Distributed Tracing

This gets you tracing infrastructure with Jaeger or Zipkin so you can follow requests bouncing between microservices. You'll use it when debugging why something's slow or figuring out where a request died in your service mesh. The skill covers both Jaeger and Zipkin setups with Docker Compose, plus instrumentation examples for Node.js and Python including the critical bit about propagating trace context between services. The best practices section is solid on sampling strategy, which matters because tracing everything in production will wreck your performance. It's straightforward observability plumbing that assumes you already have microservices that need connecting.

skills.sh installs: 418
GitHub stars: 379
last updated: Aug 21, 2026

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Install

$ npx -y skills add aj-geddes/useful-ai-prompts --skill distributed-tracing --agent claude-code

Installs into .claude/skills of the current project.

Files

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:

# docker-compose.yml
version: "3.8"
services:
  jaeger:
    image: jaegertracing/all-in-one:latest
    ports:
      - "5775:5775/udp"
      - "6831:6831/udp"
      - "16686:16686"
      - "14268:14268"
    networks:
      - tracing

networks:
  tracing: