/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.
Claude Code
$ npx -y skills add wshobson/agents --skill distributed-tracing --agent claude-code
Codex
$ npx skills add wshobson/agents --skill distributed-tracing -a codex -y
Cursor
$ npx skills add wshobson/agents --skill distributed-tracing -a cursor -y
Gemini CLI
$ npx skills add wshobson/agents --skill distributed-tracing -a gemini-cli -y
GitHub Copilot
$ npx skills add wshobson/agents --skill distributed-tracing -a github-copilot -y
OpenCode
$ npx skills add wshobson/agents --skill distributed-tracing -a opencode -y
Windsurf
$ npx skills add wshobson/agents --skill distributed-tracing -a windsurf -y
SKILL.md
distributed-tracing.SKILL.mdname: 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
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
Read more
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
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
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Questions about distributed-tracing.
- Which coding agents can use distributed-tracing?
- distributed-tracing installs in Claude Code, Codex, Cursor, Gemini CLI, GitHub Copilot, OpenCode and Windsurf with the skills CLI, using each agent's flag shown on this page. Flowy's auto-invocation, which fires a skill when your prompt matches, runs in Claude Code only.

