/distributed-tracing
Implement distributed tracing with Jaeger and Tempo to track requests across microservices and identify performance bottlenecks. Use when debugging microservices, analyzing request flows, or implementing observability for distributed systems.
One skill from wshobson-agents.
shell
$ npx -y skills add wshobson/agents --skill distributed-tracing --agent claude-codeInstalls just this skill. Get the whole plugin for auto-invocation.
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- Fires itselfClaude auto-loads it when your prompt matches the work.
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/distributed-tracing
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Implement distributed tracing with Jaeger and Tempo to track requests across microservices and identify performance bottlenecks. Use when debugging microservices, analyzing request flows, or implementing observability for distributed systems.
Stats
Stars38,174
Forks4,092
LanguagePython
LicenseMIT
Ships with wshobson-agents
SKILL.md
distributed-tracing.SKILL.md
--- name: distributed-tracing description: Implement distributed tracing with Jaeger and Tempo to track requests across microservices and identify performance bottlenecks. Use when debugging microservices, analyzing request flows, or implementing observability for distributed systems. --- # Distributed Tracing Implement distributed tracing with Jaeger and Tempo for request flow visibility across microservices. ## Purpose Track requests across distributed systems to understand latency, dependencies, and failure points. ## When to Use - Debug latency issues - Understand service dependencies - Identify bottlenecks - Trace error propagation - Analyze request paths ## Detailed patterns and worked examples Detailed pattern documentation lives in `references/details.md`. Read that file when the navigation tier above is insufficient. ## Best Practices 1. **Sample appropriately** (1-10% in production) 2. **Add meaningful tags** (user_id, request_id) 3. **Propagate context** across all service boundaries 4. **Log exceptions** in spans 5. **Use consistent naming** for operations 6. **Monitor tracing overhead** (<1% CPU impact)
