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概要と使いどころ
Design, audit, and strengthen experimental validation for IEEE manuscripts using routed claim-evidence checks. Use when planning experiments, selecting baselines, designing ablations, choosing metrics, checking fairness, adding robustness tests, explaining results, analyzing complexity, building claim-to-evidence matrices, or responding to reviewer concerns about insufficient experiments.
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Provides AWS CloudFormation patterns for ElastiCache Redis or Memcached infrastructure, including subnet groups, parameter groups, security controls, and cross-stack outputs. Use when designing cache tiers, high-availability replication groups, encryption settings, or reusable CloudFormation templates for application caching.
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。
Provides comprehensive NestJS best practices including modular architecture, dependency injection scoping, exception filters, DTO validation with class-validator, and Drizzle ORM integration. Use when designing NestJS modules, implementing providers, creating exception filters, validating DTOs, or integrating Drizzle ORM within NestJS 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。
Provides implementation patterns for Clean Architecture, Domain-Driven Design (DDD), and Hexagonal Architecture (Ports & Adapters) in NestJS/TypeScript applications. Use when structuring complex backend systems, designing domain layers with entities/value objects/aggregates, implementing ports and adapters, creating use cases, or refactoring from anemic models to rich domain models with dependency inversion.
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。
Provides implementation patterns for Clean Architecture, Hexagonal Architecture (Ports & Adapters), and Domain-Driven Design in Python applications with FastAPI or Flask. Use when designing maintainable backends with separation of concerns, implementing repository patterns, creating entities/value objects/aggregates, or structuring domain logic independent of frameworks for testability.
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。
Creates reusable Zod v4 schemas, validates API payloads, forms, and configuration input, transforms and coerces data safely, and handles validation errors with strong type inference for TypeScript applications. Use when designing validation layers, parsing `z.string()`, `z.object()`, or `z.email()` schemas, or implementing runtime type-safe data validation.
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。
Provides AWS Key Management Service (KMS) patterns using AWS SDK for Java 2.x. Use when creating/managing encryption keys, encrypting/decrypting data, generating data keys, digital signing, key rotation, or integrating encryption into Spring Boot 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。
Use after framing a question and before designing an analysis, or when choosing a method, judging whether a result is novel, or needing a prior effect size for a power calculation
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 have an approved research question and need a concrete analysis plan, before touching outcome data or fitting any model
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。
When the user needs to review an existing contract, assess risk in proposed terms, or evaluate a contract before signing.
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 roster fit and personality dynamics. Leadership assessment, role clarity, locker room culture, trade/signing impact.
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。
Maps the full customer journey from first touch to advocacy. Generates a comprehensive customer-journey.md with all stages, touchpoints, emotions, pain points, opportunities, Mermaid diagrams, and metrics. Use when mapping customer experience, designing onboarding flows, identifying churn risks, or optimizing conversion funnels.
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。
Decode a commercial lease before signing or renewing -- true all-in occupancy cost (base rent plus NNN/CAM plus escalations), the clauses that hurt small tenants (personal guarantees, relocation, restoration), and the negotiation asks landlords routinely grant. The lease is most SMBs' biggest fixed cost and least-read document.
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。
Scaffold, build, and package SwiftPM-based macOS apps without an Xcode project. Use when you need a from-scratch macOS app layout, SwiftPM targets/resources, a custom .app bundle assembly script, or signing/notarization/appcast steps outside Xcode.
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。
Best practices and example-driven guidance for building SwiftUI views and components, including navigation hierarchies, custom view modifiers, and responsive layouts with stacks and grids. Use when creating or refactoring SwiftUI UI, designing tab architecture with TabView, composing screens with VStack/HStack, managing @State or @Binding, building declarative iOS interfaces, or needing component-specific patterns and examples.
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。
Classifies and enforces constraints via soft vows, hard vows, and Nen Court layers. Use when designing or auditing enforcement mechanisms for project rules.
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。
Provides weighted scoring, rubrics, and decision-threshold patterns. Use when designing quality gates, evaluation systems, or decision 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。
Applies event-driven async messaging to decouple producers and consumers. Use when designing real-time or multi-subscriber systems needing loose coupling.
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。
Applies hexagonal architecture isolating domain from infrastructure. Use when designing systems where testability and port/adapter separation are priorities.
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。
Applies layered n-tier architecture with enforced boundaries. Use when designing moderate systems needing clear presentation, domain, and persistence layers.
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。
Applies CQRS and Event Sourcing for read/write separation and audit trails. Use when designing systems with complex domain logic or full state-change history.
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。
Applies serverless FaaS patterns for event-driven workloads. Use when designing bursty workloads with minimal infrastructure and pay-per-execution cost model.
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。
Applies client-server architecture for web/mobile apps. Use when designing systems with centralized backend services, trust boundaries, or offline-first sync.
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 designing a study that benchmarks AI systems against a human-expert panel, before data collection. Plans the arms, decoupled rubrics with anchors, planted calibration probes, reviewer panel, inter-rater reliability targets and LLM-as-judge versus human adjudication.
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。