/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.
$ npx -y skills add aj-geddes/useful-ai-prompts --skill distributed-tracing --agent claude-codeHow it fires
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- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
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/distributed-tracing
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Implement distributed tracing with Jaeger and Zipkin for tracking requests across microservices. Use when debugging distributed systems, tracking request flows, or analyzing service performance.
SKILL.md
distributed-tracing.SKILL.mdname: distributed-tracing
description: >
Implement distributed tracing with Jaeger and Zipkin for tracking requests
across microservices. Use when debugging distributed systems, tracking request
flows, or analyzing service performance.
Distributed Tracing
Table of Contents
- [Overview](#overview)
- [When to Use](#when-to-use)
- [Quick Start](#quick-start)
- [Reference Guides](#reference-guides)
- [Best Practices](#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:
# 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:Reference Guides
Detailed implementations in the `references/` directory:
| Guide | Contents | |---|---| | [Jaeger Setup](references/jaeger-setup.md) | Jaeger Setup, Node.js Jaeger Instrumentation | | [Express Tracing Middleware](references/express-tracing-middleware.md) | Express Tracing Middleware | | [Python Jaeger Integration](references/python-jaeger-integration.md) | Python Jaeger Integration | | [Distributed Context Propagation](references/distributed-context-propagation.md) | Distributed Context Propagation | | [Zipkin Integration](references/zipkin-integration.md) | 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
Read more
name: distributed-tracing description: > Implement distributed tracing with Jaeger and Zipkin for tracking requests across microservices. Use when debugging distributed systems, tracking request flows, or analyzing service performance.
Distributed Tracing
Table of Contents
- [Overview](#overview)
- [When to Use](#when-to-use)
- [Quick Start](#quick-start)
- [Reference Guides](#reference-guides)
- [Best Practices](#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:
# 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:Reference Guides
Detailed implementations in the `references/` directory:
| Guide | Contents | |---|---| | [Jaeger Setup](references/jaeger-setup.md) | Jaeger Setup, Node.js Jaeger Instrumentation | | [Express Tracing Middleware](references/express-tracing-middleware.md) | Express Tracing Middleware | | [Python Jaeger Integration](references/python-jaeger-integration.md) | Python Jaeger Integration | | [Distributed Context Propagation](references/distributed-context-propagation.md) | Distributed Context Propagation | | [Zipkin Integration](references/zipkin-integration.md) | 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
488 production-ready AI prompts, all following a standardized template with validated quality gates. Transform ChatGPT, Claude, and other AI assistants into expert consultants.
Repo: aj-geddes/useful-ai-prompts
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