How does it handle the performance overhead of tracing?

It implements various sampling strategies, such as probabilistic and rate-limiting, to ensure that tracing activities consume minimal system resources.

What is the primary benefit of using this skill with Claude Code?

It provides Claude with specialized knowledge to generate production-grade OpenTelemetry instrumentation and infrastructure configurations for complex distributed systems.

Does this skill help with cross-service request tracking?

Yes, it defines specific patterns for context propagation using W3C Trace Context headers to ensure traces aren't lost between services.

Which tracing backends are supported?

The skill includes comprehensive setup guides and configuration templates for both Jaeger and Grafana Tempo.

Can I use this for troubleshooting specific errors?

Absolutely. It includes techniques for finding slow requests, identifying error propagation, and correlating traces directly with application logs.

Distributed Tracing Implementation

Name: Distributed Tracing Implementation
Author: HermeticOrmus

byHermeticOrmus

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Analytics & Monitoring

Implements comprehensive distributed tracing using Jaeger and Tempo to monitor request flows and resolve performance bottlenecks in microservices.

About

This skill provides standardized patterns for implementing end-to-end observability across distributed systems using OpenTelemetry, Jaeger, and Grafana Tempo. It enables Claude to assist developers in instrumenting applications across Python, Node.js, and Go, configuring seamless context propagation via HTTP headers, and deploying necessary infrastructure through Docker or Kubernetes. By offering clear guidance on sampling strategies and trace analysis, it helps teams identify latency issues, debug error propagation, and visualize complex service dependencies within modern cloud-native architectures.

Key Features

Advanced sampling strategies including probabilistic and rate-limited sampling
Integrated trace analysis techniques for bottleneck and error detection
Multi-language instrumentation patterns for Python, Node.js, and Go using OpenTelemetry
Infrastructure-as-Code templates for Jaeger and Grafana Tempo deployments
Standardized context propagation for tracking requests across service boundaries
2 GitHub stars

Use Cases

Debugging high-latency requests across complex microservice clusters
Visualizing service dependency graphs to understand architectural flow
Correlating application logs with specific traces for rapid root cause analysis

About

Key Features

Advanced sampling strategies including probabilistic and rate-limited sampling
Integrated trace analysis techniques for bottleneck and error detection
Multi-language instrumentation patterns for Python, Node.js, and Go using OpenTelemetry
Infrastructure-as-Code templates for Jaeger and Grafana Tempo deployments
Standardized context propagation for tracking requests across service boundaries
2 GitHub stars

Use Cases

Debugging high-latency requests across complex microservice clusters
Visualizing service dependency graphs to understand architectural flow
Correlating application logs with specific traces for rapid root cause analysis