「perf」の検索結果

Integrates Sentry error tracking and performance monitoring into Flutter apps using `sentry_flutter`. Use when setting up crash reporting in Flutter, calling `SentryFlutter.init`, adding `SentryNavigatorObserver` to GoRouter, tracing HTTP requests with `sentry_dio`, capturing handled exceptions via `Sentry.captureException`, adding breadcrumbs, configuring session replay (`replay.onErrorSampleRate`), tuning `tracesSampleRate` or `profilesSampleRate`, controlling PII with `sendDefaultPii`, or setting user context after login and logout.

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。

Implements type-safe reactive SQL persistence in Flutter using Drift v2.32 (formerly Moor) built on SQLite with automatic code generation. Activates when defining table schemas with Drift DSL, writing type-safe join or subquery operations, handling schema migrations with MigrationStrategy, using reactive watch() streams for live UI updates, implementing ACID transactions, defining foreign key constraints or cascade deletes, enabling WAL mode for write performance, troubleshooting generated .g.dart type mismatches, migrating from sqflite or sqlite3 v2 to v3, using DAOs for query separation, or building offline-first apps requiring strong relational data integrity and compile-time SQL 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。

Hive CE (Community Edition v2.19.x) NoSQL object database for Flutter providing blazing-fast key-value and object storage with TypeAdapters. Use this skill when implementing offline-first architecture, high-performance local data caching, NoSQL document-style object stores, custom TypeAdapter serialization for complex objects, lazy loading boxes for memory efficiency, encrypted boxes (HiveAES encryption), database compaction for size optimization, storing large datasets without SQL schema overhead, implementing local-first sync patterns, or migrating from SQLite to NoSQL. Supports primitive types, custom objects, lists, and maps. Ideal for apps requiring ultra-fast read/write operations (microsecond latency), object persistence without ORM complexity, or local-first data architecture.

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。

Use when performing security audits, vulnerability assessments, penetration testing, or when asked to find security issues. Use when encountering code that handles user input, executes commands, reads files, or makes network requests.

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。

TidyFactor Design track — code-native UI design lifecycle engine (Figma alternative). Supports all 7 stages of UI design (Discovery, Foundation, Architecture, Components, Motion, Quality, Delivery) with zero per-page CSS/JS and zero build step. Pluggable CSS foundations (Native, Tailwind utility-first, daisyUI, Hybrid, shadcn, Pico, Bootstrap, Alpine) and full Arabic/RTL support (El Messiri/Tajawal, never Amiri above 24px). Trigger on commands "study", "brief", "init", "brand", "typography", "school", "tokens", "palette", "assets", "layout", "nav-footer", "components", "page", "dashboard", "motion", "states", "flow", "i18n", "perf", "audit", "clone", "retrofit", "handoff", "deploy", or requests like "build a design system", "UI design lifecycle", "prototype this website", "Figma alternative", "نموذج أولي تفاعلي", "صمّم لوحة تحكم".

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。

Builds native iOS apps in Swift — SwiftUI and UIKit screens, state and data flow, NavigationStack, lists, forms and accessibility; networking with URLSession and Codable, persistence with SwiftData, Core Data, files and the Keychain; permissions, push notifications, universal links, background tasks, widgets, App Intents and StoreKit 2 purchases; performance with Instruments and MetricKit; and release with signing, TestFlight and App Store Connect. Use when writing or changing native iOS code, adding a platform feature, preparing a build for TestFlight or the App Store, or when the user asks how to do something on iOS, even if they do not name the framework.

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。

Builds native Android apps with the Android team's guides — Jetpack Compose UI, adaptive layouts, edge-to-edge, Compose styles, migrating XML views to Compose, Navigation 3 and predictive back; Android Gradle Plugin 9 upgrades, R8 keep rule analysis, profiling with Android Studio and Perfetto, and the Android CLI; Google Play policy compliance, Play Billing upgrades and the Engage SDK; Credential Manager restore and verified email; CameraX and Media3 Cast; App Functions and the ML Kit GenAI Prompt API; Wear OS, Android TV and display glasses. Use when writing, upgrading or auditing native Android (Kotlin) code for any of these topics, even if the user does not name the library.

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。

Builds React Native and Expo apps in TypeScript — navigation with React Navigation or Expo Router, lists, forms, styling, gestures and animations with Reanimated, safe areas, accessibility and localization; networking with TanStack Query, response validation, persistence with MMKV, AsyncStorage, SQLite and secure storage; permissions, push notifications, deep links, background tasks, native modules and config plugins; performance with Hermes and the React profiler; and release with EAS Build, EAS Submit and EAS Update or bare Gradle and Xcode builds. Use when writing or changing React Native code, adding a native capability, configuring Expo, preparing a store build or an over-the-air update, or when the user asks how to do something in React Native, even if they do not name the library.

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。

Works on Kotlin Multiplatform mobile projects with shared Kotlin source sets and Android/iOS targets. Use for architecture, tests, debugging, reviews, performance, security, accessibility, dependencies and releases when `org.jetbrains.kotlin.multiplatform` is applied; inspect actual targets, `commonMain`/`commonTest`, target source sets, Compose Multiplatform and the iOS framework/host app before giving platform advice.

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 this skill whenever the user asks to inspect, audit, review, assess, or evaluate a codebase/project for maintainability, extensibility, architecture, reliability, security, testing, engineering quality, or AI-agent/prompt/tooling risks. Also use this when invoked by `/inspect`. It performs a read-only, evidence-backed, multi-agent project inspection with complete file coverage, per-dimension scoring, blunt technical findings, and explicit degraded-mode disclosure.

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。

Idea to published-ready content: brief, script, render routed through the Creator Extension content-studio skill (Hyperframes or Remotion), then a social pack. Use for "make a reel", "idea to video", "content from this idea", "script and render a clip".

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。

Diagnosis loop for hard bugs and performance regressions. Use when the user says "diagnose"/"debug this", or reports something broken/throwing/failing/slow.

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。

Install, verify, and operate mdoctor on macOS and Debian/Arch-based Linux. Use when users ask to set up mdoctor, install dependencies, test branch builds on the current machine, run first health checks, troubleshoot install/runtime issues, or perform safe cleanup/fix workflows with mdoctor commands.

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。

cProfile、メモリプロファイラー、パフォーマンスベストプラクティスを使用してPythonコードをプロファイルおよび最適化。低速なPythonコードのデバッグ、ボトルネックの最適化、アプリケーションパフォーマンスの改善時に使用。

Master Python asyncio, concurrent programming, and async/await patterns for high-performance applications. Use when building async APIs, concurrent systems, or I/O-bound applications requiring non-blocking operations.

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 systematic debugging techniques, profiling tools, and root cause analysis to efficiently track down bugs across any codebase or technology stack. Use when investigating bugs, performance issues, or unexpected 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。

Execute database migrations across ORMs and platforms with zero-downtime strategies, data transformation, and rollback procedures. Use when migrating databases, changing schemas, performing data transformations, or implementing zero-downtime deployment strategies.

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 GitLab CI/CD pipelines with multi-stage workflows, caching, and distributed runners for scalable automation. Use when implementing GitLab CI/CD, optimizing pipeline performance, or setting up automated testing and deployment.

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 advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability in production. Use when optimizing prompts, improving LLM outputs, or designing production prompt templates.

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。

Design a PostgreSQL-specific schema. Covers best-practices, data types, indexing, constraints, performance patterns, and advanced features

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 distributed tracing with Jaeger and Tempo to track requests across microservices and identify performance bottlenecks. Use when debugging microservices, analyzing request flows, or implementing observability for distributed systems.

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 evaluation strategies for LLM applications using automated metrics, human feedback, and benchmarking. Use when testing LLM performance, measuring AI application quality, or establishing evaluation frameworks.

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。