gke-productionize

gke-productionize is a skill for Claude Code, Codex from gke-labs/kube-agents. It costs 109 tokens per session (1,210 once invoked), scanned A, a copy of gke-productionize, Apache-2.0.

A production-readiness review for Google Kubernetes Engine (GKE), Google Cloud’s managed system for running containerized applications. It coordinates checks across clusters and the applications running on them.

In plain words
What is it for?
Assessing a GKE cluster or application before launch and organizing follow-up reviews for each production-readiness area.
Why use it?
It helps find risks before a GKE workload goes live, including problems with scaling, security, reliability, monitoring, recovery, and cost.

Skill for Claude CodeCodex

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

Good fit Assessing a GKE cluster or application before launch and organizing follow-up reviews for each production-readiness area.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/gke-labs/kube-agents/gke-productionize
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add gke-labs/kube-agents --skill gke-productionize
Clone the repo
git clone --depth 1 https://github.com/gke-labs/kube-agents

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-productionize

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

Your own site · 80×15
<a href="https://agentmods.dev/skills/gke-labs/kube-agents/gke-productionize"><img src="https://agentmods.dev/badge/skills/gke-labs/kube-agents/gke-productionize.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 109 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,210 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 86% 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 $0.00109 $0.01210
Opus 5 $0.00055 $0.00605
Sonnet 5 $0.00022 $0.00242
Haiku 4.5 $0.00011 $0.00121

Measured 12d ago against content hash d4a28a8e3119, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

gke-productionize 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 12d 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

86% identical to gke-productionize — 55 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-productionize/SKILL.md · 143 lines

How it starts

The opening of the file, as written. The whole thing — 143 lines — stays where its author put it; the contents beside it link to each section on GitHub.

GKE Productionize Skill

This skill acts as a high-level orchestrator for preparing a GKE cluster and its workloads for production readiness.

[!IMPORTANT] This is a meta-skill or orchestrator skill. You are expected to invoke and run many other specialized skills listed in this document as part of the overall productionization process. Do not attempt to implement all production readiness features directly within this skill; instead, use this skill to assess the environment and then delegate to the specific skills for each domain.

Scope

This skill is adaptable to:

  • A single application (already on Kubernetes or not).
  • A set of applications.
  • A target cluster.

Workflow

1. Discovery Phase

Before making recommendations, discover the current state of the environment.

Cluster Discovery

Run these commands to understand the cluster setup:

  • Check cluster details: gcloud container clusters describe {cluster_name} --location {location} --project {project}
  • Check for Autopilot vs Standard: Look for autopilot: true in the describe output.
  • Check release channel: Look for releaseChannel.
Workload Discovery

If a specific application is targeted, discover its configuration:

  • Get deployment/statefulset details: kubectl get deployment {app_name} -n {namespace} -o yaml
  • Check for dedicated namespace and labels: kubectl get namespace {namespace} -o yaml (Look for Pod Security Standards labels).
  • Check for dedicated service account usage: kubectl get pods -n {namespace} -o custom-columns="NAME:.metadata.name,SERVICE_ACCOUNT:.spec.serviceAccountName"
  • Check for resource requests and limits.
  • Check for liveness, readiness, and startup probes.
  • Check for HPA: kubectl get hpa -n {namespace}
  • Check for PDB: kubectl get pdb -n {namespace}
  • Check for NetworkPolicies: kubectl get networkpolicy -n {namespace}

2. Production Readiness Assessment

Before implementation, you MUST run the skills for each relevant specialized area listed below and incorporate its guidance into your assessment and plan. Failure to do so will result in a non-compliant production configuration.

Read the full file on GitHub · 143 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. 12d ago First seen · 143 lines · 109 tokens per session scan A d4a28a8e3119

Subscribe to this mod's changes

gke-productionize is a skill published in the GitHub repository gke-labs/kube-agents (54 stars, last pushed today), licensed Apache-2.0. It adds 109 tokens to every session and 1,210 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. It is 86% identical to gke-productionize, differing in 55 lines, and is treated as a copy.

Related

Other skills, from other repositories

deploy-docker-compose

Run the Omnigent server as a Docker compose stack (server + Postgres) on any Docker host — your laptop, a VPS, EC2 by hand, or as the base layer of any container-platform deploy. Invoke when the user wants to build the image, bring up the compose stack, debug the stack on a host they already have, or extend the stack…

omnigent-ai/omnigent · 84 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

securing-kubernetes-on-cloud

This skill covers hardening managed Kubernetes clusters on EKS, AKS, and GKE by implementing Pod Security Standards, network policies, workload identity, RBAC scoping, image admission controls, and runtime security monitoring. It addresses cloud-specific security features including IRSA for EKS, Workload Identity for…

xalgorix/xalgorix · 80 tokens

detecting-privilege-escalation-in-kubernetes-pods

Detect and prevent privilege escalation in Kubernetes pods by monitoring security contexts, capabilities, and syscall patterns with Falco and OPA policies.

xalgorix/xalgorix · 40 tokens

implementing-rbac-hardening-for-kubernetes

Harden Kubernetes Role-Based Access Control by implementing least-privilege policies, auditing role bindings, eliminating cluster-admin sprawl, and integrating external identity providers.

xalgorix/xalgorix · 41 tokens

docker-socket-mount

Docker / containerd socket mounted into a container → host RCE. Common in CI runners, GitOps controllers (ArgoCD, Flux), and 'Docker-in-Docker' setups. Single-command escape via docker run --rm --privileged -v /:/host alpine chroot /host.

PurpleAILAB/Decepticon · 67 tokens