Can I use this for microservices architecture?

Yes, it specifically includes patterns for distributed tracing and cross-service monitoring to help debug complex distributed systems.

Which monitoring tools does this skill support?

It provides configurations and best practices for industry-leading tools including Prometheus, Grafana, OpenTelemetry, DataDog, and Sentry.

Is it compatible with cloud-native environments?

Yes, the patterns and templates provided are designed for production-ready, cloud-native environments including Kubernetes and serverless stacks.

How does this skill help with SLOs?

It provides structured frameworks for defining, measuring, and visualizing Service Level Objectives to align technical performance with business goals.

Does it help with incident prevention?

Absolutely; the skill includes templates for proactive alert rules and SLO definitions designed to identify and resolve issues before they affect end-users.

Observability Engineering

Name: Observability Engineering
Author: greyhaven-ai

bygreyhaven-ai

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Analytics & Monitoring

Implements production-grade monitoring, logging, and tracing systems to ensure application reliability and performance.

This skill empowers developers to architect and deploy comprehensive observability stacks using industry-standard tools like Prometheus, Grafana, and OpenTelemetry. It provides structured guidance for implementing Service Level Objectives (SLOs), configuring distributed tracing across microservices, and setting up proactive alerting to prevent incidents before they impact users. Whether you are bootstrapping a new production environment or optimizing existing monitoring infrastructure, this skill ensures best practices are followed for metrics collection, dashboard visualization, and performance tracking.

Key Features

01Prometheus and Grafana configuration templates

02Distributed tracing setup with OpenTelemetry

0315 GitHub stars

04DataDog and Sentry integration for error tracking

05Pre-configured alert rules and dashboard layouts

06Service Level Objective (SLO) definition and implementation

Use Cases

01Implementing distributed tracing to debug latency in complex systems

02Defining and tracking reliability metrics via SLOs and dashboards

03Establishing a production-ready monitoring stack for microservices

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add greyhaven-ai/claude-code-config observability-engineering

For use in Claude.ai and ChatGPT

Download Skill