Skill UI

This guide addresses the challenges of automating Apple Notes within Continuous Integration (CI) pipelines on macOS. Because standard CI environments restrict TCC (Transparency, Consent, and Control) permissions, direct automation via osascript usually fails. It provides patterns for using mock clients for unit testing and outlines best practices for running real integration tests on self-hosted runners with proper permissions.

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 guide details how to manage and structure credentials within KubeSphere DevOps using Kubernetes Secrets. It covers various types (SSH keys, basic auth, API tokens, kubeconfig) and emphasizes the critical resource structure, specific labels, and API endpoints required for integrating credentials into automated CI/CD pipelines (e.g., Jenkins, ArgoCD).

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 comprehensive guide assists developers migrating their Miro integrations from REST API v1 to v2. It details key breaking changes, such as 'Widget' becoming 'Item' and 'Line' becoming 'Connector,' provides updated endpoint maps, and offers step-by-step instructions for upgrading the `@mirohq/miro-api` SDK and adapting request/response payloads. Use this resource when updating your integration to handle the latest Miro platform standards and data structures.

This comprehensive checklist provides a detailed methodology for simulating the initial access phase in red team engagements and penetration testing. It covers a wide spectrum of modern attack vectors, including advanced social engineering (e.g., deepfake, BEC), sophisticated phishing (spear/smishing), exploiting exposed perimeter services (RDP/VPN), and identifying cloud misconfigurations. Use this guide when planning or executing the initial foothold stage to ensure comprehensive coverage against modern threats.

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.

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.