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Gke Productionize

OrganizationPopular
google
gke-productionize

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).

Overview

Publishergoogle
Repositoryskills
Skill namegke-productionize
Stars
20.5K
Forks
1.7K
Bundled files
Instructions only
LicenseApache-2.0
Links
  • 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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by google on GitHub. Read the source before you install it.

Installation

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

1

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.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/google/skills/tree/main/skills/cloud/gke-productionize
  4. 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.
2

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-productionize .claude/skills/gke-productionize
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Gke Productionize 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 Productionize 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 Productionize 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 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.
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.

Frequently asked questions

What does the Gke Productionize AI skill do?

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).

Why use Gke Productionize on TypingMind?

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

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

Which AI models can use Gke Productionize?

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 Productionize?

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

Is the Gke Productionize 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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