Agent skill · data analytics · aj-geddes
distributed-tracing
Implement distributed tracing with Jaeger and Zipkin for tracking requests across microservices. Use when debugging distributed systems, tracking request flows, or analyzing service performance.
Why this skill is useful
Provides specific setup instructions and best practices for implementing distributed tracing with Jaeger and Zipkin, which the AI wouldn't reliably generate on its own.
What it needs
Requires docker installed locally. About 1k tokens when loaded. Last updated 2026-03-04. 307 stars on the source repository.
What this skill does
Distributed Tracing Table of Contents Overview When to Use Quick Start Reference Guides Best Practices Overview Set up distributed tracing infrastructure with Jaeger or Zipkin to track requests across microservices and identify performance bottlenecks. When to Use Debugging microservice interactions Identifying performance bottlenecks Tracking request flows Analyzing service dependencies Root cause analysis Quick Start Minimal working example: Reference Guides Detailed implementations in the references/ directory: Guide Contents --- --- Jaeger Setup Jaeger Setup, Node.js Jaeger Instrumentation Express Tracing Middleware Express Tracing Middleware Python Jaeger Integration Python Jaeger Integration Distributed Context Propagation Distributed Context Propagation Zipkin Integration Zipkin Integration, Trace Analysis Best Practices ✅ DO Sample appropriately for your traffic volume Propagate trace context across services Add meaningful span tags Log errors with spans Use consistent service naming Monitor trace latency Document trace format Keep instrumentation lightweight ❌ DON'T Sample 100% in production Skip trace context propagation Log sensitive data in spans Create excessive spans Ignore sampling configuration Use unbounded cardinality tags Deploy without testing collection
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aj-geddes/distributed-tracing