gke-manifest-generation

SKILL.md

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.

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.

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.

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).