What is the main benefit of using the correlation-tracing skill?

It allows you to track a single request as it moves through multiple services, making it easier to debug errors and identify performance bottlenecks in distributed systems.

How do correlation IDs improve logging?

By attaching a unique ID to every log entry associated with a specific request, you can filter logs from different services to see the complete lifecycle of that request in a central logging tool.

Does this skill support OpenTelemetry?

Yes, the skill includes implementation patterns for OpenTelemetry integration in both Node.js and Python, as well as manual propagation methods.

Can I use this for performance monitoring?

Absolutely. By implementing spans, you can measure the exact duration of specific operations and identify which service or dependency is causing latency in your system.

Correlation Tracing & Observability

Name: Correlation Tracing & Observability
Author: aj-geddes

byaj-geddes

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分析と監視

Implements distributed tracing with correlation IDs and span tracking to monitor request flows across microservices.

概要

This skill equips Claude with the ability to implement comprehensive distributed tracing and correlation strategies across complex microservice architectures. It provides patterns for generating unique trace IDs at system entry points, propagating context through HTTP headers, and instrumenting code with spans using tools like OpenTelemetry or custom middleware. It is particularly valuable for developers looking to debug latency issues, visualize request lifecycles, and unify logs across multiple disparate services to improve system observability and troubleshooting efficiency.

主な機能

Correlation ID generation and management for log unification
OpenTelemetry integration for industry-standard observability
Span tracking for granular performance and latency analysis
18 GitHub stars
Context propagation across microservices via HTTP headers
Multi-language implementation patterns for Node.js and Python

ユースケース

Visualizing performance bottlenecks and high-latency spans in distributed systems
Debugging request failures that span multiple downstream services
Aggregating logs from different services into a single cohesive trace

概要

主な機能

Correlation ID generation and management for log unification
OpenTelemetry integration for industry-standard observability
Span tracking for granular performance and latency analysis
18 GitHub stars
Context propagation across microservices via HTTP headers
Multi-language implementation patterns for Node.js and Python

ユースケース

Visualizing performance bottlenecks and high-latency spans in distributed systems
Debugging request failures that span multiple downstream services
Aggregating logs from different services into a single cohesive trace