Skill UI

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 guide details the operational workflow for namespace-scoped tenants within KubeSphere DevOps. It outlines how users without cluster-admin privileges can securely perform DevOps operations by strictly utilizing KubeSphere's dedicated APIs (`/kapis/`) and authenticating via OAuth tokens, ensuring strict resource isolation and access control within their designated namespace.

KubeSphere Core acts as the central operating system for cloud-native application management built on Kubernetes. It provides essential functions, including API aggregation, multi-cluster routing, and multi-tenant separation. It manages core resources and simplifies complex cloud-native deployment and operation tasks.

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.

Aeon is a comprehensive, scikit-learn compatible Python toolkit designed for advanced time series analysis. It supports a wide range of temporal machine learning tasks, including classification, regression, clustering, forecasting, anomaly detection, and similarity search. Use this skill when working with sequential patterns, time-indexed observations, or complex temporal data that requires specialized algorithms.

Dask is a powerful Python library designed for parallel and distributed computing. It allows users to scale existing pandas and NumPy workflows beyond the limits of available RAM, processing massive datasets up to the terabyte scale. It provides specialized APIs (DataFrames, Arrays, Bags) for handling structured, scientific, and unstructured data efficiently across single machines or multi-node clusters.

A comprehensive, scalable Python toolkit designed for end-to-end single-cell RNA-seq data analysis. It supports crucial steps including quality control (QC), normalization, dimensionality reduction (PCA, UMAP, t-SNE), cell clustering, and differential gene expression analysis. The skill provides ready-to-run scripts for analyzing various formats (e.g., .h5ad, 10X, R-native) and generating publication-quality visualizations and cell type annotations.

A comprehensive resource detailing the use of scikit-learn, the industry-standard Python library for classical machine learning. Use this skill for the entire ML lifecycle, including supervised tasks (classification, regression), unsupervised tasks (clustering, dimensionality reduction), data preprocessing, model evaluation, and building production-ready pipelines.

UMAP (Uniform Manifold Approximation and Projection) is a powerful technique for non-linear dimensionality reduction. This skill enables the creation of scalable, structure-preserving embeddings (2D/3D) for visualization, clustering preprocessing, and advanced machine learning feature engineering. It supports various metrics (e.g., Euclidean, Cosine) and supervised learning workflows.

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.

A comprehensive guide to optimizing CAST AI's core performance metrics, including node provisioning speed, autoscaler responsiveness, and API call efficiency. This skill covers techniques like configuring headroom, selecting optimal instance families, tuning evictors, and implementing caching for multi-cluster dashboards to ensure high availability and cost efficiency in cloud environments.