Agent skill · data analytics · aj-geddes
correlation-tracing
Implement distributed tracing with correlation IDs, trace propagation, and span tracking across microservices. Use when debugging distributed systems, monitoring request flows, or implementing observability.
Why this skill is useful
Provides specific implementation patterns and best practices for distributed tracing that the AI wouldn't reliably generate on its own.
What it needs
Requires async_hooks, express, uuid installed locally. About 1k tokens when loaded. Last updated 2026-03-04. 307 stars on the source repository.
What this skill does
Correlation & Distributed Tracing Table of Contents Overview When to Use Quick Start Reference Guides Best Practices Overview Implement correlation IDs and distributed tracing to track requests across multiple services and understand system behavior. When to Use Microservices architectures Debugging distributed systems Performance monitoring Request flow visualization Error tracking across services Dependency analysis Latency optimization Quick Start Minimal working example: Reference Guides Detailed implementations in the references/ directory: Guide Contents --- --- Correlation ID Middleware (Express) Correlation ID Middleware (Express) OpenTelemetry Integration OpenTelemetry Integration Python Distributed Tracing Python Distributed Tracing Manual Trace Propagation Manual Trace Propagation Best Practices ✅ DO Generate trace IDs at entry points Propagate trace context across services Include correlation IDs in logs Use structured logging Set appropriate span attributes Sample traces in high-traffic systems Monitor trace collection overhead Implement context propagation ❌ DON'T Skip trace propagation Log without correlation context Create too many spans Store sensitive data in spans Block on trace reporting Forget error tracking
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aj-geddes/correlation-tracing