gke-cluster-creation

gke-cluster-creation is a skill for Claude Code, Codex from gke-labs/kube-agents. It costs 92 tokens per session (3,522 once invoked), scanned A, a copy of gke-cluster-creation, Apache-2.0.

Guidance for creating, provisioning, and checking Google Kubernetes Engine clusters using predefined setups such as Autopilot, Standard Regional, GPU inference, and AI Hypercompute. It helps gather the required project, location, name, and workload details before creation.

In plain words
What is it for?
Use it to plan or create clusters, select a cluster template and mode, configure networking, inspect existing clusters, monitor creation operations, and audit readiness.
Why use it?
It helps turn cluster requirements into a reviewed configuration while making trade-offs between cost, availability, and infrastructure control clear. It also supports production-readiness checks and highlights networking choices that are hard to change later.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Needs its repository: it runs a file that does not travel with it, so clone the repository first. The line is python3 /opt/data/scripts/cluster_agent_profile.py create \.

Good fit Use it to plan or create clusters, select a cluster template and mode, configure networking, inspect existing clusters, monitor creation operations, and audit readiness.

Compare 6 skills from other repositories ↓
Install

Getting it into your agent

It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.

Clone the repo
git clone --depth 1 https://github.com/gke-labs/kube-agents
agentmods
npx agentmods add skills/gke-labs/kube-agents/gke-cluster-creation

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for gke-cluster-creation

README.md
[![agentmods](https://agentmods.dev/badge/skills/gke-labs/kube-agents/gke-cluster-creation/github.svg)](https://agentmods.dev/skills/gke-labs/kube-agents/gke-cluster-creation)
Your own site
<a href="https://agentmods.dev/skills/gke-labs/kube-agents/gke-cluster-creation"><img src="https://agentmods.dev/badge/skills/gke-labs/kube-agents/gke-cluster-creation/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for gke-cluster-creation

Your own site · 80×15
<a href="https://agentmods.dev/skills/gke-labs/kube-agents/gke-cluster-creation"><img src="https://agentmods.dev/badge/skills/gke-labs/kube-agents/gke-cluster-creation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 92 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,522 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin 89% copy Near-identical to another mod in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00092 $0.03522
Opus 5.5 $0.00037 $0.01409
Sonnet 5.5 $0.00018 $0.00704
Haiku 4.5 $0.00009 $0.00352

Measured 4d ago against content hash 22e8944021eb, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

gke-cluster-creation scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

This is a copy

89% identical to gke-cluster-creation — 207 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

agents/platform/skills/gke-cluster-creation/SKILL.md · 352 lines

How it starts

The opening of the file, as written. The whole thing — 352 lines — stays where its author put it; the contents beside it link to each section on GitHub.

GKE Cluster Creation

This reference guides creating Google Kubernetes Engine (GKE) clusters by providing a set of best-practice templates and guiding through mode selection and customization. The golden path Autopilot configuration is the default for a new cluster whose workload states no requirement it cannot meet.

MCP Tools: list_clusters, get_cluster, list_operations, get_operation

Workflow

  1. Discover context: Use list_clusters to see existing clusters. Use gcloud config get-value project if project unknown.
  2. Gather inputs: project_id, location (region or zone), cluster_name, environment type. If missing essential details, ask the user before taking action.
  3. Select mode & explain trade-offs: When the request names a workload the cluster is for ("with Agent Substrate installed"), read that workload's install requirements before choosing anything (SOUL.md §3, item 4.1): the Developer Knowledge MCP for what Google documents, the project's own repository (inspect-repository) otherwise. A requirement it states (Standard mode, a machine series, beta APIs, a node label, a bucket or IAM binding it expects) overrides every template default below, and the pull request body lists each one with its source. Search-result snippets are not requirements. If the user hasn't specified a template or mode, present the available templates (e.g., Autopilot, Standard Regional, GPU Inference, AI Hypercompute) and explain key trade-offs (Cost vs. Availability, Autopilot vs. Standard node management).
  4. Configure networking: auto-create subnet (default) or bring-your-own.
  5. Review golden path settings: present the default configuration block (the template's settings, with step 3's workload requirements applied over them) as the configuration the pull request will carry.
  6. Propose: Write the configuration as a pull request in the format the GitOps repository's provisioning/ path already holds: Config Connector YAML through the gcp-config-connector skill, Terraform HCL through the gcp-terraform skill. An empty provisioning/ path or no usable repository is a stated stop (SOUL.md §3, items 6 and 7). Never create the cluster directly: the gke MCP server offers no create tool, and the broker refuses gcloud container clusters create.
  7. Track: Once the pull request merges and the reconciler starts the create, use get_operation to monitor its progress.
  8. Verify: Use get_cluster with readMask="*" to confirm the settings the pull request carried applied.

Read the full file on GitHub · 352 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 4d ago Changed · +1 lines 22e8944021eb
  2. 5d ago Changed · +12 lines ffa97a7dcab3
  3. 8d ago Changed · -64 lines · +10 tokens per session 44543234f669
  4. 1mo ago Changed a71bc91fdfe4
  5. 1mo ago Changed · +10 lines 78cadc06d01c
  6. 1mo ago First seen · 393 lines · 82 tokens per session scan A 80515638b48c

Subscribe to this mod's changes

gke-cluster-creation is a skill published in the GitHub repository gke-labs/kube-agents (116 stars, last pushed today), licensed Apache-2.0. It adds 92 tokens to every session and 3,522 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 89% identical to gke-cluster-creation, differing in 207 lines, and is treated as a copy.

Related

Other skills, from other repositories

openshell-cli

Guide agents through using the OpenShell CLI (openshell) for sandbox management, gateway registration, provider configuration and refresh, profile management, policy iteration, settings, service exposure, BYOC workflows, and attached-provider inference. Covers basic through advanced multi-step workflows. Trigger…

NVIDIA/OpenShell · 134 tokens

azd-deployment

Deploy containerized frontend + backend applications to Azure Container Apps with remote builds, managed identity, and idempotent infrastructure.

sickn33/agentic-awesome-skills · 29 tokens

cdf-orchestration-setup

Use when the user asks to deploy, install, reinstall, redeploy, upgrade, or connect to the CDF Orchestration Airflow controller. Owns the end-to-end deploy sequence and runs the cdf-orchestration-preflight audit first. Covers make setup, controller image builds, Helm installation, controller connection…

NVIDIA/skills · 105 tokens

langbot-deploy

Deploy and configure a LangBot instance — Docker / Docker Compose, Kubernetes, the config.yaml model, the Box sandbox runtime, the plugin runtime, and the global API key. Use when installing, deploying, upgrading, or configuring LangBot in production or self-hosted environments. Triggers on "deploy langbot", "langbot…

langbot-app/LangBot · 104 tokens

compute-env-setup

Set up a compute environment on a remote provider so Claude Science jobs can run there. Covers direct SSH/conda hosts, Slurm clusters, container-via-bridge runners, and managed-API providers (Modal, GCP, RunPod). Use when standing up a new provider, porting an env to a different backend, adding a tool that needs its…

UnicomAI/wanwu · 134 tokens

compute-env-setup

Set up a reproducible Feynman compute environment for research jobs. Use when a task needs Python/R packages, GPU libraries, containers, Modal, SSH, caches, or managed model runtime setup.

companion-inc/feynman · 45 tokens