Mmcp.market

distributed-tracing skill

by wshobson·wshobson/agents·40k stars·MIT

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.

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Install the distributed-tracing skill

A skill is a folder. Copy it into your agent's skills folder and the agent loads it when the task matches its description. 

git clone --depth 1 https://github.com/wshobson/agents.git /tmp/agents
mkdir -p ~/.claude/skills
cp -r /tmp/agents/plugins/observability-monitoring/skills/distributed-tracing ~/.claude/skills/distributed-tracing
available in every project

In the Claude apps, zip the folder and upload it from the Skills settings. The folder on GitHub

The instructions your agent would load

SKILL.md as published, without the frontmatter. Read it on GitHub

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 (userid, requestid)
  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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