Component Decision Tree

by murphytrueman

Component Decision Tree is an AI Engineering skill for Claude Code, published by murphytrueman in design-system-ops.

176 stars7 forkson murphytrueman/design-system-opsAdded 2026/09/07+1% in starsRepository updated 2026/08/22

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claude-codeclaude-code-plugindesign-systemdesign-systemsdesign-tokensfigmaskills

Install in seconds

Install Component Decision Tree

Copy Component Decision Tree into your Claude Code skills folder. Run the command in your terminal, or review the source on GitHub before installing.

terminal

npx degit https://github.com/murphytrueman/design-system-ops/tree/main/skills/component-decision-tree ~/.claude/skills/component-decision-tree

Requires Node.js. Downloads this skill only — not the rest of the repository — into your Claude Code skills folder.

Without Node.js

git clone https://github.com/murphytrueman/design-system-ops.git

Clones the whole repository, then copy the skill’s own directory into your skills folder yourself.

In this catalog

Source file: skills/component-decision-tree/SKILL.md in murphytrueman/design-system-ops
Installs to: ~/.claude/skills/component-decision-tree
Collection: One of 25 skills cataloged from this repository
Category: AI Engineering — 3475 skills

What Component Decision Tree does

Component Decision Tree builds structured YAML decision trees that help agents choose the right component from alternatives. Use it when you need intent-to-component mapping and narrowing questions, not usage guidance for a single component.

Component Decision Tree is cataloged under AI Engineering on DirSkills. Component Decision Tree comes from a repository tagged claude-code, claude-code-plugin, design-system, design-systems and design-tokens.

Documentation

README

Excerpt

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.

This is the opening of the README. Read the full README on GitHub.

Frequently asked about Component Decision Tree

What else does murphytrueman publish alongside Component Decision Tree?
Component Decision Tree is one of 25 skills that DirSkills catalogs from murphytrueman/design-system-ops, the repository it ships in. Its siblings there include AI Component Description, Accessibility Per Component and Adoption Report. Each one is a separate skill with its own page in this directory, installs the same way Component Decision Tree does, and is maintained by murphytrueman in that same repository. The rest of the collection is listed on the murphytrueman/design-system-ops page.
How does Component Decision Tree compare to other AI Engineering skills?
Component Decision Tree ranks #3144 by stars among the 3475 AI Engineering skills in this catalog. The most-starred ones next to it are Architecture Decision Records, AI-First Engineering and Agentic OS. DirSkills ranks by the star count of the repository each skill ships in, so that order reflects how popular those repositories are rather than any review of Component Decision Tree against them. Open each page to compare what they document and how they install.