Skill UI

A comprehensive, generics-first utility library designed for Go developers who prefer declarative, functional programming patterns over manual loops. It provides 500+ type-safe functions for common data transformations (Map, Filter, Reduce, GroupBy, etc.) on slices, maps, and strings. By defaulting to immutable operations, it ensures thread safety and predictable state changes. The library also offers specialized packages (lop, lom, loi) to address performance bottlenecks, such as CPU-bound parallel processing or lazy evaluation, allowing engineers to choose the optimal approach for their c...

Detects host inventory including CPU, memory, disk, and accelerator limits for resource-aware planning. Generates a redacted JSON snapshot without stress tests. Supports Python 3.11+ on Linux, macOS, and Windows. Includes workload planning helpers and conservative capacity estimation.

Version-aware guidance for PufferLib reinforcement-learning environments, vectorization, policies, PuffeRL training, evaluation, and safe checkpoint review. Adapt Gymnasium/PettingZoo environments to published PufferLib 3.0.0 or work with redesigned native 4.0 source line with Python 3.10+ and Torch 2.9+. Includes safe defaults for CPU-only, network-free tools with strict JSON output and dependency-free bundled CLIs.

This skill guides the setup and configuration of the CAST AI Workload Autoscaler, enabling dynamic right-sizing of pod resource requests (CPU and Memory). It automatically adjusts resources based on actual usage, significantly reducing cloud over-provisioning costs. Use this guide to implement workload scaling policies, configure resource limits via Kubernetes annotations, and monitor scaling events in Kubernetes environments.

Collects comprehensive diagnostics from an unresponsive Oracle Cloud Infrastructure (OCI) compute instance. This tool gathers critical data—including serial console logs, cloud-init logs, VCN flow logs, and CPU metrics—into a single, self-contained tar archive. Use this when an instance is failing, stuck in provisioning, or showing underlying infrastructure errors, providing support teams with all necessary information for root-cause analysis.

Programmatically query Oracle Cloud Infrastructure (OCI) metrics using the Monitoring Query Language (MQL) and create monitoring alarms via the Python SDK. This skill bypasses the limitations of the OCI console query builder, allowing reliable retrieval of key infrastructure metrics such as CPU utilization, memory usage, network traffic, and disk I/O. Ideal for building robust dashboards, automating resource monitoring, and setting up complex alerting rules.

This guide provides comprehensive guidelines for performing vertical scaling on Qdrant instances. Vertical scaling involves upgrading the computational resources (CPU, RAM, or disk) of existing nodes rather than adding new nodes. It is the recommended first step when single nodes experience resource exhaustion (e.g., RAM usage > 80%, CPU saturation). The guide covers sizing guidelines, best practices for cloud deployment, and clearly defines the thresholds when horizontal scaling becomes necessary.

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.

This guide provides essential knowledge on resource limitations within WebContainer environments (such as memory, CPU, file system size, and process count). It is crucial for developers utilizing the WebContainers API or StackBlitz SDK to understand these constraints. Learn best practices for monitoring memory usage, optimizing mounted file systems by excluding unnecessary assets (like test or documentation folders), and managing dependencies to prevent common issues like Out-Of-Memory (OOM) crashes and slow build times.

A state-of-the-art solution for real-time object detection using the advanced YOLO 2026 model. This skill detects 80+ COCO object classes (people, vehicles, animals) and outputs bounding boxes with high precision. It features automatic hardware acceleration, optimizing the model for various platforms (CUDA, MPS, OpenVINO, ROCm) to ensure maximum speed and efficiency, whether running on CPU, GPU, or Neural Engine.

This skill provides high-performance, real-time object detection using the Intel OpenVINO runtime. It operates via a Docker container, ensuring cross-platform compatibility. Supports detection across various Intel accelerators (NCS2, iGPU, Arc GPU) and CPUs. Users can customize parameters like confidence threshold, target classes, processing FPS, and model precision to optimize performance for specific hardware and accuracy needs.

This skill generates a comprehensive capacity plan document for any service. It assists technical teams in proactively managing infrastructure by modeling traffic growth, forecasting resource utilization (CPU, memory, connections), projecting costs, and defining clear scaling strategies. Use this when assessing current resource headroom, planning for seasonal peaks, or defining a long-term infrastructure action roadmap.