「drive」の検索結果

Plan and execute reference-driven movement using first/last frames, driving video, and model-specific motion controls.

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。

Generate a project-local verification skill that drives your app the way a user does — any language, framework, or platform. Use for /create-verification-skill, "make a control skill for this repo", or when a project has no scripted way to prove UI/CLI/service behavior.

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-driven Flutter E2E testing via VM Service CLI. Control running Flutter apps in debug mode for smoke tests, regression checks, exploratory testing, and UI automation. Trigger when user mentions E2E testing, UI automation, smoke tests, integration testing, test automation, controlling Flutter apps, VM Service interaction, debugging UI flows, automated regression testing, CI sanity checks, or testing native dialogs/permissions/WebViews. Supports stateful named instances or stateless direct URI connections. Ideal for rapid test feedback, AI agent workflows, element discovery, screenshot capture, log collection, and scriptable UI interactions (tap, scroll, enterText). Use for troubleshooting flaky tests, automating repetitive manual tests, verifying app behavior after merges, or setting up automated QA 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。

Implements BLoC (v9.x) event-driven state management for Flutter with unidirectional data flow using the flutter_bloc package. Use when implementing Bloc/Cubit classes, mapping Events to States, consuming state with BlocBuilder/BlocListener/BlocConsumer, applying event transformers (restartable/droppable/sequential) for concurrency control, setting up BlocProvider/MultiBlocProvider DI, integrating Freezed for sealed union types and exhaustive pattern matching, or testing with blocTest. Ideal for enterprise apps requiring business logic separation, precise concurrency control (search debouncing, form deduplication), or event replay debugging.

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。

Implements responsive and adaptive layouts in Flutter using flutter_screenutil v5.9.x (pixel-perfect scaling from Figma), sizer v3.1.x (percentage-based viewport), and responsive_framework v1.1.x (breakpoint-driven structure changes). Use this skill when building apps for multiple screen sizes (phones, tablets, foldables, desktop, web), fixing layout overflow or sizing issues, implementing breakpoint-based adaptive UI, creating pixel-perfect designs from Figma specs, handling orientation changes, supporting Flutter web or desktop responsive layouts, or any time the words 'responsive', 'screenutil', 'sizer', 'breakpoint', 'adaptive layout', or 'screen size' appear in the request. Includes a decision matrix for selecting the correct package per scenario.

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。

FlutterSecureStorage (v10.x) for encrypted key-value storage of sensitive data using platform-native secure enclaves with biometric authentication support. Use this skill when storing OAuth tokens (access tokens, refresh tokens, ID tokens), API keys and secrets, user passwords or PINs, session tokens, encryption keys, private keys for cryptography, certificate data, biometric enrollment data, banking credentials, health data, or any sensitive information requiring platform-level encryption (iOS Keychain with Secure Enclave, Android Keystore with Hardware-backed keys). Supports Touch ID, Face ID, and Android biometric prompt integration. Handles biometric change invalidation (resetOnError), data migration from SharedPreferences/EncryptedSharedPreferences, Google Drive backup exclusion, automatic key rotation, configurable encryption algorithms (AES), and prevents data extraction even on rooted/jailbroken devices. Essential for authentication flows, secure credential management, or compliance requirements (PCI DSS, HIPAA, GDPR data protection).

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。

Generative UI (GenUI) for Flutter using AI models like Google Gemini, Anthropic Claude, or OpenAI via the genui package. Use this skill when building dynamic AI-driven interfaces, conversational UI flows, server-rendered screen layouts, LLM-powered dashboards, chat-based UI generation, or implementing prompt-to-UI systems. Supports AI-generated widgets, reactive data binding, ContentGenerator integration, and A2uiMessageProcessor for real-time UI updates from AI responses. Ideal for apps where UI structure is determined by AI/LLM output rather than hardcoded layouts.

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。

Declarative animation patterns using flutter_animate v4.5.x with composable effects (fade, scale, slide, shimmer, shake, blur), GLSL fragment shaders, and scroll-synchronized animations. Use this skill for adding UI animations and transitions, building staggered or sequenced effects, creating custom animation effects, fixing animation jank or performance issues, implementing shimmer loading states, or adding scroll-driven animations. Supports delays, curves, adaptive effects, and easy extension method chaining (.animate()).

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。

Evidence-driven application audit protocol for security, authorization, privacy, concurrency, state integrity, reliability, performance, accessibility, visual consistency, responsive behavior, and release readiness. Use for incremental checks, pre-merge review, pre-launch audits, high-stakes deep audits, AI-generated code review, hardening, or release-gate design. Prefer this protocol over ad-hoc review when the user asks whether software is safe or ready to ship.

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。

Send a box of Krispy Kreme donuts (doughnuts) to someone by mail, paid with your own credit card via Stripe Link — driven entirely from the terminal. Use when the user wants to send donuts, doughnuts, treats, or a thank-you gift to a lead, prospect, customer, colleague, or friend — e.g. "send donuts to Jane at Acme", "mail a dozen glazed to our new customer", "send a thank-you gift to the team". Orders are placed and shipped through the Zinc API; payment is a Stripe Link Shared Payment Token via the create-payment-credential (link-cli) skill.

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。

Send a box of Krispy Kreme donuts (doughnuts) to someone by mail, paid with your own credit card via Stripe Link — driven entirely from the terminal. Use when the user wants to send donuts, doughnuts, treats, or a thank-you gift to a lead, prospect, customer, colleague, or friend — e.g. "send donuts to Jane at Acme", "mail a dozen glazed to our new customer", "send a thank-you gift to the team". Orders are placed and shipped through the Zinc API; payment is a Stripe Link Shared Payment Token via the create-payment-credential (link-cli) skill.

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。

サイレント失敗を検出し、堅牢なエラーハンドリングを確保する。PRレビューでのエラーハンドリング検査時に使用。

Enforces project coding standards during code review: naming conventions (PascalCase for classes, camelCase for methods/variables, UPPER_SNAKE_CASE for constants, kebab-case for files), standard file structure, anti-magic-number policy, file size limits (500/800 lines), security patterns, and design patterns. Use when reviewing code, writing new code, or checking adherence to project conventions.

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。

テスト品質を動作カバレッジの観点から分析し、クリティカルなテストギャップを特定する。テストファイルの追加・変更時に使用。

Guides test implementation following the test pyramid (75% unit, 20% integration, 5% E2E), Arrange-Act-Assert pattern, and project coverage thresholds (branches 70%, functions 80%, lines 80%, statements 80%). Covers test naming conventions, test independence, mock and stub patterns, test data management with fixtures and builders, and CI integration. Use when writing tests, reviewing test code, or setting up test infrastructure.

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。

コードコメントの正確性、完全性、長期的な保守性を分析する。JSDoc、コメント、README等のドキュメントが変更された場合に使用。

機能を保持したまま不要な複雑性を排除し、コードの簡潔性と可読性を向上させる。30行超の関数や3段以上のネスト検出時に使用。

型設計の品質と不変性を4軸スコアで分析する。interface, type, class の追加・変更時に使用。

Creates and reviews agent skills safely by enforcing deletion-prevention harness rules: no fixed absolute temp paths, no cwd-dependent cleanup, no wildcard deletes, no workspace-external file operations, no silent cleanup failures, and mandatory temp-root validation before any destructive command. Use when creating a new skill, updating an existing SKILL.md, or reviewing prompts/runbooks that include temp files, extraction, cleanup, PowerShell, bash, rm, del, Remove-Item, or archive expansion.

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。

プロジェクトガイドラインへの準拠をチェックし、バグ、スタイル違反、コード品質問題を検出する。PRレビュー時に使用。

Enforces error handling standards: silent error prohibition, custom error class hierarchy (AppError base with category never-fallback / transient / permanent, cause preservation, ValidationError / UnauthorizedError / UpstreamError etc. — canonical definitions in PATTERNS.md), external-boundary normalization (normalizeExternalError), Result pattern (Result.ok/Result.fail), proper try-catch with error type checking, structured error logging with a single Logger contract, fallback prohibition categories (isNeverFallback), and HTTP status code mapping. Use when implementing error handling, reviewing catch blocks, or designing error responses.

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。

Test-driven development. Use when the user wants to build features or fix bugs test-first, mentions "red-green-refactor", or wants integration tests.

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。

Master Bash Automated Testing System (Bats) for comprehensive shell script testing. Use when writing tests for shell scripts, CI/CD pipelines, or requiring test-driven development of shell utilities.

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。

Implement comprehensive testing strategies with pytest, fixtures, mocking, and test-driven development. Use when writing Python tests, setting up test suites, or implementing testing best practices.

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。