메뉴
Monitoring operations skill. For VictoriaMetrics metric queries, Prometheus PromQL analysis, AlertManager alert management, cluster health assessment, resource usage analysis, and performance bottleneck identification. 触发场景：指标查询、告警分析、集群巡检、资源使用率、性能问题、容量规划。
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
Infrastructure deployment guide. For Kubernetes cluster initialization, ArgoCD deployment, VictoriaMetrics monitoring stack deployment, VictoriaLogs logging stack deployment, kube-prometheus-stack metrics collection deployment. 触发场景：集群初始化、基础设施部署、监控系统搭建、日志系统搭建、ArgoCD GitOps 配置。
K3s lightweight Kubernetes cluster deployment and operations skill. Supports automatic K3s cluster deployment (Server + Agent nodes), cluster upgrade, certificate management, backup restore, troubleshooting, and daily maintenance. 触发场景：K3s 安装、K3s 部署、K3s 升级、K3s 维护、K3s 故障、轻量集群、边缘集群、 集群初始化、k3s server、k3s agent、集群搭建、节点加入。
Kubernetes cluster diagnostic skill. For Pod anomaly troubleshooting, node failure diagnosis, resource pressure analysis, CrashLoopBackOff, OOMKilled, Pending, Evicted and similar issue identification. 触发场景：Pod 重启、Pod 崩溃、节点 NotReady、资源不足、调度失败、健康检查失败、驱逐。
Log operations skill. For VictoriaLogs LogsQL queries, Pod log analysis, error pattern recognition, and log system operations. 触发场景：日志查询、日志分析、VictoriaLogs 运维、错误排查、日志模式、fluent-bit 配置。
Middleware deployment and operations skill. For Helm deployment, status monitoring, performance tuning, and troubleshooting of Redis, MySQL, Kafka, PostgreSQL and other middlewares. 触发场景：中间件部署、Redis 运维、MySQL 运维、Kafka 运维、数据库状态、缓存问题。
SOC 직업 분류 기준
| name | monitoring-ops |
| description | Monitoring operations skill. For VictoriaMetrics metric queries, Prometheus PromQL analysis, AlertManager alert management, cluster health assessment, resource usage analysis, and performance bottleneck identification. 触发场景：指标查询、告警分析、集群巡检、资源使用率、性能问题、容量规划。 |
| allowed-tools | query_prometheus_metric get_alerts get_cluster_metrics get_node_info get_k8s_components_status |
# Node status
kube_node_status_condition{condition="Ready",status="true"}
# Pod status counts
count(kube_pod_status_phase{phase="Running"})
count(kube_pod_status_phase{phase="Pending"})
count(kube_pod_status_phase{phase="Failed"})
# Node CPU usage
100 - (avg by(instance)(rate(node_cpu_seconds_total{mode="idle"}[5m])) * 100)
# Node memory usage
(1 - node_memory_MemAvailable_bytes / node_memory_MemTotal_bytes) * 100
# Pod CPU usage (based on Limit)
sum(rate(container_cpu_usage_seconds_total{container!=""}[5m])) by (pod, namespace)
/ sum(kube_pod_container_resource_limits{resource="cpu"}) by (pod, namespace) * 100
# Pod memory usage (based on Limit)
sum(container_memory_working_set_bytes{container!=""}) by (pod, namespace)
/ sum(kube_pod_container_resource_limits{resource="memory"}) by (pod, namespace) * 100
| Metric | Warning | Critical | Description |
|---|---|---|---|
| CPU usage | >70% | >85% | For 5 minutes |
| Memory usage | >75% | >90% | For 5 minutes |
| Disk usage | >80% | >90% | Single check |
| Pod restarts | >3 | >10 | Last 1 hour |
| PVC usage | >80% | >90% | Single check |
# API Server request latency P99
histogram_quantile(0.99, rate(apiserver_request_duration_seconds_bucket{verb!="WATCH"}[5m]))
# API Server error rate
sum(rate(apiserver_request_total{code=~"5.."}[5m])) / sum(rate(apiserver_request_total[5m])) * 100
# ETCD Leader changes
changes(etcd_server_is_leader[1h])
# ETCD database size
etcd_mvcc_db_total_size_in_bytes
# ETCD disk fsync latency
histogram_quantile(0.99, rate(etcd_disk_wal_fsync_duration_seconds_bucket[5m]))
# CPU trend forecast (7-day linear regression)
predict_linear(node_cpu_seconds_total{mode="idle"}[7d], 30*24*3600)
# Disk usage trend forecast
predict_linear(node_filesystem_avail_bytes[7d], 30*24*3600)