Skill UI

A comprehensive guide to setting up and managing Continuous Integration and Continuous Deployment (CI/CD) pipelines within KubeSphere. This tool integrates with Jenkins, supports both graphical editing and Jenkinsfile-based workflows, and enables automated deployments across multi-cluster environments using ArgoCD for GitOps management. Ideal for managing the full software delivery lifecycle.

OpenSearch is a distributed search and analytics engine integrated into the KubeSphere WizTelemetry Observability Platform. It is crucial for storing, searching, and analyzing various types of observability data, including application logs, system events, audit records, and notification history. This guide covers installation, configuration, and operational best practices for managing the OpenSearch cluster within KubeSphere.

This skill provides read-only functionality to query and list comprehensive information about KubeSphere clusters. It is designed for viewing cluster lists, checking current status, retrieving detailed specifications, and identifying component versions across multiple clusters. IMPORTANT: This operation is strictly read-only. Never use these commands for creating, updating, or deleting resources; always use the KubeSphere console for management actions.

This comprehensive skill guides users through the entire lifecycle management of KubeSphere extensions. It covers extension discovery, dependency resolution, version verification, and executing controlled installations, upgrades, or uninstalls using the InstallPlan mechanism. It is essential for maintaining a stable and complex multi-component Kubernetes cluster environment.

A comprehensive guide for configuring ArgoCD within KubeSphere DevOps. It details implementing GitOps continuous deployment, covering application management, multi-cluster setup, SSO integration via Dex, and best practices for managing application state synchronization from Git repositories. Learn the differences between direct ArgoCD and KubeSphere's tenant-friendly GitOps application creation.

Automate the deployment of CAST AI across various multi-cloud Kubernetes clusters, including EKS, GKE, and AKS. This module uses Terraform Infrastructure as Code (IaC) patterns to reliably configure necessary components like IAM roles, node autoscaling policies, and cluster resources, ensuring standardized onboarding across different cloud environments.

Automate the entire deployment lifecycle for ML inference services on CoreWeave GPU cloud. This workflow integrates unit testing, container image building (to GHCR), and rolling deployment to CoreWeave Kubernetes clusters via GitHub Actions. Ideal for ensuring stable, high-performance deployment of GPU-dependent models. Key features include validating GPU manifest requests and managing deployments using standard Kubernetes APIs.

A comprehensive guide to diagnose and resolve common CoreWeave infrastructure errors. It covers issues related to GPU scheduling, Pod status (e.g., Pending), CUDA Out of Memory errors, NCCL timeouts, and Kubernetes resource allocation within AI/ML deployment pipelines.

A set of production-ready wrappers and patterns for interacting with the CAST AI REST API. This package provides robust client implementations in TypeScript and Python, handling critical features such as typed responses, singleton management, automatic retry logic with backoff, and managing cloud resources (e.g., Kubernetes clusters, node optimization). Use it when building reusable, reliable clients for cost optimization and infrastructure management.

This workflow guides users through configuring CAST AI's autoscaler policies to achieve optimal cost management and resource utilization within a Kubernetes cluster. It covers enabling spot instances, setting node downscaler/evictor rules, defining cluster limits, and creating specialized node templates (e.g., for GPU or spot workers) using Terraform. Use this when implementing Phase 2 automation or optimizing cloud spend.

A comprehensive guide and set of scripts to maximize cost savings in Kubernetes environments using CAST AI. Learn to implement aggressive spot instance strategies, perform workload right-sizing, utilize cluster hibernation for non-production environments, and analyze monthly savings reports to significantly reduce cloud compute expenditure.

This guide details how to configure webhook notifications and utilize audit logs from CAST AI. It allows users to set up real-time alerts for critical cluster events like node scaling, cost threshold breaches, and security findings. By integrating with Slack, PagerDuty, and custom HTTP endpoints, users can achieve comprehensive monitoring and automated response for their infrastructure.