Does it provide pre-built alerting rules?

Yes, it includes ready-to-use Alertmanager rules for common issues like High Error Rates and Pod CrashLooping based on PromQL.

What monitoring tools does this skill support?

This skill provides guidance and configurations for Prometheus (metrics), Grafana (visualization), Loki/ELK (logging), and Jaeger/Tempo (distributed tracing).

Can I use this for Kubernetes environments?

Yes, it includes specific configurations for Kubernetes service discovery (SD) and relabeling rules to automatically scrape annotated pods.

What are the RED and USE methods mentioned?

These are monitoring frameworks: RED focuses on services (Rate, Errors, Duration), while USE focuses on resources (Utilization, Saturation, Errors).

Infrastructure Monitoring Setup

Name: Infrastructure Monitoring Setup
Author: timequity

bytimequity

Analíticas y Monitorización

Implements comprehensive observability stacks using Prometheus, Grafana, and proactive alerting frameworks.

Acerca de

The Monitoring Setup skill equips Claude with the patterns and configurations needed to deploy a production-grade observability stack covering metrics, logs, and traces. It provides standardized templates for Prometheus scraping, Grafana dashboard definitions, and critical alert rules based on industry-standard RED and USE methodologies. This skill is particularly useful for DevOps engineers and SREs who need to establish reliable monitoring for Kubernetes environments, define clear SLIs/SLOs, and ensure system health through automated alerting and visualization.

Características Principales

Pre-defined alert rules for high error rates and pod crash loops
Implementation of RED and USE monitoring methodologies
Guidance on defining Service Level Indicators (SLIs) and Error Budgets
Prometheus time-series configuration and Kubernetes service discovery
0 GitHub stars
Automated Grafana dashboard generation for request rates and latency

Casos de Uso

Deploying a full observability stack for a new Kubernetes cluster
Standardizing dashboard metrics across multiple development teams
Configuring automated alerts for microservice performance degradation

Acerca de

Características Principales

Pre-defined alert rules for high error rates and pod crash loops
Implementation of RED and USE monitoring methodologies
Guidance on defining Service Level Indicators (SLIs) and Error Budgets
Prometheus time-series configuration and Kubernetes service discovery
0 GitHub stars
Automated Grafana dashboard generation for request rates and latency

Casos de Uso

Deploying a full observability stack for a new Kubernetes cluster
Standardizing dashboard metrics across multiple development teams
Configuring automated alerts for microservice performance degradation