component-decision-tree

Component decision tree

A skill for building structured decision trees that map user intents and requirements to specific component selections. The output is a queryable framework that AI agents traverse to select the right component for a given need — eliminating the guesswork that leads to component misuse, duplication, and inconsistency.

Context

Component selection is the first decision in any design system interaction, and it is the one that AI agents get wrong most often. The failure mode is not random — it follows predictable patterns. An agent selects a Modal when a Dialog was appropriate. It uses a Card where a List Item fits better. It creates a custom component because it could not find the existing one that serves the need.

These errors have the same root cause: the agent does not have a decision framework. It has a list of components (if it has anything at all) and it pattern-matches the user's request against component names and descriptions. This works when the match is obvious ("I need a button" → Button) and fails when the match requires judgment ("I need to show a collection of items that users can filter and sort" → is that a Table, a DataGrid, a List with filters, or a custom composition?).

Decision trees encode the judgment. Instead of relying on an agent's ability to infer the right component from a description, the tree asks a structured sequence of questions that narrow the selection to the correct component. The questions are the same ones a senior designer or developer would ask when advising a junior team member.

The practical output is a structured file that agents load alongside component metadata. When an agent receives a request, it traverses the decision tree first to identify the component, then loads the component's metadata for configuration details.

Boundaries

This skill produces decision trees for component selection — choosing between components. It does not document how to use a single component once selected (use usage-guidelines for that) or generate component metadata schemas (use metadata-schema-generator). If the system has fewer than 5 components, a decision tree adds overhead without value — a simple component index is sufficient. If no component inventory exists, run component-audit or codebase-index first to establish one.