Skill UI

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 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.

A comprehensive framework for planning, configuring, executing, and analyzing complex Computational Fluid Dynamics (CFD) simulations using pseudospectral methods. It enforces a rigorous, multi-stage workflow, guiding users from defining state equations and boundary conditions to generating resource estimates, running dry-runs, and preparing site-specific MPI jobs. Designed for high-fidelity scientific computing and reproducibility.

This tool provides a conservative, resource-aware snapshot of the current process's environment, including CPU, memory, disk, and accelerator limits. It helps users plan workloads accurately by providing clear boundary constraints (cgroup, scheduler, host limits) without performing stressful tests or making assumptions about usability.

PyLabRobot provides a comprehensive, software-only framework for developing and reviewing complex laboratory automation protocols, including liquid handling and resource management. It simulates offline device interactions and resource tracking, ensuring rigorous planning without physical hardware connection. It mandates explicit human verification before any live execution, prioritizing safety and reproducibility.

PyTDC is a specialized Python package designed to interact with the Therapeutics Data Commons. It facilitates the discovery of machine learning tasks, loading approved datasets, generating task-appropriate data splits, and evaluating predictions for drug discovery research. The tool strongly emphasizes performing metadata-only discovery and planning before initiating resource-intensive data downloads or network operations.

A comprehensive guide for optimizing query latency in Qdrant. This resource explains advanced performance tuning techniques, focusing on critical resource management like RAM utilization, segment count adjustments, and HNSW parameter tuning. It details strategies for scaling, quantization, and hardware considerations (e.g., local NVMe) to ensure consistently low-latency search performance, and outlines common pitfalls to avoid.

A comprehensive guide for frontend engineers specializing in shadcn/ui. Learn best practices for component discovery, installation, and deep customization using Tailwind CSS and Radix UI. This resource covers project setup, component architecture, and advanced styling techniques for building robust, owned, and highly customizable applications.

Utilizes Google's TimesFM foundation model for zero-shot time series forecasting. It processes any univariate time series (e.g., sales, sensor data, weather) without requiring custom model training. The skill provides probabilistic forecasts with calibrated prediction intervals (quantiles) and includes a mandatory system checker to ensure sufficient RAM, GPU, and disk resources for safe local inference.

A comprehensive guide for migrating code from deprecated libraries like alchemy-web3 to the modern alchemy-sdk (v3). This resource details handling breaking changes, understanding new API namespaces (core, nft, ws), and updating web3 interactions to ensure seamless blockchain functionality.