Results for “architecture-discovery”

Analyzes codebases to generate comprehensive architectural documentation, including technology stack detection, visual diagrams, and implementation patterns.

Explore a codebase to find opportunities for architectural improvement, focusing on making the codebase more testable by deepening shallow modules. Use when user wants to improve architecture, find refactoring opportunities, consolidate tightly-coupled modules, or make a codebase more AI-navigable.

Systematically explore an unfamiliar codebase and build a reusable architecture summary with file:line evidence, starting from documentation and tracing data flow.

Estructura aplicaciones Flutter en módulos independientes y reutilizables con flutter_modular, incluyendo diseño de paquetes, dependencias y configuración de Melos.

Analyzes a local codebase to infer its actual architecture from code-first evidence, producing a dominant architecture label, coexisting patterns, drift, and repo-fit advice for downstream agents.

Analyzes codebases to understand existing features, tracing entry points, execution paths, architecture layers, patterns, and dependencies before new development.

Design system architecture, evaluate microservices vs monolith, create architecture diagrams, analyze dependencies, choose databases, and make technical decisions with architecture decision records.

Organizes Flutter code by business features instead of technical layers, with self-contained folders for UI, logic, and data, plus a recommended project structure and dependencies.

Find deepening opportunities in a codebase, informed by the domain language in CONTEXT.md and the decisions in docs/adr/. Use when the user wants to improve architecture, find refactoring opportunities, consolidate tightly-coupled modules, or make a codebase more testable and AI-navigable.

Compare tradeoffs and recommend architectural patterns — dependency injection, service-oriented architecture, repository, domain events, circuit breaker, and anti-corruption layer. Use when choosing between design patterns, planning microservices boundaries, evaluating system design alternatives, or asking 'which pattern should I use' for a specific coupling or resilience problem.

Sintetiza a análise do projeto legado em documentação arquitetural completa — diagramas C4, ERD completo, mapa de integrações e Spec Impact Matrix. Use na fase de interpretação após o reversa-detective.

Guides module boundaries, dependency direction, and domain structure. Do not use for local edits that leave architecture unchanged.

Extrai conhecimento de negócio implícito do projeto legado — regras de negócio, ADRs retroativos via Git, máquinas de estado e matriz de permissões. Use na fase de interpretação de uma análise de engenharia reversa.

Re-derive your knowledge system from first principles when structural drift accumulates. Analyzes dimension incoherence, vocabulary mismatch, boundary dissolution, and template divergence. Preserves all content while restructuring architecture.

AL event-driven architecture for Business Central. Use when creating EventSubscribers, IntegrationEvents, BusinessEvents, or implementing publisher/subscriber patterns in extensions.

Organizes Flutter code into domain, data, and presentation layers with unidirectional dependencies, using BLoC, use cases, and repository patterns for testable, maintainable enterprise applications.

Application architecture patterns and code organization — clean architecture, hexagonal architecture, feature-based structure, dependency inversion, module boundaries, SOLID principles at module scale. Activate on "clean architecture", "hexagonal architecture", "feature-based structure", "dependency inversion", "module boundaries", "architecture pattern", "folder structure", "fat controller", "circular dependency", "IoC container". NOT for microservice decomposition (use microservices-patterns) or database schema design (use database-design-patterns).

Generates a codebase architecture document and CLAUDE.md scaffold by exploring source code and asking targeted questions.

Finds architectural friction in a codebase and proposes deep-module refactors to improve testability and AI-navigability, informed by the domain glossary and ADRs.