component-decision-tree

What problem does it solve?

This Skill solves the problem of inconsistent and incorrect component selection by providing a structured, queryable decision framework that guides users and AI agents to the most appropriate component for a given need.

Core Features & Use Cases

• Intent-to-Component Mapping: Creates decision trees that map user intents and requirements to specific components.
• Disambiguation Logic: Explicitly addresses common confusion points between similar components (e.g., Modal vs. Dialog).
• Automated Data Integration: Can pull component data from Figma, Storybook, and codebase indexes to inform tree generation.
• Use Case: An AI agent needs to display a notification. Instead of guessing, it traverses the decision tree, answering questions about persistence, severity, and blocking behavior to correctly select between Toast, Banner, Alert, or InlineMessage.

Quick Start

Use the component-decision-tree skill to generate a decision tree for selecting between notification components.