design

Feature Design Skill

Purpose

This skill provides systematic guidance for designing software architecture, API contracts, data models, and workflows based on analyzed requirements.

When to Use

• After requirements analysis is complete
• Need to design technical architecture for a feature
• Defining API contracts and data structures
• Planning module interactions and data flows
• Before starting implementation

Design Workflow

1. Architecture Design

Choose Architectural Pattern: Review architecture-patterns.md for appropriate patterns:

• Layered Architecture: UI → Business Logic → Data Access
• Modular Architecture: Cohesive modules with clear interfaces
• Event-Driven: Message-based communication
• Microservices: Independent, deployable services (if applicable)

For This Project (Python):

• Follow existing structure: src/tools/, src/core/, src/utils/
• Use dependency injection for testability
• Keep files under 500 lines (split if needed)
• Maintain single responsibility principle

Define Components:

Component Name: <name>
Responsibility: <what it does>
Dependencies: <what it needs>
Interfaces: <public API>

Deliverable: Component diagram with responsibilities

2. Data Model Design

Define Entities:

• Identify domain entities from requirements
• Define attributes and types
• Specify relationships (one-to-one, one-to-many, many-to-many)
• Define validation rules
• Consider data lifecycle (CRUD operations)

For Python Projects:

from pydantic import BaseModel, Field
from typing import Optional, List
from datetime import datetime

class EntityModel(BaseModel):
    """Entity description."""
    id: Optional[int] = None
    name: str = Field(..., min_length=1, max_length=255)
    created_at: datetime = Field(default_factory=datetime.utcnow)

    class Config:
        """Pydantic configuration."""
        validate_assignment = True

Deliverable: Data models with Pydantic schemas