视觉画布设计
canvas-design
基于设计原则创作静态视觉作品,用于海报与品牌图形。
write-incident-runbook【文档与办公】Create structured incident runbooks with diagnostic steps, resolution procedures, escalation paths, and communication templates for effective incident response. Use when documenting response procedures for recurring alerts, standardizing incident response across an on-call rotation, reducing MTTR with clear diagnostic steps, creating training materia…
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立可行行冊以引應者過診與解。
全模檔見 Extended Examples。
按事故型與複擇模。
基行冊模構:
# [Alert/Incident Name] Runbook
## Overview | Severity | Symptoms
## Diagnostic Steps | Resolution Steps
## Escalation | Communication | Prevention | Related
進 SRE 行冊模(節):
# [Service Name] - [Incident Type] Runbook
## Metadata
- Service, Owner, Severity, On-Call, Last Updated
## Diagnostic Phase
### Quick Health Check (< 5 min): Dashboard, error rate, deployments
### Detailed Investigation (5-20 min): Metrics, logs, traces, failure patterns
# ... (see EXAMPLES.md for complete template)
關模件:
得:擇模合事故複,節宜服型。
敗:
全診詢與決樹見 Extended Examples。
立步步究程附特詢。
六步診清單:
驗服健:健端察與運時度
curl -I https://api.example.com/health # Expected: HTTP 200 OK
up{job="api-service"} # Expected: 1 for all instances
察誤率:今誤百與按端分
sum(rate(http_requests_total{status=~"5.."}[5m]))
/ sum(rate(http_requests_total[5m])) * 100 # Expected: < 1%
析日:近誤與 Loki 頂誤訊
{job="api-service"} |= "error" | json | level="error"
察資用:CPU、記、連池
avg(rate(container_cpu_usage_seconds_total{pod=~"api-service.*"}[5m])) * 100
# Expected: < 70%
審近變:部署、git 提、設變
察依:下游服健、庫/API 延
敗式決樹(節):
得:診程具體、含期 vs 實值、引應者究。
敗:
五解選之全命與還程見 Extended Examples。
錄步步補與還選。
五解選(簡摘):
還部署(最速):部署後誤
kubectl rollout undo deployment/api-service
驗→察→確解(誤率 < 1%、延正、無警)
擴資:CPU/記高、連池竭
kubectl scale deployment/api-service --replicas=$((current * 3/2))
重啟服:記漏、連黏、緩污
kubectl rollout restart deployment/api-service
特旗/斷器:特能誤或外依敗
kubectl set env deployment/api-service FEATURE_NAME=false
庫補:庫連、慢詢、池竭
-- Kill long-running queries, restart connection pool, increase pool size
通驗清單:
還程:解惡況→暫/取→還→重評
得:解步清、含驗察、各行附還選。
敗:
全升級與聯目模見 Extended Examples。
定何時何升事故。
即升時:
五升級:
升程:
聯目:附角、Slack、電、PagerDuty 表予:
得:升標清、聯易得、升路合機構構。
敗:
全內外模附全式見 Extended Examples。
予預書訊為事故更。
內模(Slack #incident-response):
初告:
🚨 INCIDENT: [Title] | Severity: [Critical/High/Medium]
Impact: [users/services] | Owner: @username | Dashboard: [link]
Quick Summary: [1-2 sentences] | Next update: 15 min
進更(每 15-30 分):
📊 UPDATE #N | Status: [Investigating/Mitigating/Monitoring]
Actions: [what we tried and outcomes]
Theory: [what we think is happening]
Next: [planned actions]
減畢:
✅ MITIGATION | Metrics: Error [before→after], Latency [before→after]
Root Cause: [brief or "investigating"] | Monitoring 30min before resolved
解:
🎉 RESOLVED | Duration: [time] | Root Cause + Impact + Follow-up actions
偽警:無影、無隨
外模(態頁):
用信模:時線、影述、解、防、補(若應)
得:模省事故時、確一通、減應者認負。
敗:
全 Prometheus 警設與 Grafana 表板 JSON 見 Extended Examples。
整行冊於警與表板。
Prometheus 警加行冊鏈:
- alert: HighErrorRate
annotations:
runbook_url: "https://wiki.example.com/runbooks/high-error-rate"
dashboard_url: "https://grafana.example.com/d/service-overview"
incident_channel: "#incident-platform"
行冊嵌速診鏈:
建 Grafana 表板面附行冊鏈(markdown 面列諸事故行冊附 on-call 與升信)
得:應者可由警或表板直訪行冊、診詢預填、一擊訪關工。
敗:
configure-alerting-rules - 鏈行冊於警註以事故時即訪build-grafana-dashboards - 嵌行冊鏈於表板與診面setup-prometheus-monitoring - 行冊程中含 Prometheus 詢define-slo-sli-sla - 事故嚴分中參 SLO 影