Official agent skill

Gke Productionize

by google in google/skills

Orchestrates comprehensive production readiness reviews and assessments for GKE clusters and workloads across scalability, security, reliability, observability, backup/DR, and cost optimization.

OfficialApache-2.0Auto-check passedDevOps & Cloud

Install Gke Productionize

skills CLI
$ npx skills add google/skills --skill gke-productionize -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install google/skills gke-productionize --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/google/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/cloud/gke-productionize .claude/skills/gke-productionize && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
gke-productionize
GitHub stars
21k
Token cost
~1.9k tokens
SKILL.md length
853 words
Files
1
Skills in repo
147
Repo updated
First seen
Licence
Apache-2.0

At a glance

Orchestrates comprehensive production readiness reviews and assessments for GKE clusters and workloads across scalability, security, reliability, observability, backup/DR, and cost optimization.

  • Works in 3 steps: Discovery Phase → Production Readiness Assessment → Production Readiness Scoring
  • Asked to productionize
  • SKILL.md covers Scope, Workflow and Adaptability Guidelines
  • Calls kubectl

What it does

Gke Productionize is an agent skill from google/skills, published by the product's own GitHub organization. Orchestrates comprehensive production readiness reviews and assessments for GKE clusters and workloads across scalability, security, reliability, observability, backup/DR, and cost optimization. Use when asked to productionize, prepare, assess, audit, or review a GKE cluster or workload before going live to production. Don't use for deep-dive single-domain implementation (use specific domain skills like gke-workload-scaling, gke-platform-security, gke-workload-security, gke-service-networking, gke-reliability…

Its SKILL.md is about 1.9k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in DevOps & Cloud, covering Observability, Container orchestration and Backup and disaster recovery. It works with Google Kubernetes Engine and Kubernetes. The repository describes itself as: Agent Skills for Google products and technologies. The licence is Apache-2.0.

When your agent uses it

  • Asked to productionize
  • Review a GKE cluster
  • Workload before going live to production
  • Deep-dive single-domain implementation (use specific domain skills like gke-workload-scaling

Example prompts

  • “Use the gke-productionize skill to orchestrate comprehensive production readiness reviews and assessments for GKE clusters and workloads across…”
  • “/gke-productionize”

Workflow steps

3 steps, taken from the step headings in SKILL.md.

  1. Discovery Phase
  2. Production Readiness Assessment
  3. Production Readiness Scoring

What it can do on your machine

Read from SKILL.md and the folder at commit 7d97937. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • kubectl

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use kubectl, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Gke Productionize loads about 1.9k tokens when it runs. Until then it costs about 136 tokens; SKILL.md has 853 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~136
When it runs · the whole SKILL.md, loaded when a task matches
~1.9k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from google/skills at commit 7d97937, republished under its Apache-2.0 licence (© google). 853 words, ~1,854 tokens.

Download SKILL.mdSave it as .claude/skills/gke-productionize/SKILL.md (or your agent's skills folder).
name
gke-productionize
description
Orchestrates comprehensive production readiness reviews and assessments for GKE clusters and workloads across scalability, security, reliability, observability, backup/DR, and cost optimization. Use when asked to productionize, prepare, assess, audit, or review a GKE cluster or workload before going live to production. Don't use for deep-dive single-domain implementation (use specific domain skills like gke-workload-scaling, gke-platform-security, gke-workload-security, gke-service-networking, gke-reliability instead).
metadata.version
1.0.0
metadata.category
Containers

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 the following block in the describe output:

    yaml
    autopilot:
      enabled: true
  • 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.

A. App Onboarding (Pre-Kubernetes)

If the application is not yet running on GKE, you MUST run the gke-app-onboarding skill for planning containerization, image building, and basic deployment.

B. Scalability & Resource Management

Ensure workloads have appropriate resources and autoscaling.

  • Action: You MUST run the gke-workload-scaling skill for configuring HPA, VPA, and resource limits.
C. Observability

Ensure adequate logging and monitoring are in place.

  • Action: You MUST run the gke-observability skill for setting up Cloud Logging, Monitoring, and Managed Prometheus.
D. Reliability

Ensure high availability and graceful degradation.

  • Action: You MUST run the gke-reliability skill for configuring regional clusters, PDBs, and health probes.
E. Security

Harden the cluster and workloads.

  • Action: You MUST run the gke-platform-security and gke-workload-security skills for Workload Identity, Network Policies, and Shielded Nodes.
  • Namespace Isolation: Ensure workloads run in dedicated namespaces with Pod Security Standards (PSS) enforced via labels.
  • Least Privilege: Ensure workloads use dedicated ServiceAccounts instead of the default ServiceAccount.
F. Backup & Disaster Recovery

Ensure stateful data is protected.

  • Action: You MUST run the gke-backup-dr skill for configuring Backup for GKE and restore procedures.
G. Edge Security & Ingress

Secure external access.

  • Action: You MUST run the gke-service-networking skill for Gateway API, Ingress, and Cloud Armor.
H. Cost Optimization

Ensure efficient use of resources.

  • Action: You MUST run the gke-cost-optimization skill for strategies on rightsizing, quotas, and Spot VMs.
Show full SKILL.md (341 more words)Show less
I. Upgrades & Maintenance Posture

Ensure a safe, predictable upgrade posture.

  • Action: You MUST run the gke-upgrades skill for release channel selection, maintenance windows/exclusions, and node pool upgrade strategy.
J. Golden Path Defaults Audit

Ensure the cluster configuration matches recommended defaults.

  • Action: You MUST run the gke-golden-path skill to compare the cluster against golden path defaults and report deviations with severity and remediation.
3. Production Readiness Scoring

After the assessment, provide a summary report with a RAG (Red, Amber, Green) status for each area and an overall readiness score. This helps prioritize remediation efforts.

Apply this rubric deterministically so repeated assessments of the same environment produce the same result:

  1. Per-domain criteria: For each assessed domain (A-J), list the concrete checks performed (from the domain skill's guidance) and classify each check as pass, fail-critical (production-blocking, e.g., no resource requests, no backups for stateful data, public control plane in a locked down environment), or fail-minor (improvement, e.g., missing VPA recommendations, no Spot usage for batch).
  2. RAG mapping (per domain):
    • Red = one or more fail-critical checks.
    • Amber = no fail-critical, but one or more fail-minor checks.
    • Green = all checks pass.
  3. Domain score: Green = 100, Amber = 50, Red = 0.
  4. Weighted overall score: weight Security, Reliability, and Backup/DR at 2x; all other assessed domains at 1x. Overall score = sum(domain score x weight) / sum(weights), rounded to the nearest integer. Exclude domains that are not applicable (e.g., Backup/DR for fully stateless workloads) from both sums and note the exclusion.
  5. Readiness verdict: >= 90 with no Red domains = "Production ready"; 70-89 with no Red domains = "Ready with follow-ups"; anything else = "Not production ready".

In the report, show the per-domain check lists, RAG status, weights, and the computed overall score.

Adaptability Guidelines

  • Single App: Focus on Health Probes, HPA, Resource Limits, PDB, and Workload Identity for that specific app.
  • Cluster Wide: Focus on Cluster Autoscaler, Multi-zonal setup, Release Channels, Maintenance Windows, and default Network Policies.
  • Proactive Execution: Proactively execute relevant skills (e.g., observability, security, scaling, reliability) to assess and propose improvements, seeking user confirmation before applying state-changing implementations.

© google, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/cloud/gke-productionize of google/skills.

Open the folder on GitHubat commit 7d97937

Compare with similar skills

Gke Productionize next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Gke Productionize compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Gke Productionize this skillgoogle/skills21k—~1.9kAutomated safety check: PassApache-2.0
Kubeshark KFL2 Filter Referencekubeshark/kubeshark12k—~3.6kAutomated safety check: PassApache-2.0
Devopsnicepkg/auto-company1922 repos~814Automated safety check: PassMIT
KubeShark for KubernetesLukasNiessen/kubernetes-skill444—~1.2kAutomated safety check: PassMIT
Kcli Cluster Deploymentkarmab/kcli653—~1.5kAutomated safety check: PassApache-2.0
Aicr Analyzing SnapshotsNVIDIA/aicr439—~3.5kAutomated safety check: PassApache-2.0

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Categories

Questions about Gke Productionize

What does Gke Productionize do?

Orchestrates comprehensive production readiness reviews and assessments for GKE clusters and workloads across scalability, security, reliability, observability, backup/DR, and cost optimization. Gke Productionize is an agent skill from google/skills, published by the product's own GitHub organization. Orchestrates comprehensive production readiness reviews and assessments for GKE clusters and workloads across scalability, security, reliability, observability, backup/DR, and cost optimization.

When should I use Gke Productionize?

Gke Productionize fits situations like: asked to productionize; review a GKE cluster; workload before going live to production; deep-dive single-domain implementation (use specific domain skills like gke-workload-scaling.

How do I install Gke Productionize in Claude Code?

Run `npx skills add google/skills --skill gke-productionize -a claude-code`. Or copy the skill folder (skills/cloud/gke-productionize in google/skills) into .claude/skills/gke-productionize in your project. Claude Code loads it when a task matches its description.

How do I install Gke Productionize in Codex?

Run `npx skills add google/skills --skill gke-productionize -a codex`. Or copy the skill folder (skills/cloud/gke-productionize in google/skills) into .agents/skills/gke-productionize in your project. Codex loads it when a task matches its description.

Can I use Gke Productionize in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add google/skills --skill gke-productionize -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/gke-productionize, .gemini/skills/gke-productionize, .github/skills/gke-productionize and .opencode/skills/gke-productionize in your project.

What does Gke Productionize need to run?

Going by SKILL.md and its folder, Gke Productionize needs the command-line tools its instructions call (kubectl).

Does Gke Productionize access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Gke Productionize safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Gke Productionize use?

Gke Productionize is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Gke Productionize use?

About 1.9k tokens (SKILL.md is roughly 7.4k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Gke Productionize?

Skills that share tags, products or a category with Gke Productionize: Kubeshark KFL2 Filter Reference (kubeshark/kubeshark, 12k stars), Devops (nicepkg/auto-company, 192 stars), KubeShark for Kubernetes (LukasNiessen/kubernetes-skill, 444 stars) and Kcli Cluster Deployment (karmab/kcli, 653 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Gke Productionize?

google (a GitHub organization, an official publisher) maintains it in google/skills, which has 21,032 GitHub stars. The repository holds 147 skills in this directory. The repository was last updated on October 8, 2026.

Source: google/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.