Monitoring setup

Skill AtulPurohit/Antigravity-Awesome-Skills/skills/monitoring-setup

Installable GitHub library of 300+ professional agentic skills for Claude Code, Antigravity IDE, Gemini CLI, Cursor, and Copilot. Features a custom NPX installer, 9 stack-specific bundles, validation schemas, security auditing, and an interactive catalog explorer app.

Install

npx -y skills add AtulPurohit/Antigravity-Awesome-Skills --skill monitoring-setup

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What its author says it does

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Set up comprehensive monitoring with Prometheus, Grafana, and alerting. Covers metrics, dashboards, SLOs, and on-call runbooks.

SKILL.md

3.0 KB, 656 tokens by cl100k_base, as published. Nobody here has run it

Monitoring & Observability Setup

Purpose

Build comprehensive observability for production systems covering metrics, logs, traces, and alerts.

The Three Pillars of Observability

1️⃣ Metrics (Prometheus + Grafana)

# prometheus.yml
global:
  scrape_interval: 15s

scrape_configs:
  - job_name: 'myapp'
    static_configs:
      - targets: ['myapp:3000']
    metrics_path: '/metrics'

  - job_name: 'postgresql'
    static_configs:
      - targets: ['postgres-exporter:9187']

Key Metrics to Track

Application:
- Request rate (req/s)
- Error rate (% 5xx)
- P50/P95/P99 latency
- Active connections

Infrastructure:
- CPU utilization
- Memory usage
- Disk I/O
- Network throughput

Business:
- Active users
- Orders per minute
- Revenue per hour
- Conversion rate

Alerting Rules

# alerts.yml
groups:
  - name: application
    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%"
          runbook_url: "https://wiki.example.com/runbooks/high-error-rate"

      - alert: HighLatency
        expr: histogram_quantile(0.99, rate(http_request_duration_seconds_bucket[5m])) > 1.0
        for: 5m
        labels:
          severity: warning
        annotations:
          summary: "P99 latency above 1 second"

2️⃣ Logging (Structured)

// Always use structured JSON logs
logger.info('Order processed', {
  orderId: order.id,
  userId: order.userId,
  amount: order.total,
  duration_ms: processingTime,
  requestId: req.id,
});

// Never use string interpolation for log data
// ❌ logger.info(`Order ${orderId} processed in ${time}ms`)

3️⃣ Distributed Tracing (OpenTelemetry)

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

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

async function processOrder(orderId: string) {
  const span = tracer.startSpan('processOrder');
  span.setAttribute('order.id', orderId);
  
  try {
    // Business logic...
    span.setStatus({ code: SpanStatusCode.OK });
  } catch (error) {
    span.setStatus({ code: SpanStatusCode.ERROR, message: error.message });
    span.recordException(error);
    throw error;
  } finally {
    span.end();
  }
}

Outputs

Prometheus configuration
Grafana dashboards (JSON)
Alert rules for SLOs
Structured logging setup
OpenTelemetry instrumentation
On-call runbook template