Title: I built a design system generator inside Figma Make. Here's the workflow.

The problem I kept running into: every new client project meant re-explaining the brand to every AI tool from scratch. Figma Make didn't know the colors. Cursor didn't know the tokens. Claude didn't know the rules. I was the shared memory — and I kept losing things.

So I built Design System Gen. Entirely inside Figma Make. Powered by Claude API.

The workflow:

Upload brand references → Claude API analyzes them across 8 dimensions (palette, mood, typography, composition, lighting, texture, motion, UI DNA) → structured data, not vibes → generates 11 files, each formatted for a specific tool in your stack.

figma-make.md → paste once, Figma Make knows your entire design system design-system.css → all tokens as CSS custom properties tokens.json → W3C format, imports directly into Figma Variables starter.tsx → React starter with tokens already bound .cursorrules → Cursor knows your system from root Claude.md → Claude reads it automatically every session + 5 more for docs, LLMs, prompts, components

Every tool gets its own context file. All consistent with each other.

The part I'm most proud of: I'm a designer. I don't write code. The entire tool — UI, API integration, file generation logic — was built and shipped inside Figma Make. No external hosting. No separate codebase.

Live at: vibecodingdsgen.figma.site

Add your Claude API key and try it with your own moodboard.

#FigmaMake #DesignSystem #VibeCoding #AI #Claude #UXUI #DesignSystems #ConfigMakeathon

As someone who constantly jumps between Figma, Claude, ChatGPT, Cursor, Lovable, and other AI tools, this problem feels very familiar 😅

The tools are getting smarter, but keeping context consistent is still surprisingly manual. Love seeing people tackle that gap.

Description: ulTRACKare is a responsive healthcare operations dashboard tailored for rural health workers managing high-risk patient volumes, chronic condition tracking, and upcoming care visits. The core architecture uses a strict visual hierarchy that allows medical staff to check high-level operational counts (like active patients and open appointments) instantly at a single glance. Below the main metrics, the platform breaks down priority cases, using specialized, high-visibility alert cards to surface critical cases—such as severe hypertension alerts—ensuring urgent follow-up tasks are never overlooked.

AI Utilization & Execution: I utilized the Bubble AI app builder as the core foundation for this build by writing a highly structured, tokenized design prompt. The prompt directed the AI to establish a dark-mode user interface complete with frosted container cards, background blurs, and crisp 1px borders. After the AI built the framework, I took the wheel to clear data-type mismatches, clean container content states, and optimize the responsive layout to ensure a good user experience. https://montenegrokarlo.bubbleapps.io/version-test/patients?debug_mode=true