本文へ移動
cccskills
無料GitHub で公開

gke-storage

Manages GKE storage, including PVCs, PersistentVolumes, and Filestore. Use when configuring GKE storage or creating PVCs. For GCS FUSE mounts, use google-cloud-storage-fuse. For diagnosing storage failures (volume attach/mount errors, disk performance/node storage pressure, or Cloud Storage FUSE OOM), use gke-storage-troubleshooting. Don't use for database administration or replication strategies outside volume provisioning context.

インストール方法を見る

含まれるファイル(1)

  • SKILL.md6.8 KB

SKILL.md(原文)

インストールする前に、エージェントに与えられる指示の中身を確認できます。

GKE Storage

Routing Note: For Cloud Storage FUSE (gcsfuse) mounts or the GKE gcsfuse.csi.storage.gke.io CSI driver, open google-cloud-storage-fuse/SKILL.md.

This reference covers storage configuration for GKE clusters including persistent disks, file storage, and cloud storage integration.

MCP Tools: apply_k8s_manifest, get_k8s_resource, describe_k8s_resource, get_cluster

Golden Path Storage Defaults

The golden path Autopilot config enables these CSI drivers:

DriverGolden PathAccess ModeUse Case
Compute EngineEnabled (default)ReadWriteOnceBlock storage for
: Persistent Disk : : : databases, :
: CSI : : : single-pod workloads :
Google CloudEnabledReadWriteManyShared NFS for
: Filestore CSI : : : multi-pod access :
Cloud StorageEnabledReadWriteMany /Mount GCS buckets as
: FUSE CSI : : ReadOnlyMany : volumes :
ParallelstoreEnabledReadWriteManyHigh-performance
: CSI : : : parallel file system :
Boot disk typepd-balancedN/ANode boot disks

StorageClasses

Default StorageClasses

GKE provides built-in StorageClasses:

StorageClassDisk TypeUse Case
standard-rwopd-standardCost-effective, low IOPS
premium-rwopd-ssdHigh IOPS, databases
standard-rwxFilestore (Basic HDD)Shared NFS
premium-rwxFilestore (Basic SSD)Shared NFS, higher performance

Custom StorageClass

apiVersion: storage.k8s.io/v1
kind: StorageClass
metadata:
  name: fast-regional
provisioner: pd.csi.storage.gke.io
parameters:
  type: pd-ssd
  replication-type: regional-pd    # Replicate across 2 zones
volumeBindingMode: WaitForFirstConsumer
allowVolumeExpansion: true         # Always enable for production

PersistentVolumeClaims

Block Storage (ReadWriteOnce)

apiVersion: v1
kind: PersistentVolumeClaim
metadata:
  name: database-pvc
spec:
  accessModes:
  - ReadWriteOnce
  storageClassName: premium-rwo
  resources:
    requests:
      storage: 100Gi

Shared File Storage (ReadWriteMany via Filestore)

apiVersion: v1
kind: PersistentVolumeClaim
metadata:
  name: shared-data
spec:
  accessModes:
  - ReadWriteMany
  storageClassName: standard-rwx
  resources:
    requests:
      storage: 1Ti    # Filestore minimum is 1 TiB for Basic tier

GCS Bucket Mount (Cloud Storage FUSE)

Mount a GCS bucket as a volume without a PVC:

apiVersion: v1
kind: Pod
metadata:
  name: gcs-reader
  annotations:
    gke-gcsfuse/volumes: "true"
spec:
  containers:
  - name: reader
    image: busybox
    command: ["ls", "/data"]
    volumeMounts:
    - name: gcs-bucket
      mountPath: /data
  volumes:
  - name: gcs-bucket
    csi:
      driver: gcsfuse.csi.storage.gke.io
      readOnly: true
      volumeAttributes:
        bucketName: <BUCKET_NAME>

Requires Workload Identity for the pod's service account to have storage.objectViewer on the bucket.

Volume Expansion

If allowVolumeExpansion: true is set on the StorageClass, resize by updating the PVC:

# kubectl
kubectl patch pvc <PVC_NAME> -p '{"spec":{"resources":{"requests":{"storage":"200Gi"}}}}'
# MCP (preferred)
patch_k8s_resource(parent="...", resourceType="persistentvolumeclaim", name="<PVC_NAME>",
  patch='{"spec":{"resources":{"requests":{"storage":"200Gi"}}}}')

Kubernetes automatically resizes the filesystem.

Stateful Storage & Capacity Constraints

When configuring storage for stateful workloads, keep the following capacity and scheduling constraints in mind:

  • Existing zonal PVs pin the workload to a zone: A Pod that uses an existing zonal PersistentVolume (for example, in europe-north1-b) can only run in that zone. The cluster autoscaler can't work around a capacity shortage by adding nodes in another zone; any fallback must be in the same zone.
  • Use automated disk type selection and topology-aware scheduling for new workloads: Use volumeBindingMode: WaitForFirstConsumer so the volume is created in the zone where the Pod lands. When a ComputeClass mixes machine generations (for example, C4/N4 priority with N2 fallback), configure the StorageClass for automated disk type selection (parameters.type: dynamic with pd-type: pd-balanced, hyperdisk-type: hyperdisk-balanced, disk-type-preference: hyperdisk-type, and use-allowed-disk-topology: "true", GKE 1.35.3-gke.1290000+; or use-allowed-disk-topology: "true" on GKE 1.34.1-gke.2541000+) so the autoscaler only picks nodes that support the disk type and new volumes get a compatible disk type per node.
  • Same-zone PD-type swaps rarely help: Switching a same-zone fallback from pd-ssd to pd-balanced is unlikely to resolve a zonal capacity shortage, because both can be affected by the same zonal constraints. Prefer a fallback to a Hyperdisk-capable machine series (for example, C3 or N4) with hyperdisk-balanced.
  • Check Hyperdisk quotas first: Hyperdisk quotas (for example, HDB-TOTAL-GB, throughput, and IOPS) are separate from Persistent Disk quotas. Verify the regional limits before recommending a Hyperdisk Balanced fallback.

Best Practices

  1. Always enable volume expansion: Set allowVolumeExpansion: true on all StorageClasses
  2. Use regional PDs for production: replication-type: regional-pd replicates across 2 zones for HA
  3. Use WaitForFirstConsumer: Ensures the PV is provisioned in the same zone as the pod
  4. Choose the right disk type: pd-ssd for databases, pd-balanced (golden path default) for general use, pd-standard for cold storage
  5. Use Filestore for shared access: When multiple pods need to read/write the same files
  6. Use GCS FUSE for data pipelines: Mount buckets directly for ML training data, logs, etc.
  7. Back up PVCs: Use Backup for GKE (see the gke-backup-dr skill) to protect persistent data

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

同じリポジトリのスキル

概要と使いどころ

Configures best-practice alerting policies for AI agents using OpenTelemetry (OTel) metrics, generating output as Terraform (.tf) configuration files. Use when analyzing, writing, or deploying alerting policies to monitor agent latency, error rates, token usage, and quality metrics. Don't use for standard infrastructure monitoring unrelated to AI agents, or when the agent is not instrumented with OpenTelemetry (for Reliability, Cost, Safety, Security alerts). NOTE: Reliability, Cost, Safety, and Security alerts use generic OTel metrics and work across runtimes (such as Cloud Run, Vertex AI). Quality alerts rely on Vertex AI Online Monitors and are strictly bound to Vertex AI deployments.

日本語の概要は準備中です。原文の説明を表示しています。

google/skills2.1万2026年10月10日 更新

Deploy open models or custom weights from Model Garden to Agent Platform endpoints, check the status of an in-progress deployment operation, or clean up resources by undeploying models and deleting endpoints. Use when asked to actively deploy a model, list the Model Garden CATALOG of available models, check if a specific model is deployable (`gcloud ai model-garden models list-deployment-config`), query deployment cost, troubleshoot deployment errors (like quota limits), or undeploy/clean up endpoints. Also use when copying and deploying a 1P Tuned Model. Don't use for pure listing/discovery questions of the form "is X deployed?", "list my endpoints", or "which regions have models running?" — for those use `agent-platform-endpoint-management`. Don't use for running model evaluations (use `agent-platform-eval-flywheel` skill).

日本語の概要は準備中です。原文の説明を表示しています。

google/skills2.1万2026年10月10日 更新

Manages Agent Platform serving endpoints. Use when you need to create, list, describe, update, or delete serving endpoints for model deployment on Agent Platform. Also use when troubleshooting endpoint permission, quota, or resource busy errors. Don't use for deploying models to endpoints or for running model evaluations.

日本語の概要は準備中です。原文の説明を表示しています。

google/skills2.1万2026年10月10日 更新

Measures and improves the quality of AI models and agents on Google Cloud using the Eval Quality Flywheel methodology. Use when generating synthetic user scenarios, evaluating an agent or model, building an eval dataset, picking or writing evaluation metrics, analyzing failures, comparing results before and after a fix, or when guidance is needed on Agent Platform eval methodology — including dataset schema, LLM-as-judge scoring, and common failure causes. For fine-tuning, use agent-platform-tuning. For general production deployment, use agent-platform-deploy.

日本語の概要は準備中です。原文の説明を表示しています。

google/skills2.1万2026年10月10日 更新

Connects to and performs inference with Google Cloud Agent Platform GenAI models, including First-Party Gemini models and Third-Party OpenMaaS models (Llama, DeepSeek, Qwen, etc.). Use when asked to perform inference, ask a model a question, run a test prompt, execute chat completions, or generate code for calling Gemini or OpenMaaS models, authenticate with GenAI SDK, OpenAI SDK, or legacy Agent Platform SDK, configure base URLs and global/regional endpoints, or troubleshoot 429 Resource Exhausted (DSQ), 400 User Validation, or 404 Not Found errors. Don't use for deploying models to endpoints or for running model evaluations.

日本語の概要は準備中です。原文の説明を表示しています。

google/skills2.1万2026年10月10日 更新

Guides agents and users through migrating from Gemini API in Google AI Studio to Gemini Enterprise Agent Platform (formerly Vertex AI). Use this skill when moving applications to Google Cloud, to leverage Cloud credits, or to unify inferencing with other Cloud infrastructure (IAM, billing, telemetry).

日本語の概要は準備中です。原文の説明を表示しています。

google/skills2.1万2026年10月10日 更新

google のスキルをすべて見る

このスキルの問題を報告する