GKE Cluster Autoscaler Guide

The document explicitly rejects injected identifiers, treats pasted logs and YAML as untrusted data, and enforces a restricted naming pattern, improving input safety and data-flow clarity. Points are deducted because broad user confirmation, pre/post-change checks, rollback procedures, and least-privilege guidance are incomplete. It also recommends granting the Editor role and mentions manually deleting instances or setting minimum nodes to zero without adequately describing impact and recovery.

The skill identifies major dependencies such as kubectl, jq, and gcloud, documents several failure identifiers, and supplies log and blocker-scanning scripts. Points are deducted because this is a static review with no execution evidence; key paths depend on specific GKE log schemas, the current kube context, tool variants, and permissions, with no committed test suite. Some parsing and fallback paths may fail for complex project, cluster, or resource names.

Trigger terms, target scenarios, and the boundary with the ComputeClass skill are relatively clear, covering scaling, capacity shortages, and troubleshooting. Points are deducted because input/output formats, non-fit scenarios, and the full Autopilot/version/permission boundaries are not explicit. There is no Chinese-language guidance or mainland-China reachability assessment; the core GKE service depends on overseas cloud endpoints whose availability must be confirmed in the user environment.

The structure is readable, with references and assets supporting progressive disclosure. The README documents installation, and repository-level license, official provenance, and issue-reporting paths are clear. Points are deducted because the selected skill lacks its own version, changelog, named maintainer, and update commitment. Installation details, permission matrices, FAQs, and known limitations are incomplete, and several product/version claims require ongoing verification.

The material directly addresses enablement, tuning, scale-up/down blockers, and log diagnosis, with a usable YAML example and operational scripts. Static evidence supports completion of the core guidance task. Points are deducted because no representative outputs or execution results are verified; users must still validate GKE versions, API schemas, IAM, quotas, and actual log fields, and some recommendations may require substantial environment-specific correction.

The skill references repository-local documentation, scripts, and log fields, providing some auditability, and script comments describe expected data shapes. Points are deducted because there is no third-party evidence, committed test suite, CI coverage, or independent reproduction record. External documentation links appear only in the source material, so key version, permission, and product-behavior claims cannot be cross-checked from the supplied files.