monitoring

Define metrics, logging, tracing, and alerting rules for distributed systems.

1|Updated Mar 16, 2026

Search Tags:#monitoring #logging #metrics #observability #health check #tracing #alerting

Ready to run

npx skills add https://github.com/iceflower/opencode-agents-and-skills --skill monitoring-iceflower

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: monitoring
Source: https://github.com/iceflower/opencode-agents-and-skills/tree/main/skills/monitoring
Command: npx skills add https://github.com/iceflower/opencode-agents-and-skills --skill monitoring-iceflower

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill provides a comprehensive framework for implementing robust monitoring and observability in your systems, ensuring you can detect, diagnose, and resolve issues quickly.

Core Features & Use Cases

Observability Pillars: Understand and implement metrics, logging, and tracing.
Metric Best Practices: Define clear metric types and naming conventions.
Alerting Strategies: Design effective alerting rules with severity levels and actionable runbooks.
Health Checks: Implement liveness, readiness, and startup probes correctly.
Use Case: When deploying a new microservice, use this Skill to define the essential metrics to collect, set up alerts for critical conditions like high error rates or latency, and configure health checks for Kubernetes.

Quick Start

Implement monitoring for a new service by defining its key metrics and alerting rules.

Frequently Asked Questions about monitoring

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

What are the core observability patterns for monitoring system health?▼

Core observability patterns for monitoring system health involve implementing metrics, logging, and tracing to detect, diagnose, and resolve issues. This framework provides systematic approaches for distributed systems.

How do I define metric types and naming conventions for a new service?▼

To define metric types and naming conventions, apply framework-agnostic best practices that establish clear metric definitions. This ensures consistent observability across your distributed systems.

How should I design alerting rules and severity levels for high error rates?▼

Design alerting rules for high error rates by establishing clear severity levels and pairing them with actionable runbooks. This ensures effective alerting strategies during system incidents.

How do I implement liveness, readiness, and startup probes correctly?▼

Implement liveness, readiness, and startup probes correctly by following established health check probe strategies. This addresses the need for systematic monitoring in distributed deployments.

Can I use these monitoring patterns for microservices deployed on Kubernetes?▼

You can use these monitoring patterns for microservices on Kubernetes to configure essential health checks and alerts. The framework-agnostic design suits distributed systems requiring systematic observability.

What is the best way to set up tracing and logging in distributed systems?▼

The best way to set up tracing and logging in distributed systems is applying framework-agnostic observability patterns. This approach ensures comprehensive diagnosis and resolution of system issues.