Case Study · Atlas · AI workflow

AI-Assisted Design System Generation

Structured Claude Skills that generate a React component library, Storybook docs, and a working dashboard.

ATLAScase study

Travel-ops dashboard · AI-generated design system

The Challenge

Component libraries are repetitive to build.

And documenting them slows product work. Could a structured AI workflow generate consistent, reusable UI while holding the quality of a modern frontend codebase?

RepetitiveBuild, doc, repeat
SlowDocs lag the code
GoalConsistent, fast

↻ build · doc · repeat

My Approach

Not one prompt. A staged pipeline.

I designed a multi-step Claude Skills workflow, where each stage hands its structured output to the next.

Fig. 01 · The workflow

Four stages, one spec

A structured skill.md drives generation. The same components that get documented also build the demo dashboard, proving the system works as a whole.

01 · Specskill.md
02 · GenerateComponents
03 · DocumentStorybook
04 · AssembleDashboard

01 skill.md specrequirements · APIs · a11y

02 Component libraryReact · consistent APIs

03 Storybooka story per component

04 Dashboardbuilt from the library

My Contributions

AI wrote the code. I designed the system.

Designed the skill.md specification
Built the Claude Skills workflow
Defined component architecture, conventions, and accessibility constraints
Built light and dark mode into the token layer, so every component themes automatically
Iterated on prompts, then evaluated and refined the output
Made sure the dashboard used only library components

Outcome

One spec in. A whole system out.

One spec produces a reusable React component library, full Storybook docs, and a production-style dashboard. They stay consistent because they share one system.

GitHub Storybook Live site

Library Docs Dashboard Tokens