Skill UI

A comprehensive guide for deploying complex integrations to production cloud environments (GCP, Kubernetes). This skill covers best practices including creating multi-stage Dockerfiles, implementing robust health and readiness checks, and utilizing cloud tools like gcloud and kubectl for seamless, secure deployment with secret management.

This skill guides users on operating the FrontendIntegration resources and the associated frontend-forge extension using kubectl. It covers the full lifecycle management: creating FI from complete YAML, updating status, enabling/disabling, troubleshooting build output, and managing the extension's installation plan. Ideal for DevOps engineers responsible for robust CI/CD pipeline management.

This guide details securing the CAST AI integration across multiple layers. It covers best practices for API key management (including rotation and secrets storage), implementing least-privilege RBAC using 'kubectl', deploying the Kvisor security agent for runtime scanning, and setting up Kubernetes Network Policies to restrict egress traffic. Use this when hardening production clusters or performing security audits.

A comprehensive guide and set of tools for establishing a local development workflow tailored for CoreWeave GPU deployments. This includes steps for building Docker containers, validating Kubernetes YAML manifests using dry-run, and managing the full deployment lifecycle for AI inference services before pushing to the cloud.

A comprehensive diagnostic guide for common issues encountered with CAST AI agents, Kubernetes autoscaling, API connectivity, and resource provisioning. Covers scenarios like agent crashes, 401 errors, and scaling failures, providing specific commands (kubectl, curl, helm) and detailed fixes for robust troubleshooting.

This guide demonstrates how to deploy GPU workloads on the CoreWeave platform using Kubernetes (kubectl). It covers deploying vLLM inference services for large language model (LLM) testing and running batch CUDA jobs for benchmarking. This is ideal for users running their first GPU workloads, testing inference pipelines, or verifying cluster access on bare-metal GPU nodes (A100, H100).

This guide provides step-by-step instructions to configure access to the CoreWeave Kubernetes Service (CKS). It covers downloading the kubeconfig file, setting API tokens, and verifying GPU node access using standard `kubectl` commands. This is essential for deploying AI/ML workloads on bare-metal GPU infrastructure.

This skill provides methods for managing GPU quota limits and rate restrictions for CoreWeave cloud services. It includes using `kubectl` to check resource quotas and demonstrates implementing robust request queuing mechanisms using Python's `asyncio` library, ensuring stable and efficient resource allocation for high-concurrency inference endpoints.

This comprehensive skill guides users through the entire Fluid storage extension lifecycle within KubeSphere. It allows for installing, upgrading, and uninstalling the Fluid extension, and managing various data resources. Users can generate YAML manifests for Datasets and specialized runtimes (Alluxio, JuiceFS, Thin), perform data loading/cache warming, scale operations, and troubleshoot issues using both kubectl and KubeSphere APIs.

A comprehensive guide for managing the full lifecycle of OpenKruise within the KubeSphere platform. This skill assists users with installation, upgrading, querying status (CRDs, Pods, logs), generating manifests for advanced workloads (SidecarSet, CloneSet), performing rollouts, and troubleshooting complex runtime issues like webhooks or missing configurations, utilizing both kubectl and KubeSphere APIs.

This skill provides a comprehensive solution for deploying containerized applications to Oracle Cloud Infrastructure (OCI). It supports two primary deployment paths: OKE (Oracle Kubernetes Engine) for full Kubernetes orchestration, and Container Instances for a simpler, serverless alternative. Use this skill for automating the entire lifecycle, including pushing images to OCIR, provisioning OKE clusters via Python SDK, and executing Kubernetes deployments using kubectl.

A powerful, kubectl/docker-style CLI tool designed to manage and submit distributed computing jobs on the Qizhi (启智) platform. Use it for tasks like logging in, checking resource availability, discovering workspaces, and submitting complex GPU, CPU, or batch jobs efficiently.