Observability Assistant | 可觀測性助手

SkillMonitoring & ops

Guide observability setup, metrics design, and alerting configuration. Use when: new service instrumentation, SLO definition, alert design, maturity assessment. Not for: setting numeric targets and Error Budget policy — use /slo; log format and levels — use /logging-guide. Keywords: observability, metrics, traces, golden signals, alerting, SLO.

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the Observability Assistant | 可觀測性助手 skill

What this skill tells your AI

The instructions your AI receives, as published by asiaostrich/universal-dev-standards in skills/observability-assistant/SKILL.md and read by ahel’s review.

Language: English | 繁體中文

Version: 1.0.0 Last Updated: 2026-06-19 Applicability: Claude Code Skills

Core Standard: This skill implements Observability Standards. For the authoritative methodology (full Metrics/Traces detail, sampling, OTel integration), refer to the core standard.

Guide observability implementation across the three pillars: Logs, Metrics, and Traces.

引導三支柱可觀測性實作:Logs、Metrics、Traces。

Capabilities | 功能

CapabilityDescription說明
Instrumentation CheckPre-launch observability checklist上線前可觀測性檢查表
Maturity AssessmentL0-L4 maturity self-evaluationL0-L4 成熟度自評
Metric DesignHelp design metrics (type, naming, labels)協助設計 Metrics
Alert DesignDesign SLO-based alerts with noise reduction設計 SLO-based 告警
Golden SignalsVerify 4 golden signals coverage驗證四大黃金信號覆蓋

Usage | 使用方式

/observability                        # Show observability guide
/observability --checklist            # Run instrumentation checklist
/observability --maturity             # Maturity assessment (L0-L4)
/observability --alerting             # Alert design guide
/observability "payment-service"      # Guide for specific service

Three Pillars Framework | 三支柱框架

Each pillar gives a different lens; their power is in correlation.

PillarWhat It CapturesWhen to UseGranularity
LogsDiscrete events with contextDebugging, audit trails, error detailsHigh (per-event)
MetricsNumerical measurements over timeDashboards, alerting, capacity planningLow (aggregated)
TracesRequest flow across servicesLatency analysis, dependency mappingMedium (per-request)

Correlation fields: trace_id links Logs ↔ Traces ↔ Metrics (via Exemplars); service.name filters all three pillars. Workflow: metric anomaly → exemplar → trace → trace_id in logs.

Golden Signals | 四大黃金信號

Based on Google SRE. Every service SHOULD monitor all four before production.

SignalMeasureExample MetricAlert (SLO-based)
LatencyP50/P95/P99 via Histogram, split success/errorhttp.server.request.duration.secondsP99 > X ms for 5 min
TrafficRequests/sec, by route/methodhttp.server.request.total (rate)drop > 50% or spike > 200%
Errorserrors / total requests...request.total{status=~"5.."} ÷ totalerror rate > X% for 5 min
SaturationCPU/mem/pool/disk utilizationsystem.cpu.utilization (Gauge)resource > 80% for 10 min

Metric Types | Metric 型別

TypeBehaviorUse When
CounterOnly goes up (resets on restart)request count, error count, bytes sent
GaugePoint-in-time, up/downqueue depth, active connections, memory
HistogramDistribution across bucketsrequest duration, response size
SummaryClient-computed percentileslegacy, no server-side aggregation

Naming: <domain>.<entity>.<action>.<unit> in snake_case (e.g. db.client.query.duration.seconds). Label cardinality: keep labels under ~1000 unique values — never use user_id / request_id / raw url / ip as labels; record those in Logs or Traces instead.

Maturity Model (L0–L4) | 成熟度模型

LevelNameCharacteristicsUpgrade Action
L0No Observabilityonly stdout/stderr; debug via SSH + tail -fstructured logging; centralize collection
L1Basic Loggingstructured JSON logs, centralized, searchableadd business metrics; first dashboard
L2Metrics-DrivenLogs + Metrics, dashboards, threshold alertsenable tracing; SLO-based alerting
L3Full Observabilitythree pillars + correlation + SLO alerts + Golden Signalsanomaly detection; auto-remediation
L4IntelligentAIOps anomaly detection, predictive alerts, auto-remediationmaintain, optimize, share learnings

Self-check: find logs for one request across services (L1+) → dashboards of error rate & latency (L2+) → trace request ingress→DB→back (L3+) → auto-detect anomalies before users report (L4).

Instrumentation Checklist | 上線前檢查表

Before deploying a service to production:

  • Structured logging — JSON with trace_id correlation
  • HTTP/gRPC entry metrics — request count, duration histogram, error rate per endpoint
  • Business operation metrics — key events tracked (orders, payments)
  • Distributed tracing — span propagation, sampling rate, W3C Trace Context headers
  • Health checks — liveness (running?) and readiness (can serve?) probes
  • Golden Signals dashboard — Latency, Traffic, Errors, Saturation visualized
  • Alert rules — at minimum SLO burn-rate alerts
  • Log retention — policies set per log level
  • Sensitive data excluded — no passwords, tokens, or PII in logs/traces

Next Steps Guidance | 下一步引導

可觀測性引導完成。建議下一步:

  • 執行 /slo 定義 SLI/SLO/Error Budget ⭐ 推薦
  • 執行 /incident 設定事故回應流程
  • 執行 /checkin 提交變更

Reference | 參考

Signals

GitHub stars
73
Forks
13
Last commit
Sep 2026
Advanced
Catalog kind
skill
Gateway key
observability-asiaostrich
Source
github.com/asiaostrich/universal-dev-standards