Does this skill support multiple programming languages?

Yes, it includes implementation patterns and instrumentation code for Python (Flask), Node.js (Express), and Go.

Can I use this with Kubernetes?

Absolutely. The skill provides configuration details for deploying Jaeger and Tempo using Kubernetes operators, manifests, and Helm-adjacent patterns.

How does this help with performance optimization?

It identifies specific 'spans' or operations that cause latency, allowing you to see exactly which service or database query is slowing down your end-to-end request.

What is the difference between Jaeger and Tempo in this skill?

Jaeger is a popular open-source end-to-end distributed tracing system, while Tempo is a high-volume, cost-effective trace storage backend from Grafana often used for long-term retention.

What is distributed tracing?

Distributed tracing is a method used to monitor applications, especially those built on microservices, by tracking the path of requests as they flow through various services.

Distributed Tracing & Observability

Name: Distributed Tracing & Observability
Author: wshobson

bywshobson

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23,194

Analíticas y Monitorización

Implements end-to-end request tracking across microservices using Jaeger, Tempo, and OpenTelemetry to identify performance bottlenecks and debug complex system failures.

Acerca de

This skill equips Claude with the expertise to implement and manage distributed tracing systems within modern microservice architectures. It provides comprehensive patterns for setting up observability stacks using Jaeger and Grafana Tempo, along with production-grade instrumentation code for Python, Node.js, and Go using OpenTelemetry standards. By enabling granular visibility into request flows, spans, and context propagation, it helps developers pinpoint latency issues, trace error propagation across service boundaries, and optimize system performance through data-driven analysis and service dependency visualization.

Características Principales

Automated Jaeger and Grafana Tempo infrastructure setup via Kubernetes and Docker
Advanced sampling strategies including Probabilistic, Rate Limiting, and Adaptive
Distributed context propagation and log correlation implementation patterns
Querying and analysis workflows for identifying slow requests and service bottlenecks
23,194 GitHub stars
Multi-language OpenTelemetry instrumentation for Python, Node.js, and Go

Casos de Uso

Correlating application logs with specific trace IDs to troubleshoot production errors
Visualizing service dependencies and request paths in distributed systems
Debugging intermittent latency spikes across complex microservice meshes

Acerca de

Características Principales

Automated Jaeger and Grafana Tempo infrastructure setup via Kubernetes and Docker
Advanced sampling strategies including Probabilistic, Rate Limiting, and Adaptive
Distributed context propagation and log correlation implementation patterns
Querying and analysis workflows for identifying slow requests and service bottlenecks
23,194 GitHub stars
Multi-language OpenTelemetry instrumentation for Python, Node.js, and Go

Casos de Uso

Correlating application logs with specific trace IDs to troubleshoot production errors
Visualizing service dependencies and request paths in distributed systems
Debugging intermittent latency spikes across complex microservice meshes