What problem does it solve? Go services shipped without observability are impossible to diagnose in production. This Skill guides AI coding agents to instrument Go applications with the five production signals — structured logs, Prometheus metrics, OpenTelemetry traces, pprof/Pyroscope profiles, and RUM events — so every feature is monitorable before it ships. ## Core Features & Use Cases - Structured logging with slog: JSON handlers for production, context-aware log calls for trace correlation, and incremental migration from zap/logrus/zerolog via bridge handlers. - Metrics and alerting: Prometheus metric declarations with PromQL-as-comments conventions, histogram-based percentile tracking, multi-window burn-rate SLO alerts, and Go runtime alert rules. - Tracing and profiling: OpenTelemetry span instrumentation, otelslog log-trace correlation, exemplars linking metrics to traces, and environment-toggled pprof/Pyroscope continuous profiling. - Use Case: When adding a new HTTP endpoint to a Go microservice, use this Skill to declare latency histograms and error counters, add spans around DB queries, emit structured logs with trace IDs, and wire the PromQL into Grafana dashboards and alert rules. ## Quick Start Ask the agent to add production observability — metrics, structured logging, and tracing — to your Go service's HTTP handlers and database calls.
