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Gke Storage

OrganizationPopular
google
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.

Overview

Publishergoogle
Repositoryskills
Skill namegke-storage
Stars
20.5K
Forks
1.7K
Bundled files
Instructions only
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by google on GitHub. Read the source before you install it.

Installation

Install the Gke Storage AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/google/skills/tree/main/skills/cloud/gke-storage
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/google/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/skills/cloud/gke-storage .claude/skills/gke-storage
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Gke Storage in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Gke Storage on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Gke Storage is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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

yaml
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)

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

Shared File Storage (ReadWriteMany via Filestore)

yaml
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:

yaml
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:

bash
# 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.

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

Frequently asked questions

What does the Gke Storage AI skill do?

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.

Why use Gke Storage on TypingMind?

Because you install it once and use it with any model. Gke Storage is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Gke Storage in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/google/skills/tree/main/skills/cloud/gke-storage. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Gke Storage?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Gke Storage?

As many as you like. As long as a model supports skills, you can use Gke Storage with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Gke Storage AI skill free?

Yes. It is published on GitHub by google under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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