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logging-setup

Structured logging: slog/pino, Cloud Logging, Loki, correlation IDs, PII scrubbing, sampling, cost control.

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SebastianLuser/claude-code-arcane3 stars(GitHub 3 + cccskills 0)

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Files included (4)

  • SKILL.md3.6 KB
  • references/anti-patterns.md394 B
  • references/checklist.md506 B
  • references/pii-audit-access.md1.1 KB

SKILL.md (original)

Review the instructions the agent will receive before you install.

logging-setup — Structured Logging

Objetivo: debuggable en prod, barato a escala, sin PII.

Principios

  1. JSON estructurado siempre — nunca strings libres en prod
  2. Una línea = un evento — no multi-line/pretty-print
  3. Level + message + context en todo evento
  4. Correlation ID (trace_id, request_id, user_id, tenant_id) en cada línea
  5. Sin PII — scrubber en el logger
  6. Sampling en hot paths
  7. Logs ≠ errors ≠ metrics ≠ traces — cada uno su destino

Stack Decision

DestinoCuándo
GCP Cloud LoggingDefault Educabot (runtime GCP), $0.50/GiB ingest
Grafana LokiSelf-host, alto volumen barato
Datadog LogsSi ya pagan DD APM
ELK/OpenSearchCompliance/SIEM full-text

Default: Cloud Logging (runtime) + Loki (alto volumen/cheap retention).

Setup por Stack

Go (slog stdlib 1.21+): slog.NewJSONHandler con level configurable, ReplaceAttr para PII scrubbing, base fields (service, env, version). Middleware Gin inyecta request-scoped attrs (request_id, trace_id, route) en context.

Node/TS (pino): level via env, base fields, redact.paths para PII (password, token, authorization, cookie, dni, cuit), pinoHttp middleware con custom props (trace_id) y serializers compactos. Pino > winston: 5x más rápido, JSON nativo.

Cloud Logging fields: severity (DEBUG-CRITICAL), logging.googleapis.com/trace (auto-link a Cloud Trace), httpRequest (método/status/latency como columnas). Cloud Run/GKE: stdout se loguea automáticamente.

React/frontend: errores → Sentry, analytics → Mixpanel/Amplitude. Si necesitás logs centralizados: buffer + batch + flush a /api/logs, solo warn+ en prod. Evitar loguear interacciones rutinarias.

React Native: errores → Sentry, analytics → Mixpanel. Debug en campo: react-native-logs (archivos rotados), upload con consent. Nunca PII en logs del device.

Correlation / Trace IDs

OTel W3C Trace Context (traceparent). Todo log en un request con mismo trace_id. Go: trace.SpanFromContext(ctx).SpanContext().TraceID().

Log Levels

LevelUso
DEBUGFlujo interno. Off en prod salvo troubleshooting
INFOEventos normales. Default prod
WARNRecuperable ("retry 2/3"). Mirar tendencias
ERRORFalla operación, sistema sigue. → Sentry también
FATALSe muere el servicio. Raro. Page on-call

Si nadie va a hacer algo con la línea → bájala de level o sacala.

Sampling

Cloud Logging ~$0.50/GiB. Estrategia: siempre errores 100%, samplear éxitos (5-10%). Con OTel tracing, bajar INFO a 1-5%.

→ Read references/pii-audit-access.md for PII scrubbing fields, audit log schema, access log fields, and retention/cost tables

→ Read references/anti-patterns.md for common logging anti-patterns

→ Read references/checklist.md for the 12-item implementation checklist

Checklist

  • Log destination chosen and configured (Cloud Logging, Loki, Datadog, or ELK)
  • Correlation IDs (trace_id, request_id) injected in every log line via middleware
  • PII scrubbing in place (password, token, authorization, cookie, dni, cuit redacted)
  • Sampling strategy defined (errors 100%, success paths 5-10%)
  • Log levels set correctly per environment (DEBUG off in prod, INFO as default)
  • JSON structured format enforced — no free-form strings in production

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