Does this skill support specific tracing backends?

Yes, it includes patterns and best practices for popular backends like Jaeger, Zipkin, and Grafana Tempo, as well as cloud-native options like AWS X-Ray and GCP Cloud Trace.

Can it help with performance optimization?

Absolutely. It focuses on identifying slow spans, analyzing request durations, and finding hidden dependencies that contribute to latency in distributed systems.

What is the primary purpose of the Distributed Tracing skill?

It provides Claude with domain-specific knowledge to help users implement, configure, and debug request tracing across multiple services using standards like OpenTelemetry.

How does it help with log correlation?

The skill provides patterns for adding trace and span IDs to structured logs, allowing developers to query all logs associated with a specific request journey.

Does it cover sampling strategies for high-traffic systems?

Yes, it covers head-based, tail-based, and adaptive sampling to help manage the costs and storage requirements of high-volume trace data.

Distributed Tracing & Observability

Name: Distributed Tracing & Observability
Author: melodic-software

bymelodic-software

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Analytics & Monitoring

Implements and debugs request flows across microservices using OpenTelemetry standards and distributed tracing patterns.

About

This skill equips Claude with the specialized knowledge required to design, implement, and troubleshoot distributed tracing systems within complex microservices architectures. It provides comprehensive guidance on OpenTelemetry (OTel) instrumentation, span context propagation using W3C standards, and the selection of tracing backends like Jaeger, Zipkin, or Grafana Tempo. By applying these patterns, developers can gain end-to-end visibility into request lifecycles, identify high-latency bottlenecks, and effectively correlate logs and metrics with traces for faster incident resolution.

Key Features

Backend selection guidance for Jaeger, Zipkin, and Grafana Tempo
Trace context propagation patterns (W3C Trace Context)
Advanced sampling strategies including tail-based and adaptive sampling
Observability correlation patterns for logs, metrics, and traces
OpenTelemetry instrumentation for automatic and manual tracing
12 GitHub stars

Use Cases

Identifying performance bottlenecks in complex distributed systems
Debugging intermittent cross-service errors and request timeouts
Implementing structured logging with trace and span ID correlation

About

Key Features

Backend selection guidance for Jaeger, Zipkin, and Grafana Tempo
Trace context propagation patterns (W3C Trace Context)
Advanced sampling strategies including tail-based and adaptive sampling
Observability correlation patterns for logs, metrics, and traces
OpenTelemetry instrumentation for automatic and manual tracing
12 GitHub stars

Use Cases

Identifying performance bottlenecks in complex distributed systems
Debugging intermittent cross-service errors and request timeouts
Implementing structured logging with trace and span ID correlation