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概要と使いどころ
Implement proven backend architecture patterns including Clean Architecture, Hexagonal Architecture, and Domain-Driven Design. Use when architecting complex backend systems or refactoring existing applications for better maintainability.
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Reverse-lookup glossary that turns a vague description of a web animation or motion effect into its exact term ("the bouncy thing when a popover opens" → Pop in; "the iOS rubber-band scroll" → Rubber-banding). Use when the user asks "what's it called when…", or describes a motion effect without knowing its name and wants the right word to prompt an AI or designer with. For naming an effect, not designing or building one.
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Explicit layout reference for grouping, alignment, reading order, and progressive disclosure. Use only after Core frontend rules. Do not restyle product apps from these examples.
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Explicit polish reference for concentric radius, optical alignment, surface depth, contextual icons, and hit areas. Use only after Core frontend rules and packages/ui. Do not restyle product apps from these examples.
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Normalize supplied FF&E information and record accepted document-job inputs with exact product tags and source hashes. Use for intake or corrections; reusable library saves and schedule adoption go to their record owners.
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Adopt, read, reconcile or revise project FF&E item and schedule records with permanent identities and exact revision pins; preserve native workbook views and recover new artifacts.
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Query the ZINC22 virtual compound library (CartBlanche API, billions of make-on-demand + purchasable molecules). Look up substances by ZINC ID, resolve a SMILES to its ZINC ID (exact match), inspect purchasability/catalogs, and assemble compound sets for docking. Property (MW/logP) filtering is done locally with RDKit. For bioactivity use chembl-database-bioactivity; for approved drugs use drugbank-database-access.
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MATLAB/GNU Octave numerical computing: matrices, linear algebra, ODEs, signal processing, optimization, statistics, scientific visualization. MATLAB-syntax examples run on both. For Python use numpy/scipy; for statistical modeling use statsmodels.
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Provides structured AWS cost optimization guidance using five pillars (right-sizing, elasticity, pricing models, storage optimization, monitoring) and twelve actionable best practices with executable AWS CLI examples. Use when optimizing AWS costs, reviewing AWS spending, finding unused AWS resources, implementing FinOps practices, reducing EC2/EBS/S3 bills, configuring AWS Budgets, or performing AWS Well-Architected cost reviews.
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Provides implementation patterns for Clean Architecture, Hexagonal Architecture (Ports & Adapters), and Domain-Driven Design in PHP 8.3+ with Symfony 7.x. Use when architecting enterprise PHP applications with entities/value objects/aggregates, refactoring legacy code to modern patterns, implementing domain-driven design with Symfony, or creating testable backends with clear separation of concerns.
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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.
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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.
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Provides patterns and code examples for building Next.js 16+ applications with App Router architecture. Use when creating projects with App Router, implementing Server Components and Client Components ("use client"), creating Server Actions for forms, building Route Handlers (route.ts), configuring caching with "use cache" directive (cacheLife, cacheTag), setting up parallel routes (`@slot`) or intercepting routes, migrating to proxy.ts, or working with App Router file conventions (layout.tsx, page.tsx, loading.tsx, error.tsx).
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 comprehensive TypeScript documentation using JSDoc, TypeDoc, and multi-layered documentation patterns for different audiences. Use when creating API documentation, architectural decision records (ADRs), code examples, and framework-specific patterns for NestJS, Express, React, Angular, and Vue.
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Provides implementation patterns for Clean Architecture, Hexagonal Architecture (Ports & Adapters), and Domain-Driven Design in Java 21+ Spring Boot 3.5+ applications. Use when structuring layered architectures, separating domain logic from frameworks, implementing ports and adapters, creating entities/value objects/aggregates, or refactoring monolithic codebases for testability and maintainability.
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Provides patterns to generate comprehensive REST API documentation using SpringDoc OpenAPI 3.0 and Swagger UI in Spring Boot 3.x applications. Use when setting up API documentation, configuring Swagger UI, adding OpenAPI annotations, implementing security documentation, or enhancing REST endpoints with examples and schemas.
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Provides Amazon S3 patterns and examples using AWS SDK for Java 2.x. Use when working with S3 buckets, uploading/downloading objects, multipart uploads, presigned URLs, S3 Transfer Manager, object operations, or S3-specific configurations.
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Use when auditing a page for E-E-A-T signals. The agent reads the page and scores Experience, Expertise, Authoritativeness, and Trustworthiness — then tells you exactly what to add to each dimension.
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Use when a page ranks for a keyword but isn't in the top 3 and you want to know exactly what's missing. The agent compares the page to the top-ranking competitors and produces a specific list of entities, subtopics, and relationships to add.
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Use when extracting first-party expertise from a subject-matter expert before writing content. Produces a knowledge document of contrarian takes, specific examples, and surprising outcomes that AI can't fabricate.
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When the user needs a technical SEO audit, on-page optimization guidance, or help resolving crawlability, indexation, or performance issues that affect search rankings.
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 tools, scripts, and CLIs that an agent will call, so the interface teaches its own use instead of a wall of prose and examples. Use when building an MCP server or tool definition, writing an agent-facing script, or when an agent keeps misusing a tool it already has.
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Use when querying or calling pay-per-token foundation models, or when creating, updating, listing, or querying Unity Gateway (also called Unity AI Gateway) services, including model provider services, MCP services, system.ai services, and three-part Unity Catalog service names (catalog.schema.service). Also use for managing spend budgets and usage alerts on Unity Gateway, and for migrating from legacy AI Gateway. Also use when a workload with Model Serving endpoint permissions (for example CAN_QUERY) gets PERMISSION_DENIED querying a three-part model name, since serving-endpoint access does not grant access to a Unity Gateway service. Not for configuring legacy AI Gateway on Model Serving endpoints; use databricks-model-serving for that.
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Writes and restructures technical documentation - READMEs, tutorials, how-to guides, user guides, architecture docs, onboarding guides, runbooks and SOPs, troubleshooting guides, release notes, and knowledge base articles - grounded in the actual code or product, with commands and examples verified where possible. Use when the user asks to document a project, feature, system, or process, write or fix a README, explain a codebase to new developers, turn notes into a guide, or make existing docs clearer. For endpoint-by-endpoint API reference or OpenAPI specs, use api-documentation-writer; for CLAUDE.md or AGENTS.md files, use claude-md-writer.
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。