distributed-tracing

npx skills add wshobson/agents --skill distributed-tracing --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

# 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) 7. **Set up alerts** for trace errors 8. **Implement distributed context** (baggage) 9. **Use span events** for important milestones 10. **Document instrumentation** standards ## Integration with Logging ### Correlated Logs ```python import logging from opentelemetry import trace logger = logging.getLogger(__name__) def process_request(): span = trace.ge