distributed-tracing
by Seth Hobson
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.
Labels from its files: instructions only. 2 files, read Oct 21, 2026. Nothing was run.
npx skills add https://github.com/wshobson/agents --skill distributed-tracingWhat it lets an agent do
Each label, and the line in the skill's files that set it.
- Instructions only
Text only: no shell, no file writes, no scripts.
- No shell, no file writes, no network and no scripts found.
Findings
Matches of fixed checks, with the text they matched.
No check matched anything in this skill's files.
Not a safety rating, and no finding is not a safety guarantee. These are fixed checks of the files as read on Oct 21, 2026. Nothing was run. How skills are checked.
SKILL.md
As the publisher wrote it. Shown as text, never rendered.
---
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