850 件 ・ 関連度順
概要と使いどころ
Orchestrate iOS screenshot automation with xcodebuild/simctl for build-run, AXe for UI actions, JSON settings and plan files, Koubou-based framing (`asc screenshots frame`), and screenshot upload (`asc screenshots upload`). Use when users ask for automated screenshot capture, AXe-driven simulator flows, frame composition, or screenshot-to-upload pipelines.
日本語の概要は準備中です。原文の説明を表示しています。
Build 2D games and animations using SpriteKit. Use when creating game scenes with SKScene and SKView, adding sprites with SKSpriteNode, animating with SKAction sequences, simulating physics with SKPhysicsBody and contact detection, creating particle effects with SKEmitterNode, building tile maps, using SKCameraNode, or integrating SpriteKit scenes in SwiftUI with SpriteView.
日本語の概要は準備中です。原文の説明を表示しています。
Build 3D scenes and visualizations using SceneKit. Use when creating 3D views with SCNView and SCNScene, building node hierarchies with SCNNode, applying materials and lighting, animating with SCNAction, simulating physics with SCNPhysicsBody, loading 3D models (.usdz, .scn), adding particle effects, or embedding SceneKit in SwiftUI with SceneView. Note: SceneKit was deprecated at WWDC 2025 and is in maintenance mode; RealityKit is recommended for new projects.
日本語の概要は準備中です。原文の説明を表示しています。
Build CarPlay-enabled apps using the CarPlay framework. Use when creating navigation, audio, communication, EV charging, parking, or food ordering apps for the car display, working with CPTemplateApplicationScene, CPListTemplate, CPMapTemplate, CPNowPlayingTemplate, configuring CarPlay entitlements, or integrating with CarPlay Simulator for testing.
日本語の概要は準備中です。原文の説明を表示しています。
Schedule and execute background work on iOS using BGTaskScheduler. Use when registering BGAppRefreshTask for short background fetches, BGProcessingTask for long-running maintenance, BGContinuedProcessingTask (iOS 26+) for foreground-started work that continues in background, background URLSession downloads, or background push notifications. Covers Info.plist configuration, expiration handling, task completion, and debugging with simulated launches.
日本語の概要は準備中です。原文の説明を表示しています。
Capture and interpret iOS Simulator ETTrace profiles. Use when profiling launch or runtime latency, comparing traces, or finding CPU-heavy stacks.
日本語の概要は準備中です。原文の説明を表示しています。
Agente que simula Bill Gates — cofundador da Microsoft, arquiteto da industria de software comercial, estrategista tecnologico global, investidor sistemico e filantropo baseado em dados.
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You are an API mocking expert specializing in realistic mock services for development, testing, and demos. Design mocks that simulate real API behavior and enable parallel development.
日本語の概要は準備中です。原文の説明を表示しています。
Agente que simula Andrej Karpathy — ex-Director of AI da Tesla, co-fundador da OpenAI, fundador da Eureka Labs, e o maior educador de deep learning do mundo.
日本語の概要は準備中です。原文の説明を表示しています。
Kubernetes penetration testing systematically evaluates cluster security by simulating attacker techniques against the API server, kubelet, etcd, pods, RBAC, network policies, and secrets. Using tools
日本語の概要は準備中です。原文の説明を表示しています。
Performing authorized AWS penetration testing using Pacu, the open-source AWS exploitation framework, to enumerate IAM configurations, discover privilege escalation paths, test credential harvesting, and validate security controls through systematic attack simulation.
日本語の概要は準備中です。原文の説明を表示しています。
Execute an internal network penetration test simulating an insider threat or post-breach attacker to identify lateral movement paths, privilege escalation vectors, and sensitive data exposure within the corporate network.
日本語の概要は準備中です。原文の説明を表示しています。
Pass-the-Ticket (PtT) is a lateral movement technique that uses stolen Kerberos tickets (TGT or TGS) to authenticate to services without knowing the user's password. By extracting Kerberos tickets fro
日本語の概要は準備中です。原文の説明を表示しています。
Build and configure a resilient command-and-control infrastructure using BishopFox's Sliver C2 framework with redirectors, HTTPS listeners, and multi-operator support for authorized red team engagements.
日本語の概要は準備中です。原文の説明を表示しています。
High-performance numerical and quantum compute skill for scientific workloads; use when working on numeric algorithms, simulation, or acceleration.
日本語の概要は準備中です。原文の説明を表示しています。
Author useful, low-noise log alerts on services in a PostHog project. Use when the user asks to set up alerts for their logs, suggest alerts they should add, or evaluate whether a service is worth monitoring. Covers service triage, baseline characterisation, threshold drafting, back-testing via simulate, and shipping with a notification destination.
日本語の概要は準備中です。原文の説明を表示しています。
Monte Carlo simulation for statistical mechanics — Ising model, Metropolis-Hastings, Wolff cluster algorithm, observables (magnetization, susceptibility, specific heat), finite-size scaling, and critical phenomena analysis.
日本語の概要は準備中です。原文の説明を表示しています。
IBM quantum computing framework. Use when targeting IBM Quantum hardware, working with Qiskit Runtime for production workloads, or needing IBM optimization tools. Best for IBM hardware execution, quantum error mitigation, and enterprise quantum computing. For Google hardware use cirq; for gradient-based quantum ML use pennylane; for open quantum system simulations use qutip.
日本語の概要は準備中です。原文の説明を表示しています。
Google quantum computing framework. Use when targeting Google Quantum AI hardware, designing noise-aware circuits, or running quantum characterization experiments. Best for Google hardware, noise modeling, and low-level circuit design. For IBM hardware use qiskit; for quantum ML with autodiff use pennylane; for physics simulations use qutip.
日本語の概要は準備中です。原文の説明を表示しています。
Solve partial differential equations — finite differences, spectral methods, and physics-informed neural networks (PINNs via DeepXDE). Supports 1D/2D/3D, steady/transient, linear/nonlinear PDEs with Dirichlet, Neumann, and periodic boundary conditions.
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Nonlinear curve fitting for physics data with proper error propagation, chi-squared analysis, residual diagnostics, confidence intervals, and model comparison (AIC/BIC). Use for any parameter extraction from experimental or simulation data.
日本語の概要は準備中です。原文の説明を表示しています。
Solve ordinary differential equations (initial and boundary value problems). Supports stiff/non-stiff systems, event detection, Hamiltonian/symplectic integration, parameter sweeps, and phase space analysis. Use for any ODE system in physics, engineering, or applied math.
日本語の概要は準備中です。原文の説明を表示しています。
Patterns for loading PDE simulation datasets (PDEBench, PhiFlow, JAX-CFD) from HDF5 files. Handles layout detection (single tensor vs separate variables), spatial/temporal downsampling, multi-variable systems, HuggingFace and DaRUS data sources, and efficient PyTorch DataLoader creation. Use when preparing PDE data for neural operator training.
日本語の概要は準備中です。原文の説明を表示しています。
End-to-end gene regulatory network inference pipeline from processed single-cell data to regulon discovery and perturbation simulation. Supports RNA-only (pySCENIC) and multiome (SCENIC+) paths. Use when building gene regulatory networks from single-cell transcriptomic or multiome data.
日本語の概要は準備中です。原文の説明を表示しています。