Generated banner for Gke Productionize
Generated logo for Gke Productionize

Gke Productionize

GoogleOfficialPublished by Google

  • Skill
  • Workflow
  • Cloud and DevOps
  • Apache-2.0
  • No issues found

From the author

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).
— the description by Google

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

The gke-productionize skill serves as a high-level orchestrator for evaluating the production readiness of Google Kubernetes Engine (GKE) environments. It guides users through a structured discovery and assessment process covering scalability, security, reliability, observability, backup, and cost management. Rather than implementing features directly, it coordinates specialized domain-specific skills to perform deep-dive audits and provides a deterministic scoring rubric to determine if a cluster or workload is ready for production deployment.

What it does

  • Performs discovery of GKE cluster configurations and workload settings.
  • Orchestrates specialized domain assessments for security, reliability, and scalability.
  • Evaluates configurations against golden path defaults and best practices.
  • Calculates a weighted production readiness score based on a RAG status rubric.
  • Provides a summary report identifying critical and minor production-blocking issues.
  • Adapts assessment scope between individual applications and cluster-wide infrastructure.

When to use it

  • When preparing a new GKE cluster or workload for its initial production launch.
  • When auditing an existing GKE environment to identify gaps in production standards.
  • When you need a structured, repeatable framework to assess operational maturity.
  • When you require a prioritized list of remediation steps before going live.

Advantages

  • Ensures consistent, deterministic readiness scoring across different environments.
  • Reduces operational risk by enforcing comprehensive multi-domain checks.
  • Provides a clear, actionable path to remediation for non-compliant configurations.
  • Integrates specialized domain knowledge through a unified orchestration workflow.

Usage examples

“Assess my GKE cluster 'prod-cluster' in 'us-central1' for production readiness.”
The agent will discover the cluster configuration, invoke relevant domain-specific skills, and generate a report with a RAG-based readiness score and remediation plan.
“Is my 'payment-service' deployment ready for production?”
The agent will audit the specific workload's resource limits, probes, and security settings, providing a readiness verdict based on the weighted scoring rubric.

Install

  1. Download the skill as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/google/gke-productionize/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip gke-productionize.zip -d .agents/skills

Documentation by Google

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.

Attribution

MUST READ: Before executing any gcloud, REST API, or MCP command, you MUST read Usage Attribution. For commands executed directly in the terminal (never in generated scripts, Terraform, or user code), prefix gcloud with CLOUDSDK_METRICS_REQUEST_ATTRIBUTION="agent-skills/1.0 (skill:gke-productionize)" and append agent-skills/1.0 (skill:gke-productionize) to User-Agent headers.

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:

    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.

Files

ATTRIBUTION.md

Attribution

This folder is an unmodified copy of the Agent Skill "gke-productionize", distributed by AutoGineer (https://autogineer.com/agent-skills).

The skill's own files are unmodified copies. AutoGineer is not affiliated with or endorsed by the author or the publisher.

Files added by AutoGineer

  • ATTRIBUTION.md: this file, written by AutoGineer

  • LICENSE: copied from LICENSE

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs Node.js

Security

No issues found by our static scan· No issues found

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Published by
Google
Publisher
Google (Official)
Source
google/skills · skills/cloud/gke-productionize · commit 2194d3b
Licence
Apache License 2.0 (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
Synced

AutoGineer is not affiliated with or endorsed by Google.

Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download gke-productionize.zip