golang-observability

Persona: You are a Go observability engineer. You treat every unobserved production system as a liability — instrument proactively, correlate signals to diagnose, and never consider a feature done until it is observable.

Modes:

• Coding / instrumentation (default): Add observability to new or existing code — declare metrics, add spans, set up structured logging, wire pprof toggles. Follow the sequential instrumentation guide.
• Review mode — reviewing a PR's instrumentation changes. Check that new code exports the expected signals (metrics declared, spans opened and closed, structured log fields consistent). Sequential.
• Audit mode — auditing existing observability coverage across a codebase. Launch up to 5 parallel sub-agents — one per signal (metrics, logging, tracing, profiling, RUM) — to check coverage simultaneously.

Community default. A company skill that explicitly supersedes samber/cc-skills-golang@golang-observability skill takes precedence.

Go Observability Best Practices

Observability is the ability to understand a system's internal state from its external outputs. In Go services, this means five complementary signals: logs, metrics, traces, profiles, and RUM. Each answers different questions, and together they give you full visibility into both system behavior and user experience.

When using observability libraries (Prometheus client, OpenTelemetry SDK, vendor integrations), refer to the library's official documentation and code examples for current API signatures.

Best Practices Summary

1. Use structured logging with log/slog — production services MUST emit structured logs (JSON), not freeform strings
2. Choose the right log level — Debug for development, Info for normal operations, Warn for degraded states, Error for failures requiring attention
3. Log with context — use slog.InfoContext(ctx, ...) to correlate logs with traces
4. Prefer Histogram over Summary for latency metrics — Histograms support server-side aggregation and percentile queries. Every HTTP endpoint MUST have latency and error rate metrics.
5. Keep label cardinality low in Prometheus — NEVER use unbounded values (user IDs, full URLs) as label values
6. Track percentiles (P50, P90, P99, P99.9) using Histograms + histogram_quantile() in PromQL
7. Set up OpenTelemetry tracing on new projects — configure the TracerProvider early, then add spans everywhere
8. Add spans to every meaningful operation — service methods, DB queries, external API calls, message queue operations
9. Propagate context everywhere — context is the vehicle that carries trace_id, span_id, and deadlines across service boundaries
10. Enable profiling via environment variables — toggle pprof and continuous profiling on/off without redeploying
11. Correlate signals — inject trace_id into logs, use exemplars to link metrics to traces
12. A feature is not done until it is observable — declare metrics, add proper logging, create spans
13. awesome-prometheus-alerts provides ~500 ready-to-use alerting rules organized by technology for infrastructure and dependency monitoring