Kai

# ヘルスチェック
@kai check EKS cluster health and pod distribution

# リソース使用率
@kai analyze cluster resource utilization and identify bottlenecks

# ノード分析
@kai identify nodes with <30% CPU utilization for consolidation

# マルチクラスタービュー
@kai provide health summary across all Kubernetes clusters

# Pod の適正サイズ調整
@kai analyze pod resource requests/limits and recommend right-sizing

# スケジューリング効率
@kai identify pods with resource requests far exceeding actual usage

# コスト最適化
@kai identify underutilized nodes and recommend consolidation strategy

# 名前空間分析
@kai analyze resource allocation across namespaces

# HPA レビュー
@kai review Horizontal Pod Autoscaler policies and recommend improvements

# スケーリング分析
@kai analyze scaling patterns and recommend threshold adjustments

# VPA 評価
@kai evaluate whether Vertical Pod Autoscaler would benefit our workloads

# クラスターオートスケーリング
@kai review Cluster Autoscaler configuration for cost efficiency

# クラッシュ調査
@kai investigate pod crash loops in payment namespace

# OOM 分析
@kai identify pods experiencing OOMKilled events and recommend fixes

# スケジューリング問題
@kai analyze pending pods and identify scheduling constraints

# ネットワーク問題
@kai investigate network connectivity issues between services

# RBAC 監査
@kai audit RBAC configuration against least-privilege principles

# ネットワークポリシー
@kai analyze network policies and recommend security improvements

# Pod セキュリティ
@kai identify pods running with excessive privileges

# シークレット監査
@kai audit secrets management and recommend rotation strategy

@kai #dashboard EKS cluster health with node and pod metrics
@kai #report cluster optimization opportunities with implementation plan
@kai #recommend HPA policies for variable workloads
@kai #alert on pod OOMKilled events or node pressure conditions

# 良い例
@kai analyze production EKS cluster
in us-west-2 for pod resource
optimization

# 避けるべき例
@kai check our containers

# 良い例
@kai improve cluster utilization
while maintaining <30s pod startup
and 99.9% availability

# 避けるべき例
@kai make cluster better

# Step 1: 評価
@kai analyze cluster resource utilization

# Step 2: 無駄の特定
@kai identify pods with >50% overprovisioned resources

# Step 3: 計画
@kai #recommend right-sizing with zero-downtime approach

# Step 4: 監視
@kai #dashboard track resource utilization after changes

# Step 1: 特定
@kai identify unhealthy pods and failing deployments

# Step 2: 調査
@kai analyze logs and events for root cause

# Step 3: 修復
@kai #recommend immediate actions to restore service

# Step 4: 再発防止
@kai #recommend changes to prevent recurrence

# Step 1: ベースライン
@kai analyze current resource consumption patterns

# Step 2: 予測
@kai forecast resource needs for 2x growth

# Step 3: 計画
@kai #recommend node pool configuration for projected growth

# Step 4: 自動化
@kai #recommend autoscaling policies for demand variations