Gke Basics

OrganizationPopular

google

gke-basics

Manages core GKE cluster provisioning, credentials, Autopilot vs Standard selection, and workload deployment. Use when creating GKE clusters, fetching kubectl credentials, or deciding between Autopilot and Standard modes. Don't use for Workload Identity (use gke-workload-identity), GKE networking (use gke-networking), security hardening (use gke-platform-security or gke-workload-security), or cluster upgrades (use gke-upgrades).

Overview

Publisher	google
Repository	`skills`
Skill name	`gke-basics`
Stars	20.5K
Forks	1.7K
Bundled files	5
License	Apache-2.0
Links	Skill source Repository

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.
5 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by google on GitHub. Read the source before you install it.

Installation

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

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/google/skills/tree/main/skills/cloud/gke-basics
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.

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-basics .claude/skills/gke-basics

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Gke Basics 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 Basics 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 Basics 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 Basics & Critical Gotchas

Routing Note: For Workload Identity KSA/GSA setup, open gke-workload-identity/SKILL.md. For cluster security flags (--database-encryption-key, --security-posture, RBAC, Shielded Nodes, Binary Authorization), open gke-platform-security/SKILL.md. For cluster upgrades or maintenance windows, open gke-upgrades/SKILL.md. For generating Kubernetes YAML manifests (Deployment, StatefulSet, Service, HTTPRoute, PodDisruptionBudget), open gke-manifest-generation/SKILL.md.

Managed Kubernetes platform on Google Cloud. Defaults to Autopilot mode unless Standard is explicitly required.

Key Selection Rules: Autopilot vs. Standard

Default to Autopilot for almost all workloads.
Use Standard ONLY if:
- Custom node OS kernel parameters (sysctl) are required.
- Custom node taints or specific hardware node pools are required.
- DaemonSets require raw hostPath mounts to the host OS filesystem.
When explaining why Standard is required over Autopilot, explicitly cite all matching restrictions (e.g., custom sysctls and custom node taints).
For advanced cluster architecture or complex node pool creation planning, refer to gke-cluster-creation.

Critical Gotchas & Best Practices

Private Autopilot Clusters:

Use --enable-private-nodes for private node IP addresses.
Use --enable-private-endpoint to disable public IP access to the control plane.

Restrict control plane access with --enable-master-authorized-networks and --master-authorized-networks=CIDR_BLOCK:

bash
gcloud container clusters create-auto CLUSTER_NAME --region=REGION \
  --enable-private-nodes \
  --enable-private-endpoint \
  --enable-master-authorized-networks \
  --master-authorized-networks=CIDR_BLOCK

Workload Identity (IAM Binding):
- Never mount raw GCP Service Account JSON keys in Pods; open gke-workload-identity/SKILL.md for KSA/GSA setup.
Autopilot Resource Requests:
- In Autopilot, CPU requests must be specified in increments of 250m (0.25 vCPU). If an unaligned CPU request (e.g., 300m) is requested, round up to the nearest 250m increment (500m / 0.5 vCPU).
- Resource requests equal limits automatically. Omit limits to allow Autopilot to set defaults matching requests.

Cluster Credentials:

Always explicitly specify --region (for regional clusters) or --zone (for zonal clusters) when fetching credentials:

bash
gcloud container clusters get-credentials CLUSTER_NAME --region=REGION --quiet

Reference Directory

Core Concepts: Architecture, cluster modes (Autopilot vs Standard), networking, scaling, and security model.
CLI Usage & Tool Reference: Tool preference hierarchy (MCP vs gcloud vs kubectl), gcloud container commands, and user preference overrides.
Client Libraries: Official Kubernetes and Google Cloud Container client libraries in Python, Go, Node.js, and Java.
MCP Usage: Connecting to and using the 23 structured GKE MCP tools for cluster management, K8s resources, and diagnostics.
Infrastructure as Code: Terraform examples for google_container_cluster (Autopilot), Kubernetes provider resources, and YAML samples.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Gke Basics AI skill do?

Why use Gke Basics on TypingMind?

Because you install it once and use it with any model. Gke Basics 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 Basics in TypingMind?

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

Which AI models can use Gke Basics?

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 Basics?

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

Is the Gke Basics 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

GKE Basics & Critical Gotchas

Key Selection Rules: Autopilot vs. Standard

Critical Gotchas & Best Practices

Reference Directory

Bundled files

Frequently asked questions

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