Distributed Tracing

When to use this skill

Use Distributed Tracing when an AI agent needs a reusable SKILL.md workflow for this job: 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.

When not to use it

Skip Distributed Tracing when the task is outside the analysis category, or when a more specific skill in this directory already covers the same workflow with clearer triggers.

How to install

1. Personal install: create ~/.claude/skills/distributed-tracing/SKILL.md (and any bundled scripts) so Claude Code, Claude Desktop, and compatible agents can load it in every project.
2. Project install: commit the same folder at .claude/skills/distributed-tracing/ so teammates get the skill with the repo.
3. Restart the agent session after copying files so it re-scans the skills directory, then ask for the task in words that match the skill description.

Full install guide for Claude, Cursor, and Codex

What this skill does

# 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.get_current_span() trace_id = span.get_span_context().trace_id

logger.info( "Processing request", extra={"trace_id": format(trace_id, '032x')} ) ```

## Troubleshooting

**No traces appearing:**

- Check collector endpoint - Verify network connectivity - Check sampling configuration - Review application logs

**High latency overhead:**

- Reduce sampling rate - Use batch span processor - Check exporter configuration

## Related Skills

- `prometheus-configuration` - For metrics - `grafana-dashboards` - For visualization - `slo-implementation` - For latency SLOs

Intended uses

• Debug latency issues
• Understand service dependencies
• Identify bottlenecks
• Trace error propagation
• Analyze request paths