「state」の検索結果

Creates Vue 3 components, builds vanilla JS composables, configures Vite projects, and sets up routing and state management using JavaScript only — no TypeScript. Generates JSDoc-typed code with @typedef, @param, and @returns annotations for full type coverage without a TS compiler. Use when building Vue 3 applications with JavaScript only (no TypeScript), when projects require JSDoc-based type hints, when migrating from Vue 2 Options API to Composition API in JS, or when teams prefer vanilla JavaScript, .mjs modules, or need quick prototypes without TypeScript setup.

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。

Use when building cross-platform applications with Flutter 3+ and Dart. Invoke for widget development, Riverpod/Bloc state management, GoRouter navigation, platform-specific implementations, performance optimization.

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。

Use when building game systems, implementing Unity/Unreal Engine features, or optimizing game performance. Invoke to implement ECS architecture, configure physics systems and colliders, set up multiplayer networking with lag compensation, optimize frame rates to 60+ FPS targets, develop shaders, or apply game design patterns such as object pooling and state machines. Trigger keywords: Unity, Unreal Engine, game development, ECS architecture, game physics, multiplayer networking, game optimization, shader programming, game AI.

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。

Generates Angular 17+ standalone components, configures advanced routing with lazy loading and guards, implements NgRx state management, applies RxJS patterns, and optimizes bundle performance. Use when building Angular 17+ applications with standalone components or signals, setting up NgRx stores, establishing RxJS reactive patterns, performance tuning, or writing Angular tests for enterprise apps.

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。

Use when building C# applications with .NET 8+, ASP.NET Core APIs, or Blazor web apps. Builds REST APIs using minimal or controller-based routing, configures database access with Entity Framework Core, implements async patterns and cancellation, structures applications with CQRS via MediatR, and scaffolds Blazor components with state management. Invoke for C#, .NET, ASP.NET Core, Blazor, Entity Framework, EF Core, Minimal API, MAUI, SignalR.

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。

Browser automation with persistent page state. Use when users ask to navigate websites, fill forms, take screenshots, extract web data, test web apps, or automate browser workflows. Trigger phrases include "go to [url]", "click on", "fill out the form", "take a screenshot", "scrape", "automate", "test the website", "log into", or any browser interaction request.

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。

Scan recent merges to main (and noteworthy direct commits) since a given window (last tag or date in state). Extract titles, labels, types, linked issues, and signals for breaking changes or security. Produces structured input for a release notes drafter. Use in changelog-drafter loops.

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。

Triage recent changes, CI failures, issues, and conversations. Produces a concise, actionable findings report suitable for a loop to consume. Writes structured output to a state file or Linear board.

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。

Scan open issues and discussions. Dedupe, prioritize, and propose labels. Updates issue-triage-state.md. L1 propose-only — never auto-label or close.

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。

Triage recent changes, CI failures, issues, and conversations. Produces a concise, actionable findings report suitable for a loop to consume. Writes structured output to a state file or issue tracker.

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。

Draft client proposals and statements of work that scope, price, and win the project.

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。

AI demos and GPU compute with Gradio Spaces and Hugging Face Spaces ZeroGPU. Use when writing or reviewing code that uses `@spaces.GPU`, configuring `python_version` or `requirements.txt` for a ZeroGPU Space, or handling ZeroGPU-specific code constraints — pickle-based process isolation, `gr.State` semantics across the worker boundary, no `torch.compile` (use AoTI instead), CUDA wheel-only builds (no `nvcc` at build or runtime), large vs xlarge sizing, and dynamic duration callables. Make sure to use this skill whenever the user mentions ZeroGPU, `@spaces.GPU`, or the `spaces` Python package, or hits ZeroGPU-specific code errors like `PicklingError` across the worker boundary, `illegal duration`, or `flash-attn` wheel-build failures — even when the user does not explicitly ask for ZeroGPU coding guidance. Trigger on `import spaces` or `@spaces.GPU` in code.

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。

Use when the user asks about finding the best, top, or recommended model for a task, wants to know what AI model to use, or wants to compare models by benchmark scores. Triggers on: "best model for X", "what model should I use for", "top models for [task]", "which model runs on my laptop/machine/device", "recommend a model for", "what LLM should I use for", "compare models for", "what's state of the art for", or any question about choosing an AI model for a specific use case. Always use this skill when the user wants model recommendations or comparisons, even if they don't explicitly mention HuggingFace or benchmarks.

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。

Use Transformers.js to run state-of-the-art machine learning models directly in JavaScript/TypeScript. Supports NLP (text classification, translation, summarization), computer vision (image classification, object detection), audio (speech recognition, audio classification), and multimodal tasks. Works in browsers and server-side runtimes (Node.js, Bun, Deno) with WebGPU/WASM using pre-trained models from Hugging Face Hub.

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。

Use when you need state across calls — building env vars, navigating with cd, driving REPLs (python -i, mysql, psql, node), or responding to interactive prompts (sudo password, ssh host-key confirmation, mysql connection). Teaches the prompt-sentinel exec pattern (default mode), raw I/O for REPLs (raw_send=True then read_only=True), the one-in-flight-per-session rule, and the close-or-leak-against-the-cap discipline. Bash on macOS — never zsh; explicit shell=/bin/zsh is rejected. Read before calling terminal_pty_open.

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。

Emit the complete Resolve handoff with exact modes, outcomes, test evidence, and publication state.

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 a quick task with GSD guarantees (atomic commits, state tracking) but skip optional agents

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。

Smart entry — detect project state and route to the right next GSD action.

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。

Systematic debugging with persistent state across context resets

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。

Shallow (quick) ensemble bug-finding review using 6 specialist agents in parallel. Each specialist reviews the same patch through a different lens: Logic & Types, Boundaries & I/O, Concurrency & State, Resources & Serialization, Absence Analysis, and API Completeness. Findings are merged and deduplicated. Best for: quick first-pass review of patches, triage of diffs, broad surface-level bug scan. For deeper file-focused review with full pattern catalogs and codebase investigation, use the deep-review skill instead.

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。

Create, run, and verify TLA+ and PlusCal formal specifications. Use for modeling distributed systems, protocols, concurrent algorithms, state machines. Can compose specs from code, find divergences between spec and implementation, spot concurrency bugs and invariant violations. Use when asked to "write a TLA+ spec", "model check", "verify protocol", "find race conditions", or "formal verification".

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。

Deep code analysis with ASCII visualizations showing structure, flow, and state transitions. Use when analyzing patches/diffs, explaining classes or subsystems, understanding code architecture, reviewing changes for inconsistencies, or when asked to visualize how code works. Provides before/after diagrams, data/control flow, concurrency analysis, assumptions, and failure modes. Triggers on explain this patch/code/class, how does X work, show me the flow, visualize this change, code review requests, or proactive analysis during PR reviews.

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。

Deep file-focused code review for correctness bugs. Unlike shallow-review which runs 6 specialists in parallel across the entire patch, deep-review focuses on user-specified files with full pattern catalogs (500+ patterns), codebase investigation, and source-level context gathering. Use when: the user specifies particular files for focused review, a shallow review flagged areas that need deeper investigation, reviewing critical-path code changes, examining complex serialization/lifecycle/state-machine changes. The user instructs which files to focus on (typically a subset of files in the patch).

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。

Diff a new or changed regulation against current privacy policy and practice — outputs a gap list and a remediation plan with owners and dates. Use when a new reg drops, the user asks "does [regulation] affect us", "gap analysis for [state privacy law]", "compliance check against [reg]", or pastes regulatory text.

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。