Kubernetes Automation

Automate Kubernetes cluster management by deploying, scaling, monitoring, troubleshooting resources, and managing configurations efficiently.

Security Scan

The skill's description matches Kubernetes automation, but the runtime instructions implicitly require access to kubectl, kubeconfig, and cluster credentials (and can read/manage secrets and export cluster state) while the metadata declares no required binaries, env vars, or config paths — a clear mismatch that raises risk and ambiguity.

Purpose & Capability

The name and SKILL.md purpose (deploy/scale/monitor/troubleshoot K8s) are internally consistent, but the metadata declares no required binaries, environment variables, or config paths even though the instructions explicitly assume kubectl is configured and that the agent has cluster access (and optionally Helm/Kustomize). The skill should have declared KUBECONFIG or ~/.kube/config and kubectl as requirements.

Instruction Scope

Instructions tell an agent to perform cluster operations (deploy, scale, debug, backup/restore, manage secrets, service-mesh config). Those operations necessarily require reading cluster credentials (kubeconfig) and can access or export sensitive cluster state (including secrets). The SKILL.md does not constrain or describe how credentials/configs are accessed or protected.

Install Mechanism

This is an instruction-only skill with no install spec or code files, so nothing will be written to disk by an installer. That lowers supply-chain risk, but does not remove runtime risk from use of external tools (kubectl) and cluster credentials.

Credentials

The skill declares no required environment variables or config paths, yet it implicitly requires access to kubeconfig/cluster credentials and may need cloud provider credentials for some clusters. It also mentions managing secrets and backup/restore which are high-sensitivity operations — requesting no explicit credential/config declarations is disproportionate and opaque.

Persistence & Privilege

always is false (good). The skill can be invoked autonomously by the agent (platform default). Combined with the implicit need for cluster credentials and ability to change or export cluster state, autonomous invocation increases blast radius — consider limiting autonomous invocation or requiring explicit user approval before cluster-affecting operations.

What to consider before installing

This skill describes legitimate Kubernetes automation, but its SKILL.md assumes kubectl and access to a kubeconfig/cluster without declaring those requirements. Before installing, verify: (1) where and how the skill will obtain kubeconfig or cluster credentials (KUBECONFIG or ~/.kube/config), (2) whether the agent will be allowed to manage secrets or export cluster state (these are sensitive), (3) limit the agent's RBAC permissions to least privilege and prefer a non-production/test cluster for evaluation, (4) consider disabling autonomous invocation or requiring explicit confirmation for any destructive or data-exporting actions, and (5) ask the publisher to update metadata to list required binaries (kubectl), config paths, and exact credentials needed. If you cannot confirm these, treat the skill as risky for production clusters.

Kubernetes cluster management and automation for AI agents. Deploy, scale, monitor, and troubleshoot K8s resources effortlessly.

Overview

A comprehensive Kubernetes assistant that helps agents manage clusters, deploy applications, and handle common K8s operations.

Features

• Resource Deployment: Deploy pods, deployments, services, and more
• Scaling Operations: Scale deployments, manage replicas
• Health Monitoring: Check pod status, resource usage, events
• Troubleshooting: Diagnose failed pods, debug issues
• Config Management: Manage configmaps, secrets, namespaces
• Service Mesh: Work with Istio, Linkerd configurations
• Backup/Restore: Export and import cluster state

Commands

Deploy Application

deploy app from deployment.yaml to production namespace

Check Status

get pod status in production namespace

Scale Deployment

scale my-app to 5 replicas

Debug Issue

debug pod crash in default namespace

Use Cases

• Kubernetes deployment automation
• Cluster health monitoring
• Application scaling
• Troubleshooting pod failures
• Configuration management
• DevOps workflow automation

Requirements

• kubectl configured
• Kubernetes cluster access
• Optional: Helm, Kustomize