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-scaling, gke-platform-security, gke-workload-security, gke-service-networking, gke-reliability instead).
npx skills add https://github.com/google/skills --skill gke-productionize
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.
This skill is adaptable to:
Before making recommendations, discover the current state of the environment.
Run these commands to understand the cluster setup:
--location {location} --project {project}`
autopilot: true in the describeoutput.
releaseChannel.If a specific application is targeted, discover its configuration:
{namespace} -o yaml`
-o yaml` (Look for Pod Security Standards labels).
-o
custom-columns="NAME:.metadata.name,SERVICE_ACCOUNT:.spec.serviceAccountName"`
kubectl get hpa -n {namespace}kubectl get pdb -n {namespace}kubectl get networkpolicy -n {namespace}**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.**
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.
Ensure workloads have appropriate resources and autoscaling.
gke-workload-scaling skill for configuringHPA, VPA, and resource limits.
Ensure adequate logging and monitoring are in place.
gke-observability skill for setting up CloudLogging, Monitoring, and Managed Prometheus.
Ensure high availability and graceful degradation.
gke-reliability skill for configuringregional clusters, PDBs, and health probes.
Harden the cluster and workloads.
gke-platform-security andgke-workload-security skills for Workload Identity, Network Policies, and
Shielded Nodes.
Pod Security Standards (PSS) enforced via labels.
of the default ServiceAccount.
Ensure stateful data is protected.
gke-backup-dr skill for configuring Backupfor GKE and restore procedures.
Secure external access.
gke-service-networking skill for Gateway API,Ingress, and Cloud Armor.
Ensure efficient use of resources.
gke-cost-optimization skill for strategies onrightsizing, quotas, and Spot VMs.
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.
Workload Identity for that specific app.
Channels, Maintenance Windows, and default Network Policies.
observability, security, scaling, reliability) to assess and propose
improvements, seeking user confirmation before applying state-changing
implementations.
Skill converted from mcp-deploy-manage-agents.prompt.md
Use this skill when the user wants to launch a new AltClaw, OpenClaw, PicoClaw, or Ottie deployment through Cloud Claw. Covers the same user-facing fields and constraints exposed in the Cloud Claw UI, using the local altllm cloud-claw-* commands. Do NOT use for post-launch lifecycle tasks like start/stop/delete/logs; use cloud-claw-manage-vm.
Build hosted agents using Azure AI Projects SDK with ImageBasedHostedAgentDefinition. Use when creating container-based agents in Azure AI Foundry.
Build MCP (Model Context Protocol) servers on Cloudflare Workers with tools, resources, and prompts.
Chain agent outputs as inputs in sequential or parallel pipelines for data flow orchestration
Audit cloned or reimplemented websites for fidelity gaps, tracking scripts, source-brand and language residue, placeholders, and risky external dependencies. Use before handoff or deployment, or when asked to review a website clone for cleanup and readiness.
> Install and operate Hermes Tweet, a Hermes Agent plugin for X/Twitter research, timeline reading, tweet analysis, and approval-gated tweet actions. Use this skill when installing Hermes Tweet, researching X/Twitter accounts, monitoring launch signals, investigating mentions, auditing giveaways, or preparing guarded tweet actions. Use proactively when a Hermes Agent workflow needs current X/Twitter context. Requires XQUIK_API_KEY for read and action tools.
Handles LLM-as-judge evaluation workflows on Arize including creating/updating evaluators, running evaluations on spans or experiments, managing tasks, trigger-run operations, column mapping, and continuous monitoring. Use when the user mentions create evaluator, LLM judge, hallucination, faithfulness, correctness, relevance, run eval, score spans, score experiment, trigger-run, column mapping, continuous monitoring, or improve evaluator prompt.
Take google/gke-productionize from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.