Installation

Open Claude Code and run this command:

/plugin install castai-pack@claude-code-plugins-plus

Use --global to install for all projects, or --project for current project only.

What It Does

> 18 production-grade Claude Code skills for Kubernetes cost optimization with CAST AI

Skills (18)

castai-ci-integration SKILL.md View full skill →

'Integrate CAST AI policy validation and cost checks into CI/CD pipelines.

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CAST AI CI Integration

Overview

Add CAST AI cost validation to CI/CD pipelines: verify savings thresholds, validate Terraform plans before apply, and gate deployments on autoscaler health.

Prerequisites

GitHub Actions enabled
CAST AI API key stored as repository secret
Terraform state accessible from CI (if using Terraform)

Instructions

Step 1: GitHub Actions -- Savings Gate


# .github/workflows/castai-check.yml
name: CAST AI Cost Check

on:
  pull_request:
    paths: ["terraform/**", "k8s/**"]
  schedule:
    - cron: "0 8 * * 1"  # Weekly Monday report

jobs:
  cost-check:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4

      - name: Check CAST AI Savings
        env:
          CASTAI_API_KEY: ${{ secrets.CASTAI_API_KEY }}
          CASTAI_CLUSTER_ID: ${{ secrets.CASTAI_CLUSTER_ID }}
        run: |
          SAVINGS=$(curl -s -H "X-API-Key: ${CASTAI_API_KEY}" \
            "https://api.cast.ai/v1/kubernetes/clusters/${CASTAI_CLUSTER_ID}/savings")

          PERCENT=$(echo "$SAVINGS" | jq -r '.savingsPercentage')
          MONTHLY=$(echo "$SAVINGS" | jq -r '.monthlySavings')

          echo "### CAST AI Savings Report" >> $GITHUB_STEP_SUMMARY
          echo "- Monthly savings: \$${MONTHLY}" >> $GITHUB_STEP_SUMMARY
          echo "- Savings percentage: ${PERCENT}%" >> $GITHUB_STEP_SUMMARY

          # Fail if savings drop below threshold
          if (( $(echo "$PERCENT < 10" | bc -l) )); then
            echo "WARNING: Savings below 10% threshold"
            exit 1
          fi

      - name: Verify Agent Health
        env:
          CASTAI_API_KEY: ${{ secrets.CASTAI_API_KEY }}
          CASTAI_CLUSTER_ID: ${{ secrets.CASTAI_CLUSTER_ID }}
        run: |
          STATUS=$(curl -s -H "X-API-Key: ${CASTAI_API_KEY}" \
            "https://api.cast.ai/v1/kubernetes/external-clusters/${CASTAI_CLUSTER_ID}" \
            | jq -r '.agentStatus')

          if [ "$STATUS" != "online" ]; then
            echo "CAST AI agent is ${STATUS}, expected online"
            exit 1
          fi

Step 2: Terraform Plan Validation


  terraform-plan:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: hashicorp/setup-terraform@v3

      - name: Terraform Init & Plan
        working-directory: terraform/
        env:
          CASTAI_API_TOKEN: ${{ secrets.CASTAI_API_KEY }}
        run: |
          terraform init
          terraform plan -var-file=environments/prod.tfvars \
            -out=plan.tfplan -no-color | tee plan-output.txt

      - name: Check for Destructive Changes
        run: |
          if grep -q "will be d


                
                  
                  
                  castai-common-errors
                  SKILL.md
                  View full skill →
                
                
                  'Diagnose and fix CAST AI agent, API, and autoscaler errors.
                  
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                  CAST AI Common Errors
Overview
Diagnostic guide for the 10 most common CAST AI issues, covering agent connectivity, API errors, autoscaler failures, and node provisioning problems.
Prerequisites

kubectl access to the cluster
CASTAIAPIKEY configured
Access to CAST AI console for log correlation

Error Reference
1. Agent Pod CrashLoopBackOff

kubectl get pods -n castai-agent
kubectl logs -n castai-agent deployment/castai-agent --tail=50

Causes and fixes:

Invalid API key: Regenerate at console.cast.ai > API
Wrong provider: Set --set provider=eks|gke|aks correctly in Helm
RBAC missing: Apply the required ClusterRole and ClusterRoleBinding
Network blocked: Ensure outbound HTTPS to api.cast.ai is allowed

2. Agent Shows "Disconnected" in Console

# Check agent heartbeat
kubectl logs -n castai-agent deployment/castai-agent | grep -i "heartbeat\|connect\|error"

# Verify network connectivity from inside the cluster
kubectl run castai-debug --image=curlimages/curl --rm -it --restart=Never -- \
  curl -s -o /dev/null -w "%{http_code}" https://api.cast.ai/v1/kubernetes/external-clusters

Fix: Restart the agent pod: kubectl rollout restart deployment/castai-agent -n castai-agent
3. API Returns 401 Unauthorized

# Test API key
curl -s -o /dev/null -w "%{http_code}" \
  -H "X-API-Key: ${CASTAI_API_KEY}" \
  https://api.cast.ai/v1/kubernetes/external-clusters
# Should return 200, not 401

Fix: Generate a new API key at console.cast.ai > API > API Access Keys.
4. Nodes Not Scaling Up (Unschedulable Pods)

# Check for pending pods
kubectl get pods --all-namespaces --field-selector=status.phase=Pending

# Verify unschedulable pods policy is enabled
curl -s -H "X-API-Key: ${CASTAI_API_KEY}" \
  "https://api.cast.ai/v1/kubernetes/clusters/${CASTAI_CLUSTER_ID}/policies" \
  | jq '.unschedulablePods'

Causes:

unschedulablePods.enabled is false -- enable it
Cluster limits reached -- increase clusterLimits.cpu.maxCores
No matching node template -- check constraints match pod requirements

5. Nodes Not Scaling Down (Empty Nodes)

# Check node downscaler configuration
curl -s -H "X-API-Key: ${CASTAI_API_KEY}" \
  "https://api.cast.ai/v1/kubernetes/clusters/${CASTAI_CLUSTER_ID}/policies" \
  | jq '.nodeDownscaler'


                
                  
                  
                  castai-core-workflow-a
                  SKILL.md
                  View full skill →
                
                
                  'Configure CAST AI autoscaler policies and node templates for cost optimization.
                  
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                  CAST AI Core Workflow: Autoscaler & Policies
Overview
Primary workflow for CAST AI: configure autoscaler policies to optimize cluster costs. Covers enabling spot instances, configuring the node downscaler and evictor, setting cluster CPU/memory limits, and creating node templates for workload-specific requirements.
Prerequisites

Completed castai-install-auth with Phase 2 (cluster controller + evictor)
CASTAIAPIKEY and CASTAICLUSTERID set
Cluster in "ready" status

Instructions
Step 1: Read Current Policies

curl -s -H "X-API-Key: ${CASTAI_API_KEY}" \
  "https://api.cast.ai/v1/kubernetes/clusters/${CASTAI_CLUSTER_ID}/policies" \
  | jq .

Step 2: Enable Cost-Optimized Autoscaling

curl -X PUT -H "X-API-Key: ${CASTAI_API_KEY}" \
  -H "Content-Type: application/json" \
  "https://api.cast.ai/v1/kubernetes/clusters/${CASTAI_CLUSTER_ID}/policies" \
  -d '{
    "enabled": true,
    "unschedulablePods": {
      "enabled": true,
      "headroom": {
        "cpuPercentage": 10,
        "memoryPercentage": 10,
        "enabled": true
      }
    },
    "nodeDownscaler": {
      "enabled": true,
      "emptyNodes": {
        "enabled": true,
        "delaySeconds": 180
      }
    },
    "spotInstances": {
      "enabled": true,
      "clouds": ["aws"],
      "spotDiversityEnabled": true,
      "spotDiversityPriceIncreaseLimitPercent": 20
    },
    "clusterLimits": {
      "enabled": true,
      "cpu": {
        "minCores": 4,
        "maxCores": 100
      }
    }
  }'

Step 3: Configure Node Templates via Terraform

resource "castai_node_template" "spot_workers" {
  cluster_id = castai_eks_cluster.this.id
  name       = "spot-workers"
  is_default = false
  is_enabled = true

  constraints {
    min_cpu               = 2
    max_cpu               = 16
    min_memory            = 4096
    max_memory            = 65536
    spot                  = true
    use_spot_fallbacks    = true
    fallback_restore_rate_seconds = 600

    instance_families {
      include = ["m5", "m6i", "c5", "c6i", "r5", "r6i"]
    }

    architectures = ["amd64"]
  }

  custom_labels = {
    "workload-type" = "batch"
  }
}

resource "castai_node_template" "gpu_ondemand" {
  cluster_id = castai_eks_cluster.this.id
  name       = "gpu-ondemand"
  is_default = false
  is_enabled = true


                
                  
                  
                  castai-core-workflow-b
                  SKILL.md
                  View full skill →
                
                
                  'Configure CAST AI Workload Autoscaler for pod-level right-sizing and.
                  
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                  CAST AI Core Workflow: Workload Autoscaler
Overview
CAST AI Workload Autoscaler right-sizes pod resource requests based on actual usage, reducing over-provisioning without manual VPA tuning. This skill covers enabling the workload autoscaler, configuring scaling policies per workload, and using annotations for fine-grained control.
Prerequisites

Completed castai-core-workflow-a (cluster-level policies)
CAST AI agent v1.60+ installed
Workload Autoscaler enabled in CAST AI console

Instructions
Step 1: Install Workload Autoscaler Components

helm upgrade --install castai-workload-autoscaler \
  castai-helm/castai-workload-autoscaler \
  -n castai-agent \
  --set castai.apiKey="${CASTAI_API_KEY}" \
  --set castai.clusterID="${CASTAI_CLUSTER_ID}"

Step 2: Query Workload Recommendations

# Get resource recommendations for a specific workload
curl -s -H "X-API-Key: ${CASTAI_API_KEY}" \
  "https://api.cast.ai/v1/workload-autoscaling/clusters/${CASTAI_CLUSTER_ID}/workloads" \
  | jq '.items[] | {
    name: .workloadName,
    namespace: .namespace,
    currentCpu: .currentCpuRequest,
    recommendedCpu: .recommendedCpuRequest,
    currentMemory: .currentMemoryRequest,
    recommendedMemory: .recommendedMemoryRequest,
    savingsPercent: .estimatedSavingsPercent
  }'

Step 3: Configure Per-Workload Policies via Annotations

# Add annotations to deployments for CAST AI workload autoscaler
apiVersion: apps/v1
kind: Deployment
metadata:
  name: my-api
  annotations:
    # Enable workload autoscaling
    autoscaling.cast.ai/enabled: "true"
    # CPU configuration
    autoscaling.cast.ai/cpu-min: "100m"
    autoscaling.cast.ai/cpu-max: "4000m"
    autoscaling.cast.ai/cpu-headroom: "15"
    # Memory configuration
    autoscaling.cast.ai/memory-min: "128Mi"
    autoscaling.cast.ai/memory-max: "8Gi"
    autoscaling.cast.ai/memory-headroom: "20"
    # Apply changes automatically vs recommendation-only
    autoscaling.cast.ai/apply-type: "immediate"
spec:
  template:
    spec:
      containers:
        - name: api
          resources:
            requests:
              cpu: "500m"      # Will be auto-adjusted by CAST AI
              memory: "512Mi"  # Will be auto-adjusted by CAST AI

Step 4: Create a Scaling Policy via API

curl -X POST -H "X-API-Key: ${CASTAI_API_KEY}" \
  -H "Content-Type: application/json" \
  "https://api.cast.ai/v1/workload-autoscaling/clusters/${CASTAI_CLUSTER_ID}/policies" \
  -d '{
    "name": "cost-optimized",
    "applyType": "IMMEDIATE",
    &


                
                  
                  
                  castai-cost-tuning
                  SKILL.md
                  View full skill →
                
                
                  'Maximize Kubernetes cost savings with CAST AI spot strategies and right-sizing.
                  
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                  CAST AI Cost Tuning
Overview
Maximize Kubernetes cost savings through CAST AI: spot instance strategies, workload right-sizing, cluster hibernation, and savings tracking. Typical savings: 50-70% on cloud compute costs.
Prerequisites

CAST AI Phase 2 enabled with full automation
Savings report available (requires 24h+ of data)
Understanding of workload criticality tiers

Instructions
Step 1: Analyze Current Savings

# Get savings breakdown
curl -s -H "X-API-Key: ${CASTAI_API_KEY}" \
  "https://api.cast.ai/v1/kubernetes/clusters/${CASTAI_CLUSTER_ID}/savings" \
  | jq '{
    currentMonthlyCost: .currentMonthlyCost,
    optimizedMonthlyCost: .optimizedMonthlyCost,
    monthlySavings: .monthlySavings,
    savingsPercentage: .savingsPercentage,
    spotSavings: .spotSavings,
    rightSizingSavings: .rightSizingSavings
  }'

Step 2: Maximize Spot Usage

# Enable aggressive spot with diversity and fallbacks
curl -X PUT -H "X-API-Key: ${CASTAI_API_KEY}" \
  -H "Content-Type: application/json" \
  "https://api.cast.ai/v1/kubernetes/clusters/${CASTAI_CLUSTER_ID}/policies" \
  -d '{
    "enabled": true,
    "spotInstances": {
      "enabled": true,
      "clouds": ["aws"],
      "spotDiversityEnabled": true,
      "spotDiversityPriceIncreaseLimitPercent": 20,
      "spotBackups": {
        "enabled": true,
        "spotBackupRestoreRateSeconds": 600
      }
    }
  }'

Spot allocation strategy by workload tier:

Workload Type
Spot %
Rationale


Batch jobs, CI runners
100% spot
Interruptible, restartable


Stateless APIs (behind LB)
80% spot
Can handle brief interruptions


Stateful services, databases
0% spot
Use on-demand or reserved


ML training
80-100% spot
Checkpointing handles interrupts


Step 3: Workload Right-Sizing

# Get resource waste analysis
curl -s -H "X-API-Key: ${CASTAI_API_KEY}" \
  "https://api.cast.ai/v1/workload-autoscaling/clusters/${CASTAI_CLUSTER_ID}/workloads" \
  | jq '[.items[] | select(.estimatedSavingsPercent > 20) | {
    name: .workloadName,
    namespace: .namespace,
    wastedCpu: (.currentCpuRequest - .recommendedCpuRequest),
    wastedMemory: (.currentMemoryRequest - .recommendedMemoryRequest),
    savingsPercent: .estimatedSavingsPercent
  }] | sort_by(-.savingsPercent) | .[0:10]'

Step 4: Cluster Hibernation (Dev/Staging)


                
                  
                  
                  castai-debug-bundle
                  SKILL.md
                  View full skill →
                
                
                  'Collect CAST AI diagnostic bundle for support tickets and troubleshooting.
                  
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                  CAST AI Debug Bundle
Overview
Collect all CAST AI component logs, cluster state, and configuration into a single archive for troubleshooting or support tickets. The bundle captures agent status, Helm releases, autoscaler policies, node inventory, and recent events.
Prerequisites

kubectl access to the cluster running CAST AI
CASTAIAPIKEY and CASTAICLUSTERID configured
helm installed

Instructions
Step 1: Run the Debug Bundle Script

#!/bin/bash
# castai-debug-bundle.sh
set -euo pipefail

BUNDLE_DIR="castai-debug-$(date +%Y%m%d-%H%M%S)"
mkdir -p "$BUNDLE_DIR"

echo "=== CAST AI Debug Bundle ===" | tee "$BUNDLE_DIR/summary.txt"
echo "Generated: $(date -u)" | tee -a "$BUNDLE_DIR/summary.txt"
echo "Cluster ID: ${CASTAI_CLUSTER_ID:-unknown}" | tee -a "$BUNDLE_DIR/summary.txt"

# 1. Helm releases
echo "--- Helm Releases ---" >> "$BUNDLE_DIR/summary.txt"
helm list -n castai-agent -o yaml > "$BUNDLE_DIR/helm-releases.yaml" 2>&1

# 2. Pod status
echo "--- Pod Status ---" >> "$BUNDLE_DIR/summary.txt"
kubectl get pods -n castai-agent -o wide > "$BUNDLE_DIR/pod-status.txt" 2>&1

# 3. Agent logs (last 200 lines each)
for deploy in castai-agent cluster-controller castai-evictor castai-spot-handler castai-workload-autoscaler; do
  kubectl logs -n castai-agent "deployment/$deploy" --tail=200 \
    > "$BUNDLE_DIR/${deploy}-logs.txt" 2>&1 || echo "Not found: $deploy" >> "$BUNDLE_DIR/summary.txt"
done

# 4. Cluster events (CAST AI related)
kubectl get events -n castai-agent --sort-by='.lastTimestamp' \
  > "$BUNDLE_DIR/events.txt" 2>&1

# 5. Node inventory
kubectl get nodes -o wide > "$BUNDLE_DIR/nodes.txt" 2>&1

# 6. API cluster status (redact API key from output)
if [ -n "${CASTAI_API_KEY:-}" ] && [ -n "${CASTAI_CLUSTER_ID:-}" ]; then
  curl -s -H "X-API-Key: ${CASTAI_API_KEY}" \
    "https://api.cast.ai/v1/kubernetes/external-clusters/${CASTAI_CLUSTER_ID}" \
    | jq '{name, status, agentStatus, providerType, createdAt}' \
    > "$BUNDLE_DIR/cluster-status.json" 2>&1

  curl -s -H "X-API-Key: ${CASTAI_API_KEY}" \
    "https://api.cast.ai/v1/kubernetes/clusters/${CASTAI_CLUSTER_ID}/policies" \
    > "$BUNDLE_DIR/policies.json" 2>&1
fi

# 7. RBAC check
kubectl get clusterrole -l app.kubernetes.io/managed-by=castai \
  > "$BUNDLE_DIR/rbac.txt" 2>&1

# 8. Package bundle
tar -czf "$BUNDLE_DIR.tar.gz" "$BUNDLE_DIR"
rm -rf "$BUNDLE_DIR"
echo "Bundl


                
                  
                  
                  castai-deploy-integration
                  SKILL.md
                  View full skill →
                
                
                  'Deploy CAST AI across multi-cloud Kubernetes clusters with Terraform.
                  
                      ReadWriteEditBash(terraform:*)Bash(helm:*)Bash(kubectl:*)
                    
                
                
                  CAST AI Deploy Integration
Overview
Deploy CAST AI to EKS, GKE, and AKS clusters using official Terraform modules. Each cloud provider has a dedicated CAST AI module that handles IAM roles, node configuration, and autoscaler setup.
Prerequisites

Terraform 1.0+
CAST AI Full Access API key
Cloud provider credentials configured
Existing Kubernetes cluster

Instructions
EKS Deployment

# main.tf -- EKS cluster onboarding
module "castai_eks" {
  source  = "castai/eks-cluster/castai"
  version = "~> 3.0"

  api_token           = var.castai_api_token
  aws_account_id      = data.aws_caller_identity.current.account_id
  aws_cluster_region  = var.region
  aws_cluster_name    = var.cluster_name

  # IAM role for CAST AI to manage nodes
  aws_instance_profile_arn = aws_iam_instance_profile.castai.arn

  # Autoscaler configuration
  autoscaler_policies_json = jsonencode({
    enabled = true
    unschedulablePods = { enabled = true }
    nodeDownscaler = {
      enabled = true
      emptyNodes = { enabled = true, delaySeconds = 300 }
    }
    spotInstances = {
      enabled = true
      spotDiversityEnabled = true
    }
    clusterLimits = {
      enabled = true
      cpu = { minCores = 4, maxCores = 200 }
    }
  })

  # Node templates
  default_node_configuration = module.castai_eks.castai_node_configurations["default"]
}

GKE Deployment

module "castai_gke" {
  source  = "castai/gke-cluster/castai"
  version = "~> 2.0"

  api_token            = var.castai_api_token
  project_id           = var.gcp_project_id
  gke_cluster_name     = var.cluster_name
  gke_cluster_location = var.region

  gke_credentials = base64decode(
    google_container_cluster.this.master_auth[0].cluster_ca_certificate
  )

  autoscaler_policies_json = jsonencode({
    enabled = true
    unschedulablePods = { enabled = true }
    nodeDownscaler = {
      enabled = true
      emptyNodes = { enabled = true, delaySeconds = 300 }
    }
  })
}

AKS Deployment

module "castai_aks" {
  source  = "castai/aks/castai"
  version = "~> 1.0"

  api_token              = var.castai_api_token
  aks_cluster_name       = var.cluster_name
  aks_cluster_region     = var.region
  node_resource_group    = azurerm_kubernetes_cluster.this.node_resource_group
  azure_subscription_id  = data.azurerm_subscription.current.subscription_id
  azure_tenant_id        = data.azurerm_client_config.current.tenant_id

  autoscaler_policies_json = jsonencode({
    enabled = true
    unschedulablePods = { enabled = true }
    spotInstances = { enabled = true }
  })
}

Multi-Cluster Deployment Pattern

# Deploy CAST AI across all clusters with a for_


                
                  
                  
                  castai-hello-world
                  SKILL.md
                  View full skill →
                
                
                  'Query CAST AI cluster savings report and node inventory.
                  
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                  CAST AI Hello World
Overview
First API calls against the CAST AI REST API: list connected clusters, retrieve the savings report, and inspect node inventory. All examples use curl with the X-API-Key header -- no SDK required.
Prerequisites

Completed castai-install-auth setup
CASTAIAPIKEY environment variable set
At least one cluster connected to CAST AI

Instructions
Step 1: List Connected Clusters

curl -s -H "X-API-Key: ${CASTAI_API_KEY}" \
  https://api.cast.ai/v1/kubernetes/external-clusters \
  | jq '.items[] | {id, name, status, providerType}'

Expected output:

{
  "id": "abc123-def456",
  "name": "production-eks",
  "status": "ready",
  "providerType": "eks"
}

Step 2: Get Cluster Savings Report

export CASTAI_CLUSTER_ID="your-cluster-id"

# Current month savings
curl -s -H "X-API-Key: ${CASTAI_API_KEY}" \
  "https://api.cast.ai/v1/kubernetes/clusters/${CASTAI_CLUSTER_ID}/savings" \
  | jq '{
    monthlySavings: .monthlySavings,
    savingsPercentage: .savingsPercentage,
    currentCost: .currentMonthlyCost,
    optimizedCost: .optimizedMonthlyCost
  }'

Step 3: List Cluster Nodes

curl -s -H "X-API-Key: ${CASTAI_API_KEY}" \
  "https://api.cast.ai/v1/kubernetes/external-clusters/${CASTAI_CLUSTER_ID}/nodes" \
  | jq '.items[] | {
    name: .name,
    instanceType: .instanceType,
    lifecycle: .lifecycle,
    cpu: .allocatableCpu,
    memory: .allocatableMemory,
    zone: .zone
  }'

Step 4: Check Autoscaler Policies

curl -s -H "X-API-Key: ${CASTAI_API_KEY}" \
  "https://api.cast.ai/v1/kubernetes/clusters/${CASTAI_CLUSTER_ID}/policies" \
  | jq '{
    enabled: .enabled,
    unschedulablePods: .unschedulablePods.enabled,
    nodeDownscaler: .nodeDownscaler.enabled,
    spotInstances: .spotInstances.enabled
  }'

Output

List of connected clusters with IDs and status
Monthly savings report with before/after cost
Node inventory with instance types and lifecycle (spot vs on-demand)
Autoscaler policy status

Error Handling

                
              
                
                  
                  
                  castai-install-auth
                  SKILL.md
                  View full skill →
                
                
                  'Install and configure CAST AI agent on a Kubernetes cluster with API.
                  
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                  CAST AI Install & Auth
Overview
Connect a Kubernetes cluster (EKS, GKE, AKS, or KOPS) to CAST AI for cost optimization, autoscaling, and security scanning. Covers API key generation, Helm chart installation of the CAST AI agent, and Terraform provider setup.
Prerequisites

A running Kubernetes cluster (EKS, GKE, AKS, or KOPS)
kubectl configured with cluster admin access
helm v3 installed
A CAST AI account at https://console.cast.ai

Instructions
Step 1: Generate an API Key
Log in to https://console.cast.ai and navigate to API > API Access Keys. Create a Full Access key for Terraform-managed clusters, or Read-Only for monitoring-only.

export CASTAI_API_KEY="your-api-key-here"

# Verify the key works
curl -s -H "X-API-Key: ${CASTAI_API_KEY}" \
  https://api.cast.ai/v1/kubernetes/external-clusters | jq '.items | length'

Step 2: Install the CAST AI Agent via Helm

# Add the CAST AI Helm repository
helm repo add castai-helm https://castai.github.io/helm-charts
helm repo update

# Install the read-only monitoring agent (Phase 1)
helm upgrade --install castai-agent castai-helm/castai-agent \
  -n castai-agent --create-namespace \
  --set apiKey="${CASTAI_API_KEY}" \
  --set provider="eks"  # eks | gke | aks

kubectl get pods -n castai-agent

Step 3: Enable Full Automation (Phase 2)

export CASTAI_CLUSTER_ID="your-cluster-id"

# Cluster controller -- manages node lifecycle
helm upgrade --install cluster-controller castai-helm/castai-cluster-controller \
  -n castai-agent \
  --set castai.apiKey="${CASTAI_API_KEY}" \
  --set castai.clusterID="${CASTAI_CLUSTER_ID}"

# Evictor -- consolidates underutilized nodes
helm upgrade --install castai-evictor castai-helm/castai-evictor \
  -n castai-agent \
  --set castai.apiKey="${CASTAI_API_KEY}" \
  --set castai.clusterID="${CASTAI_CLUSTER_ID}"

# Spot handler -- graceful spot instance interruption
helm upgrade --install castai-spot-handler castai-helm/castai-spot-handler \
  -n castai-agent \
  --set castai.provider="eks" \
  --set castai.clusterID="${CASTAI_CLUSTER_ID}"

Step 4: Terraform Provider (Alternative)

terraform {
  required_providers {
    castai = {
      source  = "castai/castai"
      version = "~> 7.0"
    }
  }
}

provider "castai" {
  api_token = var.castai_api_token
}

variable "castai_api_token" {
  type      = string
  sensitive = true
}

resource "castai_eks_cluster" "this" {
  account_id = data.aws_caller_identity.current.account_id
  region     = var.aws_region
  name       =

                

              

                
                  
                  
                  castai-local-dev-loop
                  SKILL.md
                  View full skill →
                
                
                  'Set up a local Kubernetes development loop with CAST AI cost monitoring.
                  
                      ReadWriteEditBash(kubectl:*)Bash(helm:*)Bash(terraform:*)
                    
                
                
                  CAST AI Local Dev Loop
Overview
Fast iteration workflow for CAST AI integrations: test autoscaler policies in a dev cluster, validate Terraform modules before applying to production, and use the CAST AI API to measure savings impact during development.
Prerequisites

Completed castai-install-auth setup
A development Kubernetes cluster (kind, minikube, or cloud dev cluster)
kubectl, helm, and optionally terraform installed

Instructions
Step 1: Project Structure

my-castai-infra/
├── terraform/
│   ├── environments/
│   │   ├── dev.tfvars
│   │   ├── staging.tfvars
│   │   └── prod.tfvars
│   ├── modules/
│   │   └── castai-cluster/
│   │       ├── main.tf
│   │       ├── variables.tf
│   │       └── outputs.tf
│   └── main.tf
├── policies/
│   ├── dev-policy.json
│   └── prod-policy.json
├── scripts/
│   ├── check-savings.sh
│   └── validate-policies.sh
├── .env.dev            # Dev API key (git-ignored)
└── .env.example

Step 2: Dev Cluster with Relaxed Policies

# Connect your dev cluster (read-only first)
helm upgrade --install castai-agent castai-helm/castai-agent \
  -n castai-agent --create-namespace \
  --set apiKey="${CASTAI_API_KEY_DEV}" \
  --set provider="eks"

# Apply development-safe autoscaler policy
curl -X PUT -H "X-API-Key: ${CASTAI_API_KEY_DEV}" \
  -H "Content-Type: application/json" \
  "https://api.cast.ai/v1/kubernetes/clusters/${CASTAI_CLUSTER_ID}/policies" \
  -d '{
    "enabled": true,
    "unschedulablePods": { "enabled": true },
    "nodeDownscaler": {
      "enabled": true,
      "emptyNodes": { "enabled": true, "delaySeconds": 300 }
    },
    "clusterLimits": {
      "enabled": true,
      "cpu": { "minCores": 2, "maxCores": 16 }
    }
  }'

Step 3: Quick Savings Check Script

#!/bin/bash
# scripts/check-savings.sh
set -euo pipefail

API_KEY="${CASTAI_API_KEY_DEV}"
CLUSTER_ID="${CASTAI_CLUSTER_ID}"

echo "=== CAST AI Dev Cluster Savings ==="
curl -s -H "X-API-Key: ${API_KEY}" \
  "https://api.cast.ai/v1/kubernetes/clusters/${CLUSTER_ID}/savings" \
  | jq '{
    monthlySavings: .monthlySavings,
    percentage: .savingsPercentage,
    spotNodes: [.nodes[] | select(.lifecycle == "spot")] | length,
    totalNodes: [.nodes[]] | length
  }'

Step 4: Terraform Plan-Apply Loop

# Plan with dev variables
cd terraform/
terraform plan -var-file=environments/dev.tfvars -out=plan.tfplan

# Apply and check CAST AI result
terraform apply plan.tfplan

# Verify policies took effect
curl 

                

              

                
                  
                  
                  castai-performance-tuning
                  SKILL.md
                  View full skill →
                
                
                  'Optimize CAST AI autoscaler performance, node provisioning speed, and.
                  
                      ReadWriteEditBash(curl:*)Bash(kubectl:*)
                    
                
                
                  CAST AI Performance Tuning
Overview
Tune CAST AI for faster node provisioning, more responsive autoscaling, and efficient API usage. Covers headroom configuration, instance family selection, and API caching for multi-cluster dashboards.
Prerequisites

CAST AI Phase 2 (full automation) enabled
Understanding of workload scheduling patterns
Access to autoscaler policy configuration

Instructions
Step 1: Optimize Node Provisioning Speed

# Configure headroom for proactive scaling (avoids waiting for pending pods)
curl -X PUT -H "X-API-Key: ${CASTAI_API_KEY}" \
  -H "Content-Type: application/json" \
  "https://api.cast.ai/v1/kubernetes/clusters/${CASTAI_CLUSTER_ID}/policies" \
  -d '{
    "enabled": true,
    "unschedulablePods": {
      "enabled": true,
      "headroom": {
        "enabled": true,
        "cpuPercentage": 15,
        "memoryPercentage": 15
      }
    }
  }'

Headroom pre-provisions spare capacity so pods schedule immediately instead of waiting 2-5 minutes for new nodes.
Step 2: Instance Family Optimization

# Terraform: Prefer instance families with fast launch times
resource "castai_node_template" "fast_launch" {
  cluster_id = castai_eks_cluster.this.id
  name       = "fast-launch-workers"

  constraints {
    spot                  = true
    use_spot_fallbacks    = true
    fallback_restore_rate_seconds = 300

    # Newer instance types launch faster and have better availability
    instance_families {
      include = ["m6i", "m7i", "c6i", "c7i", "r6i", "r7i"]
    }

    # Enable spot diversity for faster provisioning
    spot_diversity_price_increase_limit_percent = 25

    architectures = ["amd64"]
  }
}

Step 3: Evictor Tuning for Faster Consolidation

# Reduce empty node delay for dev/staging (faster downscale)
helm upgrade castai-evictor castai-helm/castai-evictor \
  -n castai-agent \
  --reuse-values \
  --set evictor.aggressiveMode=true \
  --set evictor.cycleInterval=120

# For production, use non-aggressive with longer intervals
# --set evictor.aggressiveMode=false
# --set evictor.cycleInterval=600

Step 4: API Performance for Multi-Cluster Dashboards

import { LRUCache } from "lru-cache";

const cache = new LRUCache<string, unknown>({ max: 100, ttl: 60_000 });

interface ClusterSummary {
  id: string;
  name: string;
  savings: number;
  savingsPercent: number;
  nodeCount: number;
  spotPercent: number;
}

async function getClusterSummary(clusterId: string): Promise<ClusterSummary> {
  const cacheKey = `summary:${clust

                

              

                
                  
                  
                  castai-prod-checklist
                  SKILL.md
                  View full skill →
                
                
                  'Production readiness checklist for CAST AI cluster onboarding.
                  
                      ReadBash(kubectl:*)Bash(curl:*)Bash(helm:*)Grep
                    
                
                
                  CAST AI Production Checklist
Overview
Complete checklist for enabling CAST AI cost optimization on a production Kubernetes cluster. Covers Phase 1 (monitoring) through Phase 2 (full automation) with validation steps at each stage.
Prerequisites

CAST AI tested on a staging cluster first
Production API key (Full Access)
Change management approval for node lifecycle changes

Phase 1: Monitoring Only

[ ] Agent installed with read-only key
[ ] Agent pod healthy: kubectl get pods -n castai-agent
[ ] Console shows cluster as "Connected"
[ ] Savings report generating (wait 24h for full data)
[ ] Review savings estimate before enabling automation

Phase 2: Autoscaling Enabled

[ ] Full Access API key provisioned and stored in secrets manager
[ ] Cluster controller installed
[ ] Evictor installed with conservative settings (non-aggressive)
[ ] Spot handler installed for graceful interruption handling
[ ] Autoscaler policies configured with appropriate limits:
[ ] clusterLimits.cpu.maxCores set to safe ceiling
[ ] unschedulablePods.headroom configured (10-15%)
[ ] nodeDownscaler.emptyNodes.delaySeconds >= 300 for production
[ ] spotInstances.spotDiversityEnabled = true
[ ] Node templates created for workload-specific needs (GPU, high-memory)
[ ] PodDisruptionBudgets set on all critical workloads

Workload Autoscaler

[ ] Workload autoscaler installed
[ ] Critical deployments annotated with min/max resource bounds
[ ] Anti-shrink cooldown set (300s minimum)
[ ] Memory headroom >= 20% for production workloads

Security

[ ] API key in secrets manager (not Helm values files)
[ ] Kvisor security agent installed
[ ] Network policies applied to castai-agent namespace
[ ] RBAC reviewed and minimized
[ ] Key rotation scheduled (90-day interval)

Monitoring and Alerting

[ ] Alert on agent pod restarts: kubepodcontainerstatusrestarts_total{namespace="castai-agent"}
[ ] Alert on API errors in agent logs
[ ] CAST AI console email notifications enabled
[ ] Savings report reviewed weekly
[ ] Dashboard tracking spot vs on-demand node ratio

Rollback Procedure

# Disable autoscaling immediately (keeps agent monitoring)
curl -X PUT -H "X-API-Key: ${CASTAI_API_KEY}" \
  -H "Content-Type: application/json" \
  "https://api.cast.ai/v1/kubernetes/clusters/${CASTAI_CLUSTER_ID}/policies" \
  -d '{"enabled": false}'

# Or remove all CAST AI components
helm uninstall castai-evict

                

              

                
                  
                  
                  castai-rate-limits
                  SKILL.md
                  View full skill →
                
                
                  'Handle CAST AI API rate limits with backoff and request queuing.
                  
                      ReadWriteEdit
                    
                
                
                  CAST AI Rate Limits
Overview
The CAST AI REST API enforces rate limits per API key. The autoscaler agent communicates cluster state at 15-second intervals. For custom API integrations, implement exponential backoff and request queuing to avoid hitting limits.
Prerequisites

CAST AI API key configured
Understanding of the API endpoints you call

Rate Limit Behavior

Error
Cause
Solution


401 Unauthorized
Bad API key
Regenerate at console.cast.ai


404 Not Found
Wrong cluster ID
List clusters first to get correct ID


Empty items array
No clusters connected

Aspect
Value


Rate limit scope
Per API key


Response on limit
HTTP 429 with Retry-After header


Agent sync interval
Every 15 seconds


Recommended polling
No more than once per 30 seconds


Instructions
Step 1: Detect Rate Limits from Response Headers

async function castaiRequest(path: string): Promise<Response> {
  const response = await fetch(`https://api.cast.ai${path}`, {
    headers: { "X-API-Key": process.env.CASTAI_API_KEY! },
  });

  // Log rate limit headers for monitoring
  const remaining = response.headers.get("X-RateLimit-Remaining");
  const reset = response.headers.get("X-RateLimit-Reset");
  if (remaining) {
    console.log(`Rate limit remaining: ${remaining}, resets: ${reset}`);
  }

  if (response.status === 429) {
    const retryAfter = parseInt(response.headers.get("Retry-After") ?? "5");
    throw new RateLimitError(retryAfter);
  }

  return response;
}

class RateLimitError extends Error {
  constructor(public retryAfterSeconds: number) {
    super(`Rate limited. Retry after ${retryAfterSeconds}s`);
  }
}

Step 2: Exponential Backoff with Jitter

async function withBackoff<T>(
  fn: () => Promise<T>,
  maxRetries = 5
): Promise<T> {
  for (let attempt = 0; attempt <= maxRetries; attempt++) {
    try {
      return await fn();
    } catch (err) {
      if (attempt === maxRetries) throw err;

      let delayMs: number;
      if (err instanceof RateLimitError) {
        delayMs = err.retryAfterSeconds * 1000;
      } else {
        delayMs = Math.min(1000 * Math.pow(2, attempt), 30000);
      }
      // Add jitter to prevent thundering herd
      delayMs += Math.random() * 1000;

      console.log(`Retry ${attempt + 1}/${maxRetries} in ${delayMs}ms`);
      await new Promise((r) => setTimeout(r, delayMs));
    }
  }
  throw new Error("Unreachable");
}

Step 3: Request Queue for Batch Operations

import PQueue from "p-queue";

// Limit concurrent requests and enforce interval
const castaiQueue = new PQueue({
  concurrency: 3,
  interval: 1000,
  intervalCap: 5,   // Max 5 requests per second
});

async function queuedCastAIRequest<T>(fn


                
                  
                  
                  castai-reference-architecture
                  SKILL.md
                  View full skill →
                
                
                  'CAST AI reference architecture for multi-cluster Kubernetes cost optimization.
                  
                      ReadWriteEditGrep
                    
                
                
                  CAST AI Reference Architecture
Overview
Production-grade architecture for managing CAST AI across multiple Kubernetes clusters. Covers Terraform module layout, per-environment policies, API key management, and observability integration.
Prerequisites

Multiple Kubernetes clusters (dev, staging, production)
Terraform for infrastructure management
Centralized secrets management
Monitoring stack (Prometheus, Grafana, or Datadog)

Terraform Module Structure

infrastructure/
├── modules/
│   └── castai-cluster/
│       ├── main.tf              # CAST AI provider resources
│       ├── variables.tf         # Cluster-specific inputs
│       ├── outputs.tf           # Cluster ID, savings metrics
│       ├── policies.tf          # Autoscaler policy configuration
│       ├── node-templates.tf    # Node template definitions
│       └── security.tf          # Kvisor, RBAC
├── environments/
│   ├── dev/
│   │   ├── main.tf              # Dev cluster onboarding
│   │   ├── terraform.tfvars     # Dev-specific values
│   │   └── backend.tf           # State storage
│   ├── staging/
│   │   ├── main.tf
│   │   ├── terraform.tfvars
│   │   └── backend.tf
│   └── prod/
│       ├── main.tf
│       ├── terraform.tfvars
│       └── backend.tf
└── shared/
    ├── api-keys.tf              # Key management
    └── monitoring.tf            # Alerting rules

Reusable Module

# modules/castai-cluster/main.tf
variable "cluster_name" { type = string }
variable "cluster_id" { type = string }
variable "environment" { type = string }
variable "api_token" { type = string; sensitive = true }
variable "provider_type" { type = string }  # eks, gke, aks
variable "max_cpu_cores" { type = number; default = 100 }
variable "spot_enabled" { type = bool; default = true }
variable "hibernation_enabled" { type = bool; default = false }
variable "evictor_aggressive" { type = bool; default = false }

resource "castai_autoscaler" "this" {
  cluster_id = var.cluster_id

  autoscaler_policies_json = jsonencode({
    enabled = true
    unschedulablePods = {
      enabled = true
      headroom = {
        enabled          = true
        cpuPercentage    = var.environment == "prod" ? 15 : 5
        memoryPercentage = var.environment == "prod" ? 15 : 5
      }
    }
    nodeDownscaler = {
      enabled = true
      emptyNodes = {
        enabled      = true
        delaySeconds = var.environment == "prod" ? 300 : 60
      }
    }
    spotInstances = {
      enabled              = var.spot_enabled
      spotDiversityEnabled = true
    }
    clusterLimits = {
      enabled = true
      cpu     = { minCores = 2, maxCores = var.max_cpu_cores }
    }
  })
}

resource "castai_node_template" "default_spot"


                
                  
                  
                  castai-sdk-patterns
                  SKILL.md
                  View full skill →
                
                
                  'Production-ready CAST AI REST API wrapper patterns in TypeScript and.
                  
                      ReadWriteEdit
                    
                
                
                  CAST AI SDK Patterns
Overview
CAST AI uses a REST API with X-API-Key header authentication. There is no official SDK -- build typed wrappers around fetch or requests. These patterns cover singleton clients, typed responses, retry with backoff, and multi-cluster management.
Prerequisites

Completed castai-install-auth setup
TypeScript 5+ or Python 3.10+
Familiarity with async/await patterns

Instructions
Step 1: TypeScript API Client

// src/castai/client.ts
interface CastAIConfig {
  apiKey: string;
  baseUrl?: string;
  timeoutMs?: number;
}

interface CastAICluster {
  id: string;
  name: string;
  status: string;
  providerType: "eks" | "gke" | "aks";
  agentStatus: string;
  createdAt: string;
}

interface CastAISavings {
  monthlySavings: number;
  savingsPercentage: number;
  currentMonthlyCost: number;
  optimizedMonthlyCost: number;
}

interface CastAINode {
  name: string;
  instanceType: string;
  lifecycle: "on-demand" | "spot";
  allocatableCpu: string;
  allocatableMemory: string;
  zone: string;
}

class CastAIClient {
  private apiKey: string;
  private baseUrl: string;
  private timeoutMs: number;

  constructor(config: CastAIConfig) {
    this.apiKey = config.apiKey;
    this.baseUrl = config.baseUrl ?? "https://api.cast.ai";
    this.timeoutMs = config.timeoutMs ?? 30000;
  }

  private async request<T>(path: string, options?: RequestInit): Promise<T> {
    const controller = new AbortController();
    const timeout = setTimeout(() => controller.abort(), this.timeoutMs);

    try {
      const response = await fetch(`${this.baseUrl}${path}`, {
        ...options,
        headers: {
          "X-API-Key": this.apiKey,
          "Content-Type": "application/json",
          ...options?.headers,
        },
        signal: controller.signal,
      });

      if (!response.ok) {
        const body = await response.text();
        throw new CastAIError(response.status, body, path);
      }

      return response.json();
    } finally {
      clearTimeout(timeout);
    }
  }

  async listClusters(): Promise<CastAICluster[]> {
    const data = await this.request<{ items: CastAICluster[] }>(
      "/v1/kubernetes/external-clusters"
    );
    return data.items;
  }

  async getSavings(clusterId: string): Promise<CastAISavings> {
    return this.request(`/v1/kubernetes/clusters/${clusterId}/savings`);
  }

  async listNodes(clusterId: string): Promise<CastAINode[]> {
    const data = await this.request<{ items: CastAINode[] }>(
      `/v1/kubernetes/external-clusters/${clusterId}/nodes`
    );
    return data.items;
  }

  async updatePolicies(clusterId: string, policies: Record<string, unknown>): Promise<void> {


                
                  
                  
                  castai-security-basics
                  SKILL.md
                  View full skill →
                
                
                  'Secure CAST AI API keys, RBAC configuration, and Kvisor security agent.
                  
                      ReadWriteEditBash(kubectl:*)Bash(helm:*)Grep
                    
                
                
                  CAST AI Security Basics
Overview
Secure your CAST AI integration: API key management, RBAC least-privilege, Kvisor runtime security agent, and network policy configuration.
Prerequisites

CAST AI agent installed on cluster
Cluster admin access for RBAC configuration
Secrets manager (AWS Secrets Manager, Vault, etc.)

Instructions
Step 1: API Key Management

# Use separate keys per environment
# console.cast.ai > API > API Access Keys

# Development: Read-Only key (monitoring only)
# Staging: Full Access key with limited cluster scope
# Production: Full Access key, rotated every 90 days

# Store in secrets manager, never in code
aws secretsmanager create-secret \
  --name "castai/prod/api-key" \
  --secret-string "${CASTAI_API_KEY}"

# Rotate key procedure:
# 1. Generate new key in console
# 2. Update secrets manager
# 3. Restart CAST AI agent pods to pick up new key
# 4. Verify agent reconnects
# 5. Revoke old key in console

Step 2: RBAC Least-Privilege Review

# Audit CAST AI ClusterRoles
kubectl get clusterroles -l app.kubernetes.io/managed-by=castai -o yaml

# The CAST AI agent needs these minimum permissions:
# - get/list/watch: pods, nodes, events, namespaces, replicasets
# - get: persistentvolumes, storageclasses
# The cluster controller additionally needs:
# - create/delete: nodes (for autoscaling)
# - patch: pods/eviction (for evictor)

# Check for overly broad permissions
kubectl auth can-i --list --as=system:serviceaccount:castai-agent:castai-agent

Step 3: Enable Kvisor Security Agent

# Kvisor scans for CVEs, misconfigurations, and runtime threats
helm upgrade --install castai-kvisor castai-helm/castai-kvisor \
  -n castai-agent \
  --set castai.apiKey="${CASTAI_API_KEY}" \
  --set castai.clusterID="${CASTAI_CLUSTER_ID}" \
  --set controller.extraArgs.image-scan-enabled=true \
  --set controller.extraArgs.kube-bench-enabled=true

# Verify Kvisor is running
kubectl get pods -n castai-agent -l app.kubernetes.io/name=castai-kvisor

Step 4: Network Policies

# Restrict CAST AI agent egress to only api.cast.ai
apiVersion: networking.k8s.io/v1
kind: NetworkPolicy
metadata:
  name: castai-agent-egress
  namespace: castai-agent
spec:
  podSelector:
    matchLabels:
      app.kubernetes.io/name: castai-agent
  policyTypes:
    - Egress
  egress:
    - to:
        - ipBlock:
            cidr: 0.0.0.0/0  # api.cast.ai resolves dynamically
      ports:
        - protocol: TCP
          port: 443
    - to:  # Allow DNS
        - namespaceSelector: {}
      ports:
        - protocol: UDP
          port: 53

Step 5: Security Checklist

[ ] API keys stored in secrets manager, not Helm values files
[ ] Sep


                
                  
                  
                  castai-upgrade-migration
                  SKILL.md
                  View full skill →
                
                
                  'Upgrade CAST AI Helm charts, Terraform provider, and agent components.
                  
                      ReadWriteEditBash(helm:*)Bash(terraform:*)Bash(kubectl:*)
                    
                
                
                  CAST AI Upgrade & Migration
Overview
Upgrade CAST AI components: Helm charts for the agent/controller/evictor, Terraform provider version, and workload autoscaler. Includes rollback procedures for each component.
Prerequisites

Current CAST AI components installed
Staging cluster for testing upgrades first
Helm and kubectl access
Change management approval for production

Instructions
Step 1: Check Current Versions

# Helm chart versions
helm list -n castai-agent -o json | jq '.[] | {name: .name, chart: .chart, appVersion: .app_version}'

# Available versions
helm repo update
helm search repo castai-helm --versions | head -20

# Terraform provider version
grep "castai/castai" .terraform.lock.hcl
terraform providers

Step 2: Review Changelog

# Check CAST AI changelog for breaking changes
# https://docs.cast.ai/changelog/

# Check Terraform provider releases
# https://github.com/castai/terraform-provider-castai/releases

Step 3: Upgrade on Staging First

# Upgrade agent
helm upgrade castai-agent castai-helm/castai-agent \
  -n castai-agent --reuse-values

# Upgrade cluster controller
helm upgrade cluster-controller castai-helm/castai-cluster-controller \
  -n castai-agent --reuse-values

# Upgrade evictor
helm upgrade castai-evictor castai-helm/castai-evictor \
  -n castai-agent --reuse-values

# Upgrade workload autoscaler
helm upgrade castai-workload-autoscaler castai-helm/castai-workload-autoscaler \
  -n castai-agent --reuse-values

# Verify all pods restarted cleanly
kubectl get pods -n castai-agent -w

Step 4: Upgrade Terraform Provider

# Update version constraint in versions.tf
terraform {
  required_providers {
    castai = {
      source  = "castai/castai"
      version = "~> 7.5"  # Update to target version
    }
  }
}


# Upgrade provider
terraform init -upgrade
terraform plan -var-file=environments/staging.tfvars

# Review plan carefully for resource recreation
# Apply if plan looks safe
terraform apply -var-file=environments/staging.tfvars

Step 5: Validate After Upgrade

# Verify agent is online
curl -s -H "X-API-Key: ${CASTAI_API_KEY}" \
  "https://api.cast.ai/v1/kubernetes/external-clusters/${CASTAI_CLUSTER_ID}" \
  | jq '{agentStatus, name}'

# Verify autoscaler policies still applied
curl -s -H "X-API-Key: ${CASTAI_API_KEY}" \
  "https://api.cast.ai/v1/kubernetes/clusters/${CASTAI_CLUSTER_ID}/policies" \
  | jq '.enabled'

# Check for errors in new agent version
kubectl logs -n castai-agent deployment/castai-agent --tail=50 | grep -i error

Rollback Procedure


                
                  
                  
                  castai-webhooks-events
                  SKILL.md
                  View full skill →
                
                
                  'Configure CAST AI webhook notifications for cluster events and audit.
                  
                      ReadWriteEditBash(curl:*)
                    
                
                
                  CAST AI Webhooks & Events
Overview
CAST AI emits events for node lifecycle changes, autoscaler decisions, and security findings. Configure webhook endpoints or use the audit log API to track all cluster operations. Integrates with Slack, PagerDuty, and custom HTTP endpoints.
Prerequisites

CAST AI cluster connected and active
HTTPS endpoint for receiving webhooks (or Slack webhook URL)
API key with Full Access

Instructions
Step 1: Configure Notification Channels in Console
Navigate to console.cast.ai > your cluster > Notifications. Available channels:

Slack: Webhook URL integration
Email: Per-user notifications
PagerDuty: Incident escalation
Custom webhook: Any HTTPS endpoint

Step 2: Query Audit Log via API

# Get recent cluster operations
curl -s -H "X-API-Key: ${CASTAI_API_KEY}" \
  "https://api.cast.ai/v1/kubernetes/clusters/${CASTAI_CLUSTER_ID}/audit-log?limit=20" \
  | jq '.items[] | {
    time: .createdAt,
    action: .action,
    initiator: .initiatedBy,
    details: .details
  }'

Step 3: Build a Custom Notification Handler

// castai-webhook-handler.ts
import express from "express";

const app = express();
app.use(express.json());

interface CastAIEvent {
  eventType: string;
  clusterId: string;
  clusterName: string;
  timestamp: string;
  data: {
    nodeName?: string;
    instanceType?: string;
    lifecycle?: string;
    action?: string;
    savingsImpact?: number;
  };
}

app.post("/castai/events", async (req, res) => {
  const event: CastAIEvent = req.body;

  switch (event.eventType) {
    case "node.added":
      console.log(
        `Node added: ${event.data.nodeName} (${event.data.instanceType}, ${event.data.lifecycle})`
      );
      await notifySlack(
        `New ${event.data.lifecycle} node: ${event.data.instanceType}`
      );
      break;

    case "node.removed":
      console.log(`Node removed: ${event.data.nodeName}`);
      break;

    case "node.spot_interrupted":
      console.log(`Spot interruption: ${event.data.nodeName}`);
      await notifyPagerDuty("Spot instance interrupted", event);
      break;

    case "savings.threshold":
      console.log(`Savings threshold crossed: ${event.data.savingsImpact}%`);
      break;

    default:
      console.log(`Unhandled event: ${event.eventType}`);
  }

  res.status(200).json({ received: true });
});

async function notifySlack(message: string): Promise<void> {
  await fetch(process.env.SLACK_WEBHOOK_URL!, {
    method: "POST",
    headers: { "Content-Type": "application/json" },
    body: JSON.stringify({
      text: `:kubernetes: CAST



      
      
          How It Works
          1. Install the Pack

/plugin install castai-pack@claude-code-plugins-plus

2. Get Your API Key
Sign up at console.cast.ai, navigate to API > API Access Keys, and create a Full Access key.
3. Connect Your First Cluster

export CASTAI_API_KEY="your-key"

helm repo add castai-helm https://castai.github.io/helm-charts
helm repo update

helm upgrade --install castai-agent castai-helm/castai-agent \
  -n castai-agent --create-namespace \
  --set apiKey="${CASTAI_API_KEY}" \
  --set provider="eks"

4. Check Your Savings

export CASTAI_CLUSTER_ID="your-cluster-id"

curl -s -H "X-API-Key: ${CASTAI_API_KEY}" \
  "https://api.cast.ai/v1/kubernetes/clusters/${CASTAI_CLUSTER_ID}/savings" \
  | jq '{monthly: .monthlySavings, percent: .savingsPercentage}'

5. Enable Autoscaling
Follow castai-core-workflow-a to configure autoscaler policies and start saving.
        

      
      

      
      

      
      
  Ready to use castai-pack?
  
    
    
  




      
      
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          Tags
          
            castaicast aisaassdkintegration

Workload Type	Spot %	Rationale
Batch jobs, CI runners	100% spot	Interruptible, restartable
Stateless APIs (behind LB)	80% spot	Can handle brief interruptions
Stateful services, databases	0% spot	Use on-demand or reserved
ML training	80-100% spot	Checkpointing handles interrupts

Error	Cause	Solution
`401 Unauthorized`	Bad API key	Regenerate at console.cast.ai
`404 Not Found`	Wrong cluster ID	List clusters first to get correct ID
Empty `items` array	No clusters connected

Aspect	Value
Rate limit scope	Per API key
Response on limit	HTTP 429 with `Retry-After` header
Agent sync interval	Every 15 seconds
Recommended polling	No more than once per 30 seconds