“ddd”的搜索结果

Compute genome-to-genome distances (ANI, AAI, dDDH, k-mer Mash) and assign taxonomic classifications using skani (Shaw 2023), FastANI (Jain 2018), pyani / pyANI ANIb / ANIm, OrthoANI (Lee 2016), AAI (amino-acid identity), dDDH via TYGS / GGDC, GTDB-Tk (Chaumeil 2020 standard prokaryote taxonomy), and Mash MinHash (Ondov 2016). Use when delineating prokaryote species (95% ANI threshold; Jain 2018 Nat Commun 9:5114), assigning genomes to GTDB taxonomy with ANI radius, computing genome similarity matrices for clustering, classifying archaea, evaluating MAG (metagenome-assembled genome) species assignment, applying skani for fast metagenomic ANI screening, or reconciling 16S rRNA-based taxonomy with whole-genome ANI.

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Extract a DDD-style ubiquitous language glossary from the current conversation, flagging ambiguities and proposing canonical terms. Saves to UBIQUITOUS_LANGUAGE.md. Use when user wants to define domain terms, build a glossary, harden terminology, create a ubiquitous language, or mentions "domain model" or "DDD".

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Create, modify, or refactor application use cases into Laravel-native DDD Actions. Use for business operations such as placing or cancelling orders, extracting controller orchestration, and fixing use-case behavior.

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Create, extend, or fix Eloquent business models and their state transitions. Use when moving business rules out of controllers or Actions, or explicitly migrating a model into a DDD capability.

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ドメイン定義書の新規作成・更新・レビューを支援する。AI駆動開発成果物フローのビジネス要求(D1)に位置する成果物で、本プロダクトを構成する業務領域(ドメイン)を網羅・分類する文書。問題空間(プロダクトが扱う業務領域)の整理であり、DDDにおけるBounded Context(解空間としての実装上の境界)とは別レイヤーで扱う。BRDの下流に位置する成果物。後続のプロダクト要求仕様書、ドメイン要求仕様書群(各ドメインごとに1つ)など、様々な成果物から参照される。BRD策定後、ドメイン整理の段階で呼び出す。

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DDD v3 — bounded contexts, aggregates, value objects, domain events, Ubiquitous Language, strategic design

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DDDに基づいたドメインモデルを設計します。戦略的設計（境界コンテキスト、ユビキタス言語）と戦術的設計（エンティティ、値オブジェクト、集約、ドメインサービス、ドメインイベント）を定義し、ヘキサゴナルアーキテクチャとの統合を行います。ScalarDBのトランザクション境界や管理テーブル分類の考慮事項を含みます。中間状態はresearch/に記録されます。

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システム調査エージェント - コードベースの構造・技術スタック・問題点・DDD適合性を調査。/system-investigation [対象パス] で呼び出し。

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評価統合エージェント - MMI評価とDDD評価の結果を統合し、優先度付き改善計画を策定。/integrate-evaluations [対象パス] で呼び出し。

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Extract a DDD-style ubiquitous language glossary from the current conversation, flagging ambiguities and proposing canonical terms. Saves to UBIQUITOUS_LANGUAGE.md. Use when user wants to define domain terms, build a glossary, harden terminology, create a ubiquitous language, or mentions "domain model" or "DDD".

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Facilitate DDD domain modeling sessions via EventStorming conversation and DML (Domain Modeling Language). Use whenever the user wants to model a business domain by discovering domain events, commands, aggregates, policies, read models, and bounded contexts through dialogue. Produces incrementally-built DML as the primary artifact, plus a Markdown session report. Also invoke for refining existing DML, mapping out a new feature's domain model, or when the user says "ドメインモデリングしたい", "イベントストーミング", "DDDで整理したい", "DMLを育てたい".

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Compute genome-to-genome distances (ANI, AAI, dDDH, k-mer Mash) and assign taxonomic classifications using skani (Shaw 2023), FastANI (Jain 2018), pyani / pyANI ANIb / ANIm, OrthoANI (Lee 2016), AAI (amino-acid identity), dDDH via TYGS / GGDC, GTDB-Tk (Chaumeil 2020 standard prokaryote taxonomy), and Mash MinHash (Ondov 2016). Use when delineating prokaryote species (95% ANI threshold; Jain 2018 Nat Commun 9:5114), assigning genomes to GTDB taxonomy with ANI radius, computing genome similarity matrices for clustering, classifying archaea, evaluating MAG (metagenome-assembled genome) species assignment, applying skani for fast metagenomic ANI screening, or reconciling 16S rRNA-based taxonomy with whole-genome ANI.

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Compute genome-to-genome distances (ANI, AAI, dDDH, k-mer Mash) and assign taxonomic classifications using skani (Shaw 2023), FastANI (Jain 2018), pyani / pyANI ANIb / ANIm, OrthoANI (Lee 2016), AAI (amino-acid identity), dDDH via TYGS / GGDC, GTDB-Tk (Chaumeil 2020 standard prokaryote taxonomy), and Mash MinHash (Ondov 2016). Use when delineating prokaryote species (95% ANI threshold; Jain 2018 Nat Commun 9:5114), assigning genomes to GTDB taxonomy with ANI radius, computing genome similarity matrices for clustering, classifying archaea, evaluating MAG (metagenome-assembled genome) species assignment, applying skani for fast metagenomic ANI screening, or reconciling 16S rRNA-based taxonomy with whole-genome ANI.

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Domain-Driven Design methodology using bounded contexts, ubiquitous language, and strategic/tactical patterns

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DDD architecture knowledge base. Provides patterns, antipatterns, and PHP-specific guidelines for Domain-Driven Design audits.

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Extract a DDD-style domain glossary from conversation context, choosing canonical terms and flagging ambiguity. Use when the user asks to define domain terms, build a glossary, clarify terminology, create a ubiquitous language, or mentions domain modeling or DDD.

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Turn an approved specification into pseudocode, a plain outline of program logic, and an architecture defining module responsibilities and API inputs and outputs. After user confirmation, proceed with implementation and unit tests based on those designs.

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• Turning an approved specification into pseudocode
• Defining module boundaries and dependency rules
• Designing API inputs, outputs, and error responses

Plan backend architecture using Clean Architecture, Hexagonal Architecture, or Domain-Driven Design. Clarify business boundaries, choose a suitable structure, define how modules depend on each other, and outline migration steps and validation checks.

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• Design the structure of a new backend
• Reduce tight coupling in a monolithic application
• Define shared architecture standards for a team

Design or refactor backend services with clear layers and dependency rules. Using Clean Architecture, Hexagonal Architecture, and Domain-Driven Design, it separates business logic from databases and frameworks and defines interfaces that support testing without external services.

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• Designing the structure of a new backend service
• Separating business logic from database and HTTP code
• Resolving circular dependencies between layers

Plan backend systems with clear module boundaries and dependency rules. This skill helps select architecture patterns, organize business logic, and outline migration steps for systems that are easier to maintain and test.

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• Designing a new backend system
• Restructuring a monolithic application
• Defining shared architecture standards

Organize existing code components into business areas to prepare for service separation. Produce domain maps, component assignment tables, and refactoring plans that align namespaces, the naming structure used to organize code, with business responsibilities.

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• Grouping existing components by business responsibility
• Planning service extraction from an existing application
• Aligning code namespaces with business areas

Idiomatic Golang design patterns — functional options, constructor APIs, `init()` and global-state avoidance, enums, panic vs error decisions, resource management and lifecycle, graceful shutdown, timeouts and retries, streaming and iterators, and architecture styles (clean, hexagonal, DDD, flat). Apply when choosing between architectural patterns, implementing functional options, designing constructor APIs, setting up graceful shutdown, applying resilience patterns, or asking which idiomatic Go pattern fits a specific problem. Not for wiring a DI container or comparing DI libraries (→ See `samber/cc-skills-golang@golang-dependency-injection` skill), nor for error wrapping, `errors.Is`/`As`, or logging mechanics (→ See `samber/cc-skills-golang@golang-error-handling` skill).

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Apply Vaughn Vernon-inspired lightweight DDD rules when using bounded contexts, subdomains, context maps, and tactical modeling without excess ceremony.

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Apply Vaughn Vernon-inspired implementation DDD rules when building aggregates, domain events, repositories, application services, or context integrations.

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