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.
- Installs
- 10.8K
- Last 24h
- +30
- Trending rank
- #4360
- Security
- Pass
Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/wshobson/agents --skill distributed-tracing- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads distributed-tracing automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
Security audits
Independent scans of the skill's instructions and bundled files.
SKILL.md
The instructions your agent reads when the skill is triggered.
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
- Sample appropriately (1-10% in production)
- Add meaningful tags (user_id, request_id)
- Propagate context across all service boundaries
- Log exceptions in spans
- Use consistent naming for operations
- Monitor tracing overhead (<1% CPU impact)
- Set up alerts for trace errors
- Implement distributed context (baggage)
- Use span events for important milestones
- 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 metricsgrafana-dashboards- For visualizationslo-implementation- For latency SLOs
FAQ
What does the distributed-tracing skill do?
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.
How do I install distributed-tracing?
Run npx skills add https://github.com/wshobson/agents --skill distributed-tracing in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/distributed-tracing/ (personal) or .claude/skills/distributed-tracing/ (project).
Which AI agents can use distributed-tracing?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is distributed-tracing safe to install?
Security partners have reviewed it: Gen Agent Trust Hub passed; Socket passed; Snyk passed; Runlayer passed; ZeroLeaks passed. As with any skill, read the SKILL.md and bundled scripts before installing.
Who maintains distributed-tracing?
It is published by wshobson in wshobson/agents. It has 10,826 recorded installs.
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