GKE Production Golden Path
Set production-oriented GKE defaults, readiness checks, and decision guardrails for cluster design.
The skill explicitly forbids requesting or emitting secrets, requires clarification for Day-0 decisions, and supplies several security-oriented defaults with trade-off guidance. Deductions apply because create_cluster/update_cluster can cause external changes without an explicit pre-mutation confirmation requirement, rollback procedure, or defined permission scope; data-flow disclosure, dependency security, and recovery boundaries are incomplete.
The rules, required inputs, defaults, and YAML asset are broadly consistent and key configuration paths are statically traceable. Deductions apply because there are no tool parameter contracts, execution procedures, abnormal-input handling, or diagnostic failure messages, and the document references a gke-basics CLI reference not included in the supplied material. Static review provides no execution evidence, so the score remains at or below 10.
The audience and use cases are reasonably clear: GKE design, production-readiness checks, and default configuration, with a stated non-fit for dedicated node autoscaling. The skill also respects a user preference for gcloud or kubectl. Deductions apply because input-to-output formats, non-fit boundaries, and semantic trigger conditions remain broad; Chinese-language support and mainland-China reachability are not addressed, and the core capability depends on a Google Cloud environment.
The documentation includes front matter, layered rules, required inputs, defaults, configurable settings, guardrails, and a separate YAML asset. The README provides installation, licensing, support, and contribution information, identifies Apache-2.0, and states that the repository is under active development. Deductions apply because the skill lacks its own version policy, changelog, named maintenance responsibility, dependency versions, examples, FAQ, and systematic troubleshooting; some guidance depends on another skill or an unavailable referenced resource.
The skill provides concrete Autopilot production defaults, Day-0 decision prompts, and deviation trade-offs, which could directly support cluster design or auditing. Deductions apply because no actual output format, audit-report example, or verified key-path result is supplied, and applicability across GKE versions, organizational policies, and workloads is not established. Static calibration limits effectiveness to 7.
The supplied Markdown rules and YAML configuration are auditable, and the YAML states that some content was condensed from a gcloud describe export. Deductions apply because there are no CI workflows, committed tests, version-pinned official citations, or third-party execution records to independently reproduce and corroborate the claims; evidence coverage is therefore thin under static review.
- Add explicit user confirmation, change scope, required permissions, and rollback or recovery procedures before invoking create_cluster or update_cluster.
- Validate the YAML against the target GKE version, organization policies, and current Autopilot API, especially maintenance exclusions, monitoring components, and security settings.
- The referenced gke-basics CLI reference and resources/recommended-ap.yaml were not supplied, so their completeness cannot be verified.
- The skill does not document Chinese interaction or mainland-China reachability; workflows relying on Google Cloud control-plane access, MCP, or gcloud may be constrained by network and credential environments.
What does this skill do, and when should you use it?
This skill supports GKE cluster design, production-readiness verification, and configuration audits. It uses a recommended Autopilot production configuration as the baseline and explains security or operational trade-offs when users choose different settings. It highlights Day-0 decisions such as networking, private nodes, subnets, and IP allocation, and reports severity and remediation for deviations in existing clusters. It is not intended for dedicated node autoscaling setup; the source directs that use case to gke-scaling.
Collects project ID, region, cluster name, environment, networking, scale, and cost inputs; uses the MCP tools get_cluster, create_cluster, and update_cluster to inspect or change clusters; applies defaults such as Autopilot, private nodes, Workload Identity, Cloud DNS, monitoring, logging, storage CSI drivers, and restricted Pod Security Standards; documents decisions and rationale; and audits existing clusters against the golden path with deviation severity and remediation.
- A platform engineer is creating a production GKE Autopilot cluster and wants consistent security and operations defaults.
- A cloud architect is reviewing networking, private-node, subnet, and IP-allocation decisions before cluster creation.
- An operations team is auditing an existing GKE cluster for deviations from the recommended configuration.
- An application team needs to change pod density, disk type, or machine type and wants the documented trade-offs.
- A GKE administrator wants a review of the implications before changing a golden-path default.
What are this skill's strengths and limitations?
- Covers production defaults across security, networking, observability, logging, storage, and operations.
- Clearly separates Day-0 and Day-1 decisions and requires rationale for deviations.
- Defines MCP operations for retrieving, creating, and updating clusters.
- Includes auditing guidance with severity and remediation reporting.
- Focused on the GKE golden path rather than general Kubernetes configuration.
- Does not specialize in node autoscaling.
- MCP connection, permission, and parameter details are not documented in the supplied sources.
- The supplied sources provide no test suite, platform validation results, or execution-failure examples.
How do you install this skill?
Run npx skills add google/skills, then select skills/cloud/gke-golden-path from the repository. The README does not document a more specific client installation location or installed directory layout.
How do you use this skill?
Use a prompt such as: “Create a production GKE cluster named prod-gke in us-central1 for project my-project using the golden path defaults.” Provide or confirm the project ID, region, and cluster name; include networking, scale expectations, and cost constraints when relevant. Day-0 decisions should be clarified before proceeding. The source points to skills/cloud/gke-golden-path/assets/golden-path-autopilot.yaml for the full cluster-level policy, but does not document exact execution output or failure-handling procedures.