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kube-agents
kube-agents contains 25 collected skills from gke-labs, with repository-level occupation coverage and site-owned skill detail pages.
Skills in this repository
Autonomously poll, triage, investigate, and resolve unaddressed open issues on our target GitHub repository strictly within authorized scope.
Configures, optimizes, and troubleshoots GKE ComputeClasses. Use when configuring Spot VMs with on-demand fallback, targeting specific accelerators (GPUs/TPUs) or machine families, restricting ComputeClass access, or debugging pending pods related to node pool auto-creation. Do not use for cluster-level Node Auto Provisioning configuration or general GKE cluster creation.
Audit, monitor, and debug the logging, tracing, metrics, and API/dashboard observability of the Platform Agent.
Standard Operating Procedure (SOP) for generating and updating secure, compliant, and cost-effective GKE manifests.
Systematic Standard Operating Procedure (SOP) for diagnosing GKE workload failures, crash loops, resource OOMs, mounting errors, and connectivity timeouts.
Reviews Kubernetes Pod security contexts for workload-level isolation and privilege escalation risks.
Propose declarative configuration updates securely by committing file changes and submitting GitHub Pull Requests (PRs) for SRE review.
Reviews Kubernetes Admission Control (Webhooks, VAP/MAP) for vulnerabilities.
Orchestrates comprehensive Kubernetes security audits specifically tailored for AI agent workloads.
Orchestrates comprehensive Kubernetes security reviews.
Analyzes Kubernetes project architecture and resources to build context before performing specific security reviews.
Reviews Kubernetes RBAC configurations for permissions and privilege escalation risks.
Reviews Kubernetes and application audit logging for AI agents, ensuring tamper-proof observability.
Reviews AI agent Kubernetes configurations for credentials exposed to agents.
Reviews Kubernetes configurations to prevent data exfiltration by AI agents.
Reviews Kubernetes network and firewall configurations specifically for AI agent execution sandboxes.
Reviews AI agent architectures (API gateways, WAFs, input sanitization) for prompt injection risks.
Reviews AI agent execution sandboxes for code escape and lateral movement risks.
Reviews Kubernetes Gateway API configs (Gateway, HTTPRoute, etc.) for security risks.
Reviews Kubernetes namespace configurations for workload isolation, multi-tenancy, and boundary defense.
Reviews Kubernetes network configurations (NetworkPolicies, Services, Ingress) for isolation and exposure risks.
Reviews Kubernetes manifests for node boundary violations and risks of node-to-cluster privilege escalation.
Reviews Kubernetes ServiceAccount configurations for identity management and least privilege boundaries.
Reviews Kubernetes storage configurations, PVs, and VolumeMounts for data leakage and privilege escalation risks.
Reviews agent skill definitions for token efficiency, clarity, and standardized formatting.