What are recording rules and why should I use them?

Recording rules allow you to pre-compute frequently needed or computationally expensive expressions and save their result as a new set of time series, improving dashboard performance.

How do I validate my Prometheus configuration with this skill?

The skill provides specific promtool commands to check the syntax of your prometheus.yml and rule files before deployment.

Can this skill help with Kubernetes monitoring?

Yes, it includes specialized configurations for Kubernetes service discovery and Helm-based installation patterns.

Does this support high availability?

The skill follows best practices for HA, including integration patterns for long-term storage solutions like Thanos or Cortex and multi-instance setups.

Prometheus Configuration & Monitoring

Name: Prometheus Configuration & Monitoring
Author: HermeticOrmus

byHermeticOrmus

Analytics & Monitoring

Configures Prometheus for comprehensive metric collection, alerting, and infrastructure monitoring across diverse environments.

About

This skill provides a complete framework for setting up Prometheus to achieve deep observability of applications and infrastructure. It includes detailed guides for deployment via Kubernetes or Docker, advanced scrape configurations with service discovery, and the implementation of recording rules to optimize query performance. By providing standardized templates for alerting and validation scripts, this skill ensures your monitoring stack is production-ready, scalable, and capable of delivering actionable insights through high-quality metric collection.

Key Features

Dynamic service discovery for Kubernetes, files, and static targets
Production-grade alerting rules for proactive incident response
Multi-platform deployment for Kubernetes and Docker Compose
Automated configuration validation and troubleshooting commands
Pre-computed recording rules for optimized PromQL querying
0 GitHub stars

Use Cases

Establishing standardized alerting and performance thresholds for DevOps teams
Implementing end-to-end observability for microservice architectures
Automating metric scraping and service discovery in cloud-native environments

About

Key Features

Dynamic service discovery for Kubernetes, files, and static targets
Production-grade alerting rules for proactive incident response
Multi-platform deployment for Kubernetes and Docker Compose
Automated configuration validation and troubleshooting commands
Pre-computed recording rules for optimized PromQL querying
0 GitHub stars

Use Cases

Establishing standardized alerting and performance thresholds for DevOps teams
Implementing end-to-end observability for microservice architectures
Automating metric scraping and service discovery in cloud-native environments