Gke Manifest Generation Agent Skill

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.

7. High Availability on GKE

• Topology Spread: For deployments with >1 replica, use podAntiAffinity

or topologySpreadConstraints with topologyKey: "kubernetes.io/hostname"

to distribute pods across GKE nodes and availability zones.

• PodDisruptionBudget: For deployments with >1 replica, declare a

PodDisruptionBudget to guarantee minimum replica availability during

voluntary GKE node upgrades and maintenance cycles.

8. Updates & Server-Side Apply Reconciliations

• Stable List Keys: Under Kubernetes Server-Side Apply (SSA), elements in

associative lists (like volumes, volume mounts, ports, and container

definitions) are matched and merged by their unique identifier keys

(typically name). You must keep the name key stable when modifying

properties of an existing list item. Renaming the name key will cause SSA

to create a brand new entry and leave the old entry intact (orphaned) rather

than modifying it.

• Minimal Diff: Make only the changes requested. Adhere closely to

existing labels, annotations, and conventions.

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Specialty Workloads: GKE AI/Inference Serving (vLLM, TGI, etc.)

For model serving workloads, prioritize using optimized tooling like GKE

Inference Quickstart if available. If generating manually:

• GPU Request & Allocation:
• Always request nvidia.com/gpu in both requests and limits.
• Add a nodeSelector or node affinity targeting the desired GKE

accelerator tag (e.g., cloud.google.com/gke-accelerator: nvidia-l4).

• Shared Memory Boost:
• Model servers require high shared memory (/dev/shm) for inter-process

communications. Always declare and mount an emptyDir volume with

medium: Memory to /dev/shm.

• Weight Loading Optimization:
• Mount model weight directories (like GCS buckets) using the GKE GCS Fuse

CSI driver (csi.storage.gke.io) as readOnly: true for efficient

cold-starts.

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Tooling & Grounding Guidelines

When generating manifests, you should leverage the following tooling to reduce

hallucinations and optimize configurations:

• Inference Workloads (GKE Inference Quickstart CLI):

• Make sure you have the

Google Cloud SDK installed.

• For all AI/LLM inference workloads (e.g. model serving), you must

prioritize using the gcloud CLI GKE Inference Quickstart command to

generate the optimized manifests instead of writing them manually:

        gcloud container ai profiles manifests create \
          --model={model_name} \
          --model-server={server_name} \
          --accelerator-type={accelerator_type} \
          --output=manifest \
          --output-path={output_file_path}

• *Constraint*: You must include all resources returned by this command

(Deployments, Services, PodMonitoring, etc.) without filtering.

• Grounding in Official Documentation (Developer Knowledge API):

• For GKE-specific features, API defaults, manifest examples, or security

contexts, you must query Google's developer knowledge base to

retrieve official GKE documentation:

• answer_query: Use this to ask direct questions (e.g., *"How to

configure GCS Fuse CSI driver in GKE"*). This is the preferred tool

for general queries.

• search_documents: Use this to search for relevant GKE guides

or examples when you don't have a specific question.

• get_document: Use this to fetch full document contents when

you have a specific document ID.

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Reference Examples

For detailed, production-ready manifest templates, consult the following

reference guides:

• Basic Hardened Nginx Workload:

Production-ready deployment with dedicated service account, security

contexts, probes, anti-affinity, and PodDisruptionBudget.

• Network Policy: Default-deny ingress

network policy and selective ingress allowance for specific apps.

• AI/LLM Inference Workload: GPU resource

allocation, Workload Identity, GCS FUSE CSI driver mounting, /dev/shm

shared memory boost, and startup probes.

• GKE Gateway API Routing: Exposing workloads

using GKE L7 Gateway API (Gateway and HTTPRoute resources).