Does it include pre-made dashboards?

Yes, it provides Grafana JSON templates and Kiali configuration for immediate visualization of service topology and health metrics.

How does this help with Service Level Objectives (SLOs)?

The skill defines 'Golden Signal' metrics (latency, traffic, errors, saturation) and provides alerting rules to help you monitor and maintain your SLOs.

Which service meshes does this skill support?

This skill provides comprehensive configurations, templates, and best practices specifically tailored for both Istio and Linkerd.

Is OpenTelemetry integration supported?

Yes, the skill includes patterns for integrating the OpenTelemetry Collector to unify telemetry data across your entire infrastructure.

Can I use this for distributed tracing?

Yes, it includes detailed templates for Jaeger and OpenTelemetry to implement end-to-end distributed tracing across your service mesh.

Service Mesh Observability

Name: Service Mesh Observability
Author: Microck

byMicrock

•

분석 및 모니터링

Implements comprehensive monitoring, distributed tracing, and performance visualization for Istio and Linkerd service mesh environments.

This skill provides a specialized framework for establishing deep visibility into microservices communication within Istio and Linkerd meshes. It offers ready-to-use templates for Prometheus metrics, Jaeger distributed tracing, and Kiali topology visualization, enabling developers to debug latency issues, monitor 'Golden Signals,' and define Service Level Objectives (SLOs) for complex distributed systems. It is particularly useful for teams looking to standardize their observability stack and automate the deployment of monitoring infrastructure across Kubernetes clusters.

주요 기능

01Interactive service topology visualization templates for Kiali and Linkerd Viz.

02Customizable alerting rules for high error rates, latency spikes, and certificate expirations.

03Distributed tracing implementation patterns using Jaeger and OpenTelemetry standards.

04Automated configuration for Prometheus, Grafana, and Jaeger within mesh environments.

052 GitHub stars

06Pre-configured PromQL queries for tracking request rates, error rates, and P99 latency.

사용 사례

01Implementing distributed tracing to identify performance bottlenecks in deep service call chains.

02Setting up production-grade dashboards for monitoring real-time service-to-service communication.

03Debugging high latency and intermittent 5xx errors across complex microservice architectures.

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add microck/opendots-microck service-mesh-observability

For use in Claude.ai and ChatGPT

Download Skill