# 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.

Source: https://skillsagentes.com/skills/wshobson/agents/distributed-tracing
Repository: https://github.com/wshobson/agents
Author: wshobson
License: MIT
Updated: hace 2 meses
Context cost: 61 tok installed, 526 tok once triggered, 2.6k tok with every bundled file
Bundle: 2 files, 10 KB
Permissions requested: none declared

## Install

```bash
npx -y skills add wshobson/agents --skill distributed-tracing --agent claude-code
```

## What it does

- Implementa tracing distribuido con Jaeger y Tempo para seguir solicitudes entre microservicios
- Añade contexto correlacionado a logs usando trace_id de OpenTelemetry
- Provee buenas prácticas de sampling, tagging y propagación de contexto entre servicios
- Ofrece guía de troubleshooting para trazas faltantes o alta sobrecarga de latencia

## Use it when

- Depurar problemas de latencia
- Entender dependencias entre servicios
- Identificar cuellos de botella
- Analizar rutas de solicitudes o propagación de errores

## What triggers it

- "Ayúdame a implementar distributed tracing con Jaeger en mis microservicios"
- "¿Cómo configuro Tempo para rastrear solicitudes entre servicios?"
- "No aparecen trazas en mi collector, ayúdame a depurarlo"
- "Quiero correlacionar mis logs con trace_id usando OpenTelemetry"

## Before you install

- Requiere una infraestructura de Jaeger o Tempo con un collector configurado, además de instrumentación con OpenTelemetry.

## Files

- SKILL.md — 2 KB
- references/details.md — 8 KB

## SKILL.md

Reproduced verbatim from wshobson/agents under MIT. This section is the upstream document and is in English.

# 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

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Skills Agentes — https://skillsagentes.com/skills/wshobson/agents/distributed-tracing