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gke-basics

Manages core GKE cluster provisioning, credentials, Autopilot vs Standard selection, and workload deployment

Install / Use

npx skills add google/skills --skill gke-basics

Installs into whichever agent you are using.

About this skill
📄

SKILL.md

Installable skill definition

Quality Score

79/100

Category

Security

Supported Platforms

Zed

Our assessment of gke-basics

gke-basics scores 79/100 on our quality scale, 548th of 653 Security skills we index.

Its SKILL.md is 3.6 KB long, split into 4 sections and no code examples: a solid amount of guidance for an agent.

With 20,340 GitHub stars, it is one of the more widely adopted skills in the catalogue.

Substance
26/30
Structure
8/20
Description
12/15
Adoption
18/20
Freshness
15/15

Maintenance, license and trust

  • The repository was last updated 3 days ago, so gke-basics is actively maintained.
  • It is released under the Apache-2.0 license, a permissive license that allows use, modification and commercial use with attribution.
  • Its trust signals score 100/100, with no cautions. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.

gke-basics compared with similar skills

All 4 of these similar skills score higher than gke-basics; compare them before choosing.

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gke-basics (this skill)by google7920.3k3d agoSKILL.md
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Frequently asked questions

How do I install gke-basics?
Run npx skills add google/skills --skill gke-basics. The install tabs above show the steps for each supported agent.
Which AI agents does gke-basics work with?
It is written for Zed, as a SKILL.md file. Other agents that read the same format can often use it too.
Is gke-basics safe to use?
It is Apache-2.0-licensed and scores 100/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
Is gke-basics still maintained?
The repository was last updated 3 days ago, so gke-basics is actively maintained.

name: gke-basics metadata: version: "1.1.0" category: Containers description: >- Manages core GKE cluster provisioning, credentials, Autopilot vs Standard selection, and workload deployment. Use when creating GKE clusters, fetching kubectl credentials, configuring Workload Identity, or deciding between Autopilot and Standard modes. Don't use for specialized GKE networking (use gke-networking), advanced security hardening (use gke-platform-security or gke-workload-security), or cluster upgrades (use gke-upgrades).

GKE Basics & Critical Gotchas

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

  1. 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:
      gcloud container clusters create-auto CLUSTER_NAME --region=REGION \
        --enable-private-nodes \
        --enable-private-endpoint \
        --enable-master-authorized-networks \
        --master-authorized-networks=CIDR_BLOCK
      
  2. Workload Identity (IAM Binding):

    • Never mount raw GCP Service Account JSON keys in Pods.
    • Annotate the Kubernetes ServiceAccount (KSA) to bind to the Google Service Account (GSA):
      metadata:
        annotations:
          iam.gke.io/gcp-service-account: GSA_NAME@PROJECT_ID.iam.gserviceaccount.com
      
  3. 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.
  4. Cluster Credentials:

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

Related Skills

View on GitHub
GitHub Stars20.3k
CategorySecurity
Updated3d ago
Forks1.7k

Languages

Python

Trust signals

100/100

From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.

No cautions