Gke Manifest Generation

GKE Manifest Generation Skill

This skill provides guidelines, tooling integration, and templates to translate natural language descriptions or application code changes into secure, compliant, and cost-effective Kubernetes YAML manifests optimized for both GKE Autopilot and GKE Standard clusters.

Core Rules & Verification

When generating or updating YAML manifests, you must strictly adhere to the following rules:

1. Namespace & Resource Isolation

• Explicit Namespace: Always declare namespace: {namespace} explicitly in the metadata of every resource (Deployments, Services, ConfigMaps, Secrets, PVCs, Roles, bindings). Map it to the namespace configured in your active SETTINGS.md. Never omit the namespace.
• Dedicated ServiceAccount: Avoid using the namespace's default ServiceAccount. Always create and reference a dedicated ServiceAccount (e.g., devteam-agent-sa) for each microservice.

2. GKE Resource Tuning (Autopilot & Standard)

• Resources Requests & Limits: Always specify CPU and Memory requests and limits for all containers.

  • GKE Autopilot: Requests determine pod billing directly; requests and limits must be equal. If they differ, Autopilot will automatically scale requests up to match limits, which can significantly increase costs.
  • GKE Standard: Requests ensure stable scheduling and bin-packing; limits prevent resource starvation/noisy-neighbor issues.

• Density Defaults: For stateless apps or sidecars on GKE Standard, default to conservative requests (e.g., requests.cpu: "100m" or "200m", requests.memory: "256Mi" or "512Mi") with burstable limits. Use a reasonable overcommit ratio for limits (e.g., 2x to 4x requests, like limits.cpu: "400m" to "800m", and limits.memory: "512Mi" to "1Gi"). Avoid excessive overcommit limits (like limits.cpu: "4" for a 100m request) to prevent severe CPU throttling and latency degradation under heavy scheduling load, particularly in environments without guaranteed node shares.

• Spot VMs for Staging/Dev: For non-production workloads (e.g., namespaces containing -test, -dev, or -staging), or if the user requests cost optimization, automatically target GKE Spot VMs. This requires injecting both the nodeSelector targeting Spot VMs AND the corresponding toleration to tolerate the Spot VM taint:

  nodeSelector:
    cloud.google.com/gke-spot: "true"
  tolerations:
    - key: "cloud.google.com/gke-spot"
      operator: "Equal"
      value: "true"
      effect: "NoSchedule"

  (On GKE Standard, this assumes a Spot node pool is configured).

3. Container Security Hardening (Pod Security Standards)

• Non-Root Execution: Always configure securityContext at the Pod level (and container level if overriding) to run as a non-root user (e.g., runAsNonRoot: true, runAsUser: 10000, runAsGroup: 10000, fsGroup: 10000). This is strictly enforced on GKE Autopilot and is a critical security baseline for GKE Standard.
• Minimal Privileges: Always set allowPrivilegeEscalation: false and seccompProfile: {type: RuntimeDefault}.
• Read-Only Root Filesystem: Set readOnlyRootFilesystem: true to prevent modifications to the container image filesystem.
  • Writable Directory Fallback: If readOnlyRootFilesystem is enabled, mount a local emptyDir volume to /tmp or /var/run/ to allow applications (like Java/Nginx) to write temp files without crashing.
• Secret Volume Mounting: Prefer mounting Secrets as read-only files (configured in the volumes spec with defaultMode: 0400) instead of mapping them as environment variables, unless the application framework exclusively supports env-var based configuration. This prevents secrets leaking into application logs.

4. Health Checking (Mandatory Probes)

• Liveness & Readiness Probes: Every Deployment container must define both livenessProbe and readinessProbe.

  • Web/API: Use httpGet probes.
  • TCP Services: Use tcpSocket probes.
  • Databases/Caches: Use command-based exec probes (e.g., exec.command: ["redis-cli", "ping"]).

• Startup Probes for Slow-Starting Apps: For applications with slow boot times (e.g., Java spring boot, complex Python scripts, LLM model servers), you must also define a startupProbe. When a startupProbe is defined, the liveness and readiness probes are disabled until it succeeds, preventing Kubernetes from prematurely killing the pod during startup:

  startupProbe:
    httpGet:
      path: /healthz
      port: 8080
    failureThreshold: 30
    periodSeconds: 10

• Sensible Defaults: Set initialDelaySeconds: 5 to 15 depending on startup time (e.g., Java requires a longer delay than Go/Nginx).

5. Services & Ingress Routing

• Internal ClusterIP: Default all internal microservices to type: ClusterIP. Never use type: LoadBalancer or NodePort unless the workload is explicitly intended to be publicly accessible from the internet.
• Port Naming: Always assign clear, standard names to service and container ports (e.g., name: http-web or name: grpc-api) to enable automatic protocol discovery, tracing, and Web App routing.
• Prefer Gateway API: When exposing APIs externally, prioritize using GKE Gateway API (Gateway and HTTPRoute resources) over legacy Ingress objects to enable advanced L7 routing and security features (e.g., Cloud Armor).

6. Volume Mounts, StorageClasses & subPath Safety

• Avoid Directory Overwrites: When mounting a ConfigMap or Secret to an application directory containing other files (like Nginx public directories), always use subPath to overlay only the specific file. Caveat: Note that containers using subPath volume mounts do not receive automatic configuration updates if the underlying ConfigMap or Secret is modified; pods must be restarted manually to pick up changes.
• StorageClass Selection: Use the correct GKE storage class in PersistentVolumeClaims:
  • CSI Driver Clusters (Autopilot & Modern Standard): Use standard-rwo (default balanced PD) or premium-rwo (SSD PD).
  • Legacy Standard Clusters: Use standard (default PD) or premium (SSD PD) if standard-rwo/premium-rwo are not configured.
  • Database rule: Use SSD storage classes (premium-rwo or premium) only when the prompt explicitly requests high IOPS, low latency, or database storage.