is monitoring-observability safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains monitoring-observability?

It is published by rohitg00 in rohitg00/awesome-claude-code-toolkit. It has 191 recorded installs.

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monitoring-observability

Install

Install

Describe the task

Review the result

SKILL.md

Monitoring & Observability

OpenTelemetry Setup

Custom Spans and Metrics

Prometheus Metrics

Structured Logging

Alerting Rules

Anti-Patterns

Checklist

FAQ

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