data

Data - Data Layer Generator

Generate data layer components that implement domain interfaces and handle external data sources.

Philosophy

The data layer is the only place for:

• Freezed models with JSON serialization
• API field mapping (@JsonKey)
• Repository implementations
• Data source abstractions (remote, local, cache)

This keeps the domain pure while handling all serialization and external communication here.

When to Use This Skill

• After /domain has created entities and repository interfaces
• When implementing API integration
• When adding local storage/caching
• When you have backend code or OpenAPI specs as reference
• User asks to "implement repository", "create DTOs", or "wire up API"

Note: This skill replaces the former /api skill. All API integration functionality is now here.

What This Skill Creates

lib/features/{feature}/data/
├── models/
│   ├── {entity}_model.dart       # Freezed DTO with JSON
│   └── {entity}_model.g.dart     # Generated JSON code
├── repositories/
│   └── {feature}_repository_impl.dart  # Implements domain interface
└── data_sources/
    ├── {feature}_remote_data_source.dart   # API calls
    └── {feature}_local_data_source.dart    # Local storage (optional)

Input Sources

Source	What Claude Extracts
Feature spec (.spec.md)	Endpoints, DTOs, storage needs
Backend code (NestJS, Express)	Controllers, DTOs, types
OpenAPI/Swagger spec	Paths, schemas, request/response shapes
Endpoint descriptions	GET /users → User[]

Workflow

Step 1: Locate Requirements

Check for:

lib/features/{feature}/.spec.md           # Feature specification
lib/features/{feature}/domain/            # Domain entities/repository interface

If domain layer doesn't exist, ask user to run /domain {feature} first.

Step 2: Analyze Data Sources

From spec:

• API Endpoints - Section 3.1 (URLs, methods, request/response shapes)
• Local Storage - Section 3.2 (what to persist, cache strategy)
• Field Mappings - API field names vs domain field names