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This skill follows a structured workflow to catch insecure workload defaults, resource starvation, network exposure, privilege sprawl, fragile rollouts, and API drift. It loads platform-specific references for EKS, GKE, AKS, or OpenShift only when needed, then produces remediated manifests with validation steps: kubectl dry-run, schema checks, and policy scans. The workflow is built for AI-assisted developers, platform engineers, and SREs who generate or review Kubernetes configurations.
Compatible AgentsThe repository documents support for these agents. The skills may also work with other agents that can load SKILL.md files, but they may need some setup or small changes.
Create or edit `AGENTS.md` in the repository root and add the documented `## Kubernetes` section directing Codex to follow `.kubernetes-skill/SKILL.md` and load references from `.kubernetes-skill/references/` as needed.
Review one Kubernetes manifest for its highest-risk failure modes
Uses kubernetes-skill
A local Kubernetes YAML file path replacing `<manifest-path>`
Target Kubernetes cluster version and distribution, if known
Target namespace and environment criticality
Deployment method and policy enforcement details, if known
Use the kubernetes-skill to review the Kubernetes manifest at `<manifest-path>`. The path is a placeholder for one local YAML file. Use the supplied cluster version, distribution, namespace, environment, deployment method, and policy enforcement details; state assumptions for anything unknown. Identify the most relevant failure modes, propose a small remediation with tradeoffs, and provide validation and rollback steps. Do not apply the manifest.
Claude
Codex