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概要と使いどころ
Detects long-lived objects (caches, listeners, singletons) that capture an entire enclosing scope via closures/environment capture, keeping large arrays or buffers alive, and proposes copying only the needed fields.
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 an Estuary derivation that maintains a sliding time window of events (e.g. "transfers in the last 24h", "failed logins in the last 5 minutes") using internal SQLite state and readDelay for time-based expiration. Derivations add complexity and cost — confirm the user wants a derivation before reaching for this. Use when user says "windowing derivation", "derivation for last-24h events", "rolling-window derivation", "sliding-window transformation", "fraud-detection derivation", "rate-limit derivation", or "time-window derivation".
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。
Diagnoses and prevents mobile performance regressions on every stack — Flutter, native Android, native iOS, React Native and Kotlin Multiplatform. Covers startup time, rendering and jank, memory, battery and background work, app size, the profiler and benchmark to use on each platform, and regression detection in CI. Use when an app is slow, janky, heavy or draining the battery, when setting performance budgets, or when a release must not regress.
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。
CloudFormation template optimization, nested stacks, drift detection, and production-ready patterns. Use when writing or reviewing CF 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。
Build image analysis applications with Azure AI Vision SDK for Java. Use when implementing image captioning, OCR text extraction, object detection, tagging, or smart cropping.
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。
Azure AI Vision Image Analysis SDK for captions, tags, objects, OCR, people detection, and smart cropping. Use for computer vision and image understanding tasks.
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。
Azure AI Text Analytics SDK for sentiment analysis, entity recognition, key phrases, language detection, PII, and healthcare NLP. Use for natural language processing on 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。
Azure AI Text Translation SDK for real-time text translation, transliteration, language detection, and dictionary lookup. Use for translating text content in applications.
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 content moderation applications with Azure AI Content Safety SDK for Java. Use when implementing text/image analysis, blocklist management, or harm detection for hate, violence, sexual conten...
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。
Azure AI Content Safety SDK for Python. Use for detecting harmful content in text and images with multi-severity classification.
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。
Analyze text and images for harmful content using Azure AI Content Safety (@azure-rest/ai-content-safety). Use when moderating user-generated content, detecting hate speech, violence, sexual conten...
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。
Assess pipeline layer state, detect prerequisite blockers, classify audit status, and persist remediation plan
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 methodology for verifying that IA, BE, and FE specification layers are mutually consistent. Covers coverage matrices, field mapping checks, error code propagation, access control consistency, and data contract drift detection.
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。
Validate implementation prerequisites, load slice/spec context, detect execution mode, and write contract schemas before TDD execution
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。
Senior Kotlin specialist code review — framework-agnostic. Use whenever the user asks to review, audit, critique, or get feedback on Kotlin code, a file, a class, a function, a module, a diff, a branch, a commit, or a pull request. Judges Kotlin language idioms, logic correctness, architecture and layering, concurrency (coroutines, Flow, structured concurrency, cancellation, race conditions), null safety, exception safety, testability, performance, and security. Detects force-unwraps, unchecked casts, GlobalScope, leaked mutability, broken structured concurrency, hidden side effects, weak abstractions, untestable seams, allocation hot spots, weak crypto, input-validation gaps, and PII logging. Outputs prioritized findings with file:line anchors and minimal-diff fixes. No assumptions about DI framework, UI framework, or build system.
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 Kotlin unit testing with auto-detection, coverage analysis, and AI edge cases. Use when generating tests for biz/ layer classes, analyzing test coverage gaps, or discovering edge cases.
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。
Apply SOLID principles to write flexible, maintainable, and testable code. Use when designing classes, interfaces, and module boundaries. Covers Single Responsibility, Open/Closed, Liskov Substitution, Interface Segregation, and Dependency Inversion with practical TypeScript examples and detection heuristics.
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。
Write clean, readable, and maintainable code following principles from Robert C. Martin's "Clean Code" and Object Calisthenics. Use when writing, reviewing, or refactoring code to improve naming, function design, formatting, error handling, and class structure. Includes code smell detection and refactoring guidance.
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。
Continuous self-improvement through systematic logging, pattern detection, and behavioral updates. Use when: the owner corrects you, a task fails, you discover a better approach, or you notice a recurring pattern. Store raw learnings in .learnings/, update the specific skill or workflow that caused the issue when appropriate, and avoid vague promises to do better.
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。
Runbook skill for failures: cron error, PA failure, cascade, gateway disconnect, semantic DB stale. Walks: detect → classify → diagnose → notify → log. Replaces ad-hoc failure handling. Triggers: "cron failed", "X is broken", "cascade", "incident", "gateway down", "PA failure".
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 working with Cypress Deep — cypress end-to-end testing management and analysis. Covers test execution monitoring, dashboard integration, screenshot and video analysis, test report parsing, flaky test detection, and configuration auditing. Use when investigating test failures, reviewing Cypress Cloud results, or managing Cypress test suites.
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 working with Codescene — codeScene behavioral code analysis including code health scoring, hotspot detection, change coupling analysis, and PR integration for code quality gates. Use when configuring CodeScene for codebase visualization, technical debt prioritization, and automated code review feedback.
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 working with Codeclimate — code Climate quality management. Covers maintainability analysis, test coverage tracking, issue detection, GPA scoring, and repository configuration. Use when managing Code Climate projects, reviewing maintainability ratings, tracking test coverage trends, or configuring analysis engines.
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 working with Codeowners — cODEOWNERS file management covering ownership mapping analysis, coverage gap detection, team assignment validation, orphaned path identification, and review requirement auditing. Use when auditing code ownership across repositories, identifying unowned code paths, validating team configurations, or optimizing pull request review workflows.
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。