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.
Install with the open skills CLI (global, non-interactive — available in every Claude Code session):
npx skills add wshobson/agents --skill "distributed-tracing" -g -a claude-code -yOr manually — clone and copy the skill directory (SKILL.md + companion files):
git clone --depth 1 https://github.com/wshobson/agents /tmp/agents && cp -r /tmp/agents/plugins/observability-monitoring/skills/distributed-tracing ~/.claude/skills/distributed-tracing-wshobsonThis skill is a directory: SKILL.md is the entry point; the files below ship with it.
---
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)
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
Use when facing 2+ independent tasks that can be worked on without shared state or sequential dependencies
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes
Use when implementing any feature or bugfix, before writing implementation code