Cloud Platform Integrator
Integrate Kubernetes workloads with AWS EKS, Azure AKS, and GCP GKE including IAM, ingress controllers, storage classes, and platform-specific features.
Best use case
Cloud Platform Integrator is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Integrate Kubernetes workloads with AWS EKS, Azure AKS, and GCP GKE including IAM, ingress controllers, storage classes, and platform-specific features.
Teams using Cloud Platform Integrator should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/cloud-kubernetes-integrator/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How Cloud Platform Integrator Compares
| Feature / Agent | Cloud Platform Integrator | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Integrate Kubernetes workloads with AWS EKS, Azure AKS, and GCP GKE including IAM, ingress controllers, storage classes, and platform-specific features.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
## Purpose & When-To-Use
**Trigger conditions:**
- Deploying Kubernetes workloads to managed cloud clusters (EKS, AKS, GKE)
- Integrating cloud IAM with Kubernetes service accounts
- Setting up cloud-native ingress controllers
- Configuring persistent storage with cloud block storage
- Enabling cluster autoscaling for dynamic workloads
- Integrating cloud monitoring and logging services
**Not for:**
- Generic Kubernetes manifest generation (use kubernetes-manifest-generator)
- Helm chart creation (use kubernetes-helm-builder)
- Service mesh configuration (use kubernetes-servicemesh-configurator)
- Serverless deployments (use cloud-serverless-designer)
- Complete orchestration (use cloud-native-orchestrator agent)
---
## Pre-Checks
**Time normalization:**
- Compute `NOW_ET` using NIST/time.gov semantics (America/New_York, ISO-8601): 2025-10-26T01:33:54-04:00
- Use `NOW_ET` for all citation access dates
**Input validation:**
- `cloud_provider` must be: aws, azure, or gcp
- `cluster_name` must be valid for cloud provider naming rules
- `region` must be valid region for selected cloud provider
- `ingress_type` if specified must match cloud provider capabilities
**Source freshness:**
- AWS EKS Best Practices (accessed 2025-10-26T01:33:54-04:00): https://aws.github.io/aws-eks-best-practices/
- Azure AKS Best Practices (accessed 2025-10-26T01:33:54-04:00): https://learn.microsoft.com/en-us/azure/aks/best-practices
- GCP GKE Best Practices (accessed 2025-10-26T01:33:54-04:00): https://cloud.google.com/kubernetes-engine/docs/best-practices
**Decision thresholds:**
- T1 for basic cloud integration (IAM, storage classes)
- T2 for production integration (ingress, autoscaling, monitoring)
---
## Procedure
### T1: Basic Cloud Integration (≤2k tokens)
**Step 1: Configure IAM for Kubernetes**
- **AWS**: Create IRSA (IAM Roles for Service Accounts) configuration
- **Azure**: Configure AAD Pod Identity or Workload Identity
- **GCP**: Set up Workload Identity binding
- Generate ServiceAccount with cloud IAM annotation
**Step 2: Define storage classes**
- **AWS**: Create StorageClass for gp3 EBS volumes with encryption
- **Azure**: Create StorageClass for Azure Disk (Premium_LRS)
- **GCP**: Create StorageClass for Persistent Disk (pd-ssd)
- Add reclaim policy and volume expansion settings
**Output:**
- Cloud IAM to Kubernetes ServiceAccount binding config
- StorageClass definitions for persistent storage
- Basic integration validation steps
**Abort conditions:**
- Cloud region not supported by cluster
- IAM permissions insufficient for IRSA/Workload Identity setup
---
### T2: Production Cloud Integration (≤6k tokens)
**All T1 steps plus:**
**Step 1: Deploy cloud-native ingress controller**
- **AWS**: Install AWS Load Balancer Controller for ALB/NLB ingress
- **Azure**: Configure Application Gateway Ingress Controller (AGIC)
- **GCP**: Set up GKE Ingress for Cloud Load Balancing
- Configure ingress annotations for SSL, health checks, routing
**Step 2: Enable cluster autoscaling**
- **AWS**: Deploy Cluster Autoscaler or Karpenter for node provisioning
- **Azure**: Configure AKS cluster autoscaler with node pools
- **GCP**: Enable GKE cluster autoscaler with min/max node counts
- Set autoscaling policies based on CPU/memory utilization
**Step 3: Integrate cloud monitoring**
- **AWS**: Configure Container Insights with CloudWatch
- **Azure**: Enable Azure Monitor for containers
- **GCP**: Set up Cloud Logging and Cloud Monitoring
- Add log aggregation and metrics collection configs
**Step 4: Configure container registry integration**
- **AWS**: Set up ECR pull secrets or IRSA for ECR
- **Azure**: Configure ACR integration with AKS
- **GCP**: Enable Artifact Registry with Workload Identity
- Add imagePullSecrets to ServiceAccount
**Step 5: Network policy and security**
- Configure cloud-specific network policies
- Set up VPC/VNet integration for private clusters
- Add security group rules for ingress/egress
- Enable pod security policies (PSPs) or Pod Security Standards
**Output:**
- Complete cloud-native ingress setup
- Cluster autoscaling configuration
- Monitoring and logging integration
- Container registry authentication
- Network and security configurations
**Abort conditions:**
- Ingress controller conflicts with existing setup
- Insufficient cloud quotas for autoscaling
- Network policy conflicts with cloud VPC rules
---
### T3: Advanced Cloud Platform Features (≤12k tokens)
**All T1 + T2 steps plus:**
**Step 1: Multi-AZ and high availability**
- Configure node pools across availability zones
- Set topology spread constraints for pod distribution
- Add pod disruption budgets for maintenance
**Step 2: Advanced autoscaling**
- Configure custom metrics autoscaling (KEDA)
- Set up predictive autoscaling based on schedules
- Add spot/preemptible instance integration
**Step 3: Disaster recovery and backup**
- Configure Velero with cloud storage backend
- Set up cross-region cluster federation
- Add automated backup schedules
**Output:**
- Multi-AZ HA configuration
- Advanced autoscaling with custom metrics
- Disaster recovery and backup setup
---
## Decision Rules
**Cloud provider-specific features:**
- **AWS EKS**: IRSA for IAM, ALB Controller, Karpenter for autoscaling, EBS CSI driver
- **Azure AKS**: Workload Identity, AGIC, Virtual nodes, Azure Monitor
- **GCP GKE**: Workload Identity, GKE Ingress, Autopilot mode, Cloud Logging native
**Ingress controller selection:**
- **AWS ALB**: Native AWS integration, Layer 7 load balancing, WAF integration
- **Azure App Gateway**: Azure-native, WAF, SSL offload
- **GCP GKE Ingress**: Cloud Load Balancing, global load balancing, CDN integration
- **NGINX/Traefik**: Cloud-agnostic, advanced routing, middleware support
**Storage class types:**
- **AWS**: gp3 (general purpose SSD), io2 (high IOPS), efs (shared filesystem)
- **Azure**: Premium_LRS (SSD), Standard_LRS (HDD), Azure Files (shared)
- **GCP**: pd-ssd (SSD), pd-standard (HDD), Filestore (shared NFS)
**Autoscaling strategy:**
- **Cluster Autoscaler**: Standard, multi-cloud compatible
- **Karpenter (AWS)**: Fast, bin-packing optimization, spot instances
- **GKE Autopilot**: Fully managed, pay-per-pod
- **AKS Virtual Nodes**: Serverless node pool with ACI
**Ambiguity handling:**
- If ingress_type not specified → use cloud-native option (ALB, AGIC, GKE Ingress)
- If storage_required unclear → ask about stateful application needs
- If autoscaling_enabled unclear → recommend based on workload variability
---
## Output Contract
**Required fields (all tiers):**
```yaml
iam_config:
platform: "aws-irsa | azure-workload-identity | gcp-workload-identity"
service_account:
apiVersion: v1
kind: ServiceAccount
metadata:
name: "app-sa"
annotations:
cloud_annotation: "arn:aws:iam::xxx | azure_client_id | gcp_sa_email"
iam_policy: "cloud IAM policy or role definition"
storage_classes:
- name: "cloud-storage"
provisioner: "cloud-specific CSI driver"
parameters:
type: "gp3 | Premium_LRS | pd-ssd"
encrypted: "true"
reclaimPolicy: "Retain | Delete"
allowVolumeExpansion: true
```
**Additional T2 fields:**
```yaml
ingress_controller:
type: "alb | app-gateway | gke-ingress | nginx"
installation: "Helm chart or manifest YAML"
configuration: "controller-specific settings"
ingress_class: "IngressClass resource YAML"
autoscaling:
type: "cluster-autoscaler | karpenter | aks-autoscaler | gke-autoscaler"
configuration: "autoscaler deployment or managed config"
scaling_policies:
min_nodes: integer
max_nodes: integer
target_cpu_utilization: integer
monitoring:
platform: "cloudwatch | azure-monitor | cloud-logging"
configuration: "monitoring agent DaemonSet or managed config"
log_aggregation: "FluentBit/Fluentd configuration"
metrics_collection: "Prometheus scraping or cloud metrics"
registry_auth:
method: "irsa | workload-identity | image-pull-secret"
configuration: "registry authentication setup"
```
**Additional T3 fields:**
```yaml
high_availability:
multi_az: boolean
topology_spread_constraints: "pod topology config"
pod_disruption_budgets: "PDB YAML"
advanced_autoscaling:
keda_scalers: ["custom metric scalers"]
predictive_scaling: "schedule-based scaling rules"
spot_instances:
enabled: boolean
fallback_to_on_demand: boolean
disaster_recovery:
velero_config: "Velero installation with cloud storage"
backup_schedule: "cron schedule for backups"
cross_region_replication: boolean
```
---
## Examples
```yaml
# T1 Example: AWS IRSA ServiceAccount
apiVersion: v1
kind: ServiceAccount
metadata:
name: app-service-account
namespace: default
annotations:
eks.amazonaws.com/role-arn: arn:aws:iam::123456789012:role/app-role
```
```yaml
# T1 Example: AWS EBS StorageClass
apiVersion: storage.k8s.io/v1
kind: StorageClass
metadata:
name: ebs-gp3
provisioner: ebs.csi.aws.com
parameters:
type: gp3
encrypted: "true"
iops: "3000"
throughput: "125"
reclaimPolicy: Retain
allowVolumeExpansion: true
volumeBindingMode: WaitForFirstConsumer
```
```yaml
# T2 Example: AWS ALB Ingress
apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
name: app-ingress
annotations:
kubernetes.io/ingress.class: alb
alb.ingress.kubernetes.io/scheme: internet-facing
alb.ingress.kubernetes.io/target-type: ip
alb.ingress.kubernetes.io/certificate-arn: arn:aws:acm:...
spec:
rules:
- host: app.example.com
http:
paths:
- path: /
pathType: Prefix
backend:
service:
name: app-service
port:
number: 80
```
---
## Quality Gates
**Token budgets (enforced):**
- **T1**: ≤2,000 tokens - basic IAM and storage integration
- **T2**: ≤6,000 tokens - ingress, autoscaling, monitoring, registry auth
- **T3**: ≤12,000 tokens - HA, advanced autoscaling, disaster recovery
**Safety checks:**
- IAM policies follow least-privilege principle
- Storage encryption enabled by default
- Ingress configured with HTTPS/TLS (production)
- Network policies restrict unnecessary traffic
**Auditability:**
- All cloud resources cite official cloud provider documentation
- IAM role ARNs/IDs explicitly specified
- Storage classes specify encryption and reclaim policies
- Autoscaling policies include min/max node constraints
**Determinism:**
- Same inputs produce identical cloud integration configs
- Storage class parameters are explicit (not cloud defaults)
- IAM annotations use consistent format
**Validation requirements:**
- Cloud IAM configs validate against cloud provider schemas
- StorageClass manifests validate with kubectl
- Ingress resources validate against Kubernetes API
- T2+ configs include cost estimate for cloud resources
---
## Resources
**Official Documentation (accessed 2025-10-26T01:33:54-04:00):**
- AWS EKS User Guide: https://docs.aws.amazon.com/eks/latest/userguide/
- AWS EKS Best Practices: https://aws.github.io/aws-eks-best-practices/
- AWS Load Balancer Controller: https://kubernetes-sigs.github.io/aws-load-balancer-controller/
- Azure AKS Documentation: https://learn.microsoft.com/en-us/azure/aks/
- Azure AKS Best Practices: https://learn.microsoft.com/en-us/azure/aks/best-practices
- GCP GKE Documentation: https://cloud.google.com/kubernetes-engine/docs
- GCP GKE Best Practices: https://cloud.google.com/kubernetes-engine/docs/best-practices
**IAM and Identity:**
- AWS IRSA: https://docs.aws.amazon.com/eks/latest/userguide/iam-roles-for-service-accounts.html
- Azure Workload Identity: https://learn.microsoft.com/en-us/azure/aks/workload-identity-overview
- GCP Workload Identity: https://cloud.google.com/kubernetes-engine/docs/how-to/workload-identity
**Storage and Networking:**
- AWS EBS CSI Driver: https://github.com/kubernetes-sigs/aws-ebs-csi-driver
- Azure Disk CSI Driver: https://github.com/kubernetes-sigs/azuredisk-csi-driver
- GCP Persistent Disk CSI Driver: https://github.com/kubernetes-sigs/gcp-compute-persistent-disk-csi-driver
**Autoscaling:**
- Cluster Autoscaler: https://github.com/kubernetes/autoscaler/tree/master/cluster-autoscaler
- Karpenter: https://karpenter.sh/
- AKS Cluster Autoscaler: https://learn.microsoft.com/en-us/azure/aks/cluster-autoscaler
- GKE Cluster Autoscaler: https://cloud.google.com/kubernetes-engine/docs/concepts/cluster-autoscalerRelated Skills
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