菜单
Go observability — always-on production signals: slog logging, Prometheus metrics, OpenTelemetry tracing, pprof profiling, alerting, Grafana. Apply when instrumenting Go services or migrating zap/logrus/zerolog to slog. Not for performance deep-dives (→ golang-benchmark, golang-performance).
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/context-labs/whip --skill golang-observability命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
基于 SOC 职业分类
正在显示 SKILL.md
| name | golang-observability |
| description | Go observability — always-on production signals: slog logging, Prometheus metrics, OpenTelemetry tracing, pprof profiling, alerting, Grafana. Apply when instrumenting Go services or migrating zap/logrus/zerolog to slog. Not for performance deep-dives (→ golang-benchmark, golang-performance). |
| user-invocable | true |
| license | MIT |
| compatibility | Designed for Claude Code, Codex or similar harness, and for projects using Golang. |
| metadata | {"author":"samber","version":"1.3.0","openclaw":{"emoji":"📡","homepage":"https://github.com/samber/cc-skills-golang","requires":{"bins":"[Truncated]"},"install":[]}} |
| allowed-tools | Read Edit Write Glob Grep Bash(go:*) Bash(golangci-lint:*) Bash(git:*) Agent WebFetch WebSearch AskUserQuestion |
| paths | ["**/*.go"] |
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.
Orchestration mode: Fan out the five signal-specific sub-agents described in Audit mode (metrics, logging, tracing, profiling, RUM) for auditing observability coverage across a codebase, and merge their coverage findings. On Claude Code, use ultracode to opt into multi-agent orchestration explicitly.
Modes:
Community default. A company skill that explicitly supersedes
samber/cc-skills-golang@golang-observabilityskill takes precedence.
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.
log/slog — production services MUST emit structured logs (JSON), not freeform stringsslog.InfoContext(ctx, ...) to correlate logs with traceshistogram_quantile() in PromQLSee samber/cc-skills-golang@golang-error-handling skill for the single handling rule. See samber/cc-skills-golang@golang-troubleshooting skill for using observability signals to diagnose production issues. See samber/cc-skills-golang@golang-security skill for protecting pprof endpoints and avoiding PII in logs. See samber/cc-skills-golang@golang-context skill for propagating trace context across service boundaries. See samber/cc-skills@promql-cli skill for querying and exploring PromQL expressions against Prometheus from the CLI.
For simple fan-out to multiple slog handlers, prefer stdlib slog.NewMultiHandler before adding third-party handler-composition dependencies.
logger := slog.New(slog.NewMultiHandler(
slog.NewJSONHandler(os.Stdout, nil),
auditHandler,
))
Use third-party slog handler libraries only when the stdlib handler composition is insufficient.
| Signal | Question it answers | Tool | When to use |
|---|---|---|---|
| Logs | What happened? | log/slog | Discrete events, errors, audit trails |
| Metrics | How much / how fast? | Prometheus client | Aggregated measurements, alerting, SLOs |
| Traces | Where did time go? | OpenTelemetry | Request flow across services, latency breakdown |
| Profiles | Why is it slow / using memory? | pprof, Pyroscope | CPU hotspots, memory leaks, lock contention |
| RUM | How do users experience it? | PostHog, Segment | Product analytics, funnels, session replay |
Each signal has a dedicated guide with full code examples, configuration patterns, and cost analysis:
Structured Logging — Why structured logging matters for log aggregation at scale. Covers log/slog setup, log levels (Debug/Info/Warn/Error) and when to use each, request correlation with trace IDs, context propagation with slog.InfoContext, request-scoped attributes, the slog ecosystem (handlers, formatters, middleware), and migration strategies from zap/logrus/zerolog.
Metrics Collection — Prometheus client setup and the four metric types (Counter for rate-of-change, Gauge for snapshots, Histogram for latency aggregation). Deep dive: why Histograms beat Summaries (server-side aggregation, supports histogram_quantile PromQL), naming conventions, the PromQL-as-comments convention (write queries above metric declarations for discoverability), production-grade PromQL examples, multi-window SLO burn rate alerting, and the high-cardinality label problem (why unbounded values like user IDs destroy performance).
Distributed Tracing — When and how to use OpenTelemetry SDK to trace request flows across services. Covers spans (creating, attributes, status recording), otelhttp middleware for HTTP instrumentation, error recording with span.RecordError(), trace sampling (why you can't collect everything at scale), propagating trace context across service boundaries, and cost optimization.
Profiling — On-demand profiling with pprof (CPU, heap, goroutine, mutex, block profiles) — how to enable it in production, secure it with auth, and toggle via environment variables without redeploying. Continuous profiling with Pyroscope for always-on performance visibility. Cost implications of each profiling type and mitigation strategies.
Real User Monitoring — Understanding how users actually experience your service. Covers product analytics (event tracking, funnels), Customer Data Platform integration, and critical compliance: GDPR/CCPA consent checks, data subject rights (user deletion endpoints), and privacy checklist for tracking. Server-side event tracking (PostHog, Segment) and identity key best practices.
Alerting — Proactive problem detection. Covers the four golden signals (latency, traffic, errors, saturation), awesome-prometheus-alerts provides ~500 ready-to-use rules by technology, Go runtime alerts (goroutine leaks, GC pressure, OOM risk), severity levels, and common mistakes that break alerting (using irate instead of rate, missing for: duration to avoid flapping).
Grafana Dashboards — Prebuilt dashboards for Go runtime monitoring (heap allocation, GC pause frequency, goroutine count, CPU). Explains the standard dashboards to install, how to customize them for your service, and when each dashboard answers a different operational question.
Signals are most powerful when connected. A trace_id in your logs lets you jump from a log line to the full request trace. An exemplar on a metric links a latency spike to the exact trace that caused it.
otelslog bridgeimport "go.opentelemetry.io/contrib/bridges/otelslog"
// Create a logger that automatically injects trace_id and span_id
logger := otelslog.NewHandler("my-service")
slog.SetDefault(slog.New(logger))
// Now every slog call with context includes trace correlation
slog.InfoContext(ctx, "order created", "order_id", orderID)
// Output includes: {"trace_id":"abc123", "span_id":"def456", "msg":"order created", ...}
// When recording a histogram observation, attach the trace_id as an exemplar
// so you can jump from a P99 spike directly to the offending trace
obs := histogram.WithLabelValues("POST", "/orders")
if eo, ok := obs.(prometheus.ExemplarObserver); ok {
eo.ObserveWithExemplar(duration, prometheus.Labels{"trace_id": traceID})
} else {
obs.Observe(duration)
}
If the project currently uses zap, logrus, or zerolog, migrate to log/slog. It is the standard library logger since Go 1.21, has a stable API, and the ecosystem has consolidated around it. Continuing with third-party loggers means maintaining an extra dependency for no benefit.
Migration strategy:
slog as the new logger with slog.SetDefault()zap.L().Info(...) / logrus.Info(...) / log.Info().Msg(...) calls with slog.Info(...)A feature is not production-ready until it is observable. Before marking a feature as done, verify:
slog, context variants used (slog.InfoContext), no PII in logs, errors MUST be either logged OR returned (NEVER both).span.RecordError().user_id (not email), consent checked before tracking.// ✗ Bad — log AND return (error gets logged multiple times up the chain)
if err != nil {
slog.Error("query failed", "error", err)
return fmt.Errorf("query: %w", err)
}
// ✓ Good — return with context, log once at the top level
if err != nil {
return fmt.Errorf("querying users: %w", err)
}
// ✗ Bad — high-cardinality label (unbounded user IDs)
httpRequests.WithLabelValues(r.Method, r.URL.Path, userID).Inc()
// ✓ Good — bounded label values only
httpRequests.WithLabelValues(r.Method, routePattern).Inc()
// ✗ Bad — not passing context (breaks trace propagation)
result, err := db.Query("SELECT ...")
// ✓ Good — context flows through, trace continues
result, err := db.QueryContext(ctx, "SELECT ...")
// ✗ Bad — using Summary for latency (can't aggregate across instances)
prometheus.NewSummary(prometheus.SummaryOpts{
Name: "http_request_duration_seconds",
Objectives: map[float64]float64{0.99: 0.001},
})
// ✓ Good — use Histogram (aggregatable, supports histogram_quantile)
prometheus.NewHistogram(prometheus.HistogramOpts{
Name: "http_request_duration_seconds",
Buckets: prometheus.DefBuckets,
})