structured-logging-and-observability
Use this skill when building production services, pipelines, or automation that needs to be debugged, monitored, or audited. Add structured logs, metrics, and health checks before shipping any service.
Datos de origen
- Repositorio
- aiming-lab/MetaClaw
- Última actividad en el origen
- 10 de marzo de 2026 a las 18:04
- Idioma detectado de SKILL.md
- inglés
- Estrellas
- 3464
- Forks
- 452
Opciones de instalación
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SKILL.md
Instrucciones de origen · Vista previa de solo lectura- name
- structured-logging-and-observability
- description
- Use this skill when building production services, pipelines, or automation that needs to be debugged, monitored, or audited. Add structured logs, metrics, and health checks before shipping any service.
- category
- automation
# Structured Logging and Observability
**Log levels:**
- `DEBUG`: detailed diagnostic (off in production)
- `INFO`: normal operation milestones
- `WARNING`: recoverable unexpected state
- `ERROR`: operation failed, action needed
**Structured logs (JSON) over free-form text:**
```python
import structlog
log = structlog.get_logger()
log.info("request_complete", method="POST", path="/api/data", status=200, latency_ms=42)
```
**Metrics to expose:** request rate, error rate, latency (p50/p95/p99), queue depth.
**Health check endpoint:** `/health` returning `{"status": "ok"}` — required for load balancers.
**Anti-pattern:** Logging only on error; you can't diagnose what you didn't observe.
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