This content originally appeared on DEV Community and was authored by opcheese
The Challenge: Real Project, Real Deadlines
Let me start with the reality: we needed to build a complex address selection component for a Russian election information system. Not a simple dropdown—a 4-level hierarchical system where users select Region → Settlement → Street → House, with each level requiring separate API calls and auto-selection when only one option is available.
Traditional estimate: 2-3 weeks
Our result: 30 minutes to production-ready code
Design revision cycles: Zero
Here’s exactly how we did it, with real code examples and honest breakdowns of what works and what doesn’t.
The Foundation: Why shadcn/ui Changes Everything
Before diving into the workflow, let’s talk about why we chose shadcn/ui as our foundation. This isn’t just another component library—it’s a systematic approach to design-to-code consistency that makes AI assistance predictable.
The shadcn/ui Architecture
shadcn/ui provides:
- Copy-paste components instead of npm dependencies
- Radix UI primitives for accessibility and behavior
- Tailwind CSS for styling flexibility
- CSS variables for theme customization
- TypeScript interfaces that match component behavior
But here’s the key insight: shadcn/ui has a Figma kit that mirrors the code implementation exactly.
shadcn/ui Figma Kit: The Missing Link
The shadcn/ui Figma kit provides:
// What the Figma kit gives you:
- Components that match code behavior exactly
- Design tokens that map to CSS variables
- Variant systems that mirror React props
- State management that reflects code logic
- Typography scales that match Tailwind classes
Base select component from shadcn/ui Figma kit with proper variants
This eliminates the translation layer. When your designer uses the kit, every component property maps directly to code props. Every color token exists in your CSS. Every spacing value matches your Tailwind config.
Our Enhanced CSS Variable System
We extend the shadcn/ui foundation with Figma-specific variables:
:root {
/* Base shadcn/ui variables */
--background: hsl(0 0% 100%);
--foreground: hsl(240 10% 3.9%);
--primary: hsl(240 5.9% 10%);
/* Extended Figma-specific variables */
--figma-primary: #7d5ce6;
--figma-primary-accent: #f5f2fd;
--figma-secondary-accent: #e8e3fb;
--figma-primary-text: #323255;
--figma-secondary-text: #64636b;
--figma-input-stroke: #e3e3e6;
--figma-foreground: #ffffff;
/* Typography classes matching Figma text styles */
.title-large {
font-family: 'Mulish', sans-serif;
font-size: 20px;
font-weight: 700;
line-height: 24px;
}
.body-large {
font-family: 'Mulish', sans-serif;
font-size: 14px;
font-weight: 400;
line-height: 20px;
}
}
The result: Designers and developers share the same vocabulary. When a designer uses primary-accent in Figma, developers use var(--figma-primary-accent) in code.
Designer Requirements: Component Thinking is Non-Negotiable
The biggest failure point in most Figma-to-code workflows is inadequate component structure. Here’s what designers must understand and implement:
1. Atomic Component Design
// Wrong: Monolithic component
❌ "Address Form" (everything in one component)
// Right: Atomic components
✅ "Select" (base component with variants)
✅ "Address Header" (title + description)
✅ "Address Result Card" (polling station display)
✅ "Address Input System" (composition of above)
2. Proper Variant Definition
Every component variant must map to React props:
// Figma component properties → React props
Figma: variant = "Primary" | "Secondary" | "Ghost"
React: variant?: "primary" | "secondary" | "ghost"
Figma: state = "Default" | "Click" | "Loading" | "Disabled"
React: state?: "default" | "click" | "loading" | "disabled"
Figma: showLeftIcon = boolean
React: showLeftIcon?: boolean
Proper button component with variants that map directly to React props
3. Boolean Properties for Conditional Logic
// Component properties in Figma:
showLeftIcon: boolean
showRightIcon: boolean
showIcon: boolean
itemText: string (default: "Item text")
endIcon: instance swap (optional)
These map directly to React component logic:
interface SelectMenuItemProps {
itemText?: string;
showIcon?: boolean;
endIcon?: React.ReactNode | null;
state?: "Default" | "Selected" | "Click";
}
// Conditional rendering based on Figma properties
{showIcon && (
endIcon || (
<div className="flex size-4 items-center justify-center">
<CheckIcon />
</div>
)
)}
4. Design Token Usage (No Hardcoded Values)
// Wrong: Hardcoded values
Fill: #f5f2fd
Text: #323255
Border: #e3e3e6
// Right: Design tokens
Fill: Colors/Base/primary-accent
Text: Colors/Base/primary-text
Border: Colors/Base/input-stroke
5. Component Matrix Documentation
For complex components, designers must create component matrices showing all state combinations:
// Button component matrix:
Primary + Default
Primary + Click
Primary + Loading
Primary + Disabled
Secondary + Default
Secondary + Click
... (all combinations)
This matrix becomes the specification AI uses for implementation.
Complete design system with variables that map to CSS custom properties
The Documentation Generation Process: Figma MCP in Action
Now let’s dive deep into how we extract this structured information from Figma components using the Model Context Protocol (MCP).
Understanding Figma MCP
The Figma MCP server provides programmatic access to Figma files through a standardized interface. Unlike the REST API, MCP provides structured access optimized for AI context.
MCP capabilities:
-
get_code– Extract component code and structure -
get_variable_defs– Get all design variables and tokens -
get_image– Capture component screenshots -
search_files– Find components across projects
Our Extraction Script Architecture
// figma-extractor.ts - Our automated extraction pipeline
import { MCPClient } from '@modelcontextprotocol/client';
interface FigmaExtractorConfig {
fileId: string;
nodeIds: string[];
outputDir: string;
includeVariables: boolean;
includeImages: boolean;
}
class FigmaComponentExtractor {
private mcpClient: MCPClient;
private config: FigmaExtractorConfig;
async extractComponent(nodeId: string): Promise<ComponentSpec> {
// Step 1: Get component code structure
const codeData = await this.mcpClient.call('get_code', {
node_id: nodeId,
include_variants: true,
include_properties: true
});
// Step 2: Extract design variables
const variables = await this.mcpClient.call('get_variable_defs', {
file_id: this.config.fileId,
filter_used_only: true
});
// Step 3: Capture component images
const images = await this.mcpClient.call('get_image', {
node_id: nodeId,
format: 'png',
scale: 2
});
// Step 4: Generate structured documentation
return this.generateComponentSpec({
code: codeData,
variables,
images,
nodeId
});
}
}
Node ID Collection Process
We use a Google Sheets workflow for tracking components:
// Google Sheets structure:
// Column A: Component Name
// Column B: Figma URL
// Column C: Extracted Node ID
// Column D: Status
// Column E: Last Updated
// Example row:
"Select Menu Item" | "https://figma.com/file/..." | "92_459" | "Extracted" | "2024-12-15"
// Node ID extraction from Figma URL:
function extractNodeId(figmaUrl: string): string {
// Figma URLs contain node ID after "node-id="
// https://www.figma.com/design/file123?node-id=92-459&t=abc123
const match = figmaUrl.match(/node-id=([^&]+)/);
return match ? match[1].replace('-', '_') : '';
}
Generated Documentation Structure
For each component, our script generates comprehensive documentation:
# Select Menu Item Component
## Generated Code Structure
typescript
interface SelectMenuItemProps {
itemText?: string;
showIcon?: boolean;
endIcon?: React.ReactNode | null;
state?: “Default” | “Selected” | “Click”;
}
function SelectMenuItem({
itemText = “Item text”,
showIcon = true,
endIcon = null,
state = “Default”
}: SelectMenuItemProps) {
// Implementation based on Figma structure
}
## Design Variables Used
css
–figma-primary-text: #323255 (Colors/Base/primary-text)
–figma-foreground: #ffffff (Colors/Base/foreground)
–figma-primary-accent: #f5f2fd (Colors/Base/primary-accent)
–figma-secondary-accent: #e8e3fb (Colors/Base/secondary-accent)
## Component Variants
- Default: `bg-[var(--figma-foreground)] font-normal`
- Selected: `bg-[var(--figma-primary-accent)] font-semibold`
- Click: `bg-[var(--figma-secondary-accent)] font-normal`
## Integration Requirements
- Uses Radix UI SelectItem primitive
- Requires CheckIcon from lucide-react
- Must support conditional icon rendering
- Should handle item text overflow with truncation
Real MCP Integration Example
Here’s how we use Figma MCP within our RooCode workflow:
// .roo/tools/figma-mcp.ts
export class FigmaMCPTool {
async getComponentSpec(nodeId: string): Promise<string> {
const response = await fetch('http://localhost:3000/mcp/figma', {
method: 'POST',
body: JSON.stringify({
method: 'get_code',
params: { node_id: nodeId }
})
});
return response.text();
}
async getDesignVariables(): Promise<Record<string, string>> {
const response = await fetch('http://localhost:3000/mcp/figma', {
method: 'POST',
body: JSON.stringify({
method: 'get_variable_defs',
params: { file_id: process.env.FIGMA_FILE_ID }
})
});
return response.json();
}
}
Our Simple Extraction Script
Instead of complex batch processing systems, we use a straightforward Node.js script that reads a CSV file and processes Figma components:
// The actual script we use - simple and effective
import { MCPClient } from "mcp-client";
import fs from "fs-extra";
import { parse } from "csv-parse/sync";
// Read CSV with component descriptions and Figma URLs
const csv = fs.readFileSync("demofigma2.csv", "utf8");
const rows: [string, string][] = parse(csv, { skip_empty_lines: true });
// Connect to Figma MCP server
const client = new MCPClient({ name: "FigmaDocGen", version: "1.0.0" });
await client.connect({ type: "sse", url: "http://localhost:3845/sse" });
// Process each component
for (const comp of components) {
const nodeId = extractNodeId(comp.link);
// Get component code and structure
const codeResult = await client.callTool({
name: "get_code",
arguments: {
nodeId: nodeId,
clientLanguages: "typescript",
clientFrameworks: "react"
}
});
// Extract variables used in the component
const code = codeResult.content?.[0]?.text || "";
const variables = codeResult.content?.[2]?.text || "";
// Get component screenshot
const imageResult = await client.callTool({
name: "get_image",
arguments: { nodeId: nodeId }
});
// Generate markdown documentation
const md = [
`# ${comp.description}`,
`## Code\n\`\`\`typescript\n${code}\n\`\`\``,
`## Variables:\n ${variables}`,
``
];
await fs.writeFile(`docs/${comp.description}/index.md`, md.join('\n\n'));
}
What this simple script does:
- Reads CSV with component names and Figma URLs
- Extracts node IDs from Figma URLs
- Calls Figma MCP to get code structure and variables
- Captures screenshots of each component
- Generates markdown docs with code, variables, and images
CSV Structure:
Button Sample all, https://figma.com/file/...?node-id=82-909
Select Menu Item, https://figma.com/file/...?node-id=92-459
Typography, https://figma.com/file/...?node-id=95-1597
Generated Output:
figma_docs/
├── component_1_Button_Sample_all/
│ ├── index.md
│ └── img_1.png
├── component_2_Select_Menu_Item/
│ ├── index.md
│ └── img_1.png
└── component_3_Typography/
├── index.md
└── img_1.png
Key insight: The script is intentionally simple – no complex batch processing, retry logic, or dependency management. Just straightforward CSV → Figma MCP → Documentation pipeline.
Variable Extraction and CSS Generation: The LLM-Powered Approach
Our variable mapping process is elegantly simple: we use standard shadcn/ui and Tailwind variables, just with the right values from Figma. The LLM handles the intelligent mapping using Context7 MCP documentation.
What Our Figma Script Extracts
From the typography component shown above, our script extracts this variables section:
## Variables:
onSurfaces/onGeneral: #212529,
onSurfaces/onBgElevated: #3b4146,
spacing/m: 32,
surfaces/bgElevated2: #d3d7da,
spacing/l: 48,
surfaces/bgGeneral: #ffffff,
font/size/text-l: 20,
font/family/font-sans: Mulish,
font/line-height/m: 24,
font/weight/bold: 700,
Title/Large: Font(family: "Mulish", style: Bold, size: 20, weight: 700, lineHeight: 24),
Body/Large: Font(family: "Mulish", style: Regular, size: 14, weight: 400, lineHeight: 20),
Body/Medium: Font(family: "Mulish", style: Regular, size: 12, weight: 400, lineHeight: 16)
Simple extraction: Just the raw values from Figma, no complex mapping logic.
Context7 MCP: shadcn/Tailwind Documentation
Context7 MCP provides the LLM with current shadcn/ui and Tailwind documentation, including examples like:
/* Standard shadcn/ui variables from Context7 */
:root {
--background: oklch(1 0 0);
--foreground: oklch(0.145 0 0);
--primary: oklch(0.205 0 0);
--primary-foreground: oklch(0.985 0 0);
--secondary: oklch(0.97 0 0);
--border: oklch(0.922 0 0);
--radius: 0.625rem;
}
LLM-Generated CSS Mapping
When provided with both:
- Figma variable extraction (raw values)
- Context7 shadcn documentation (standard structure)
The LLM generates the correct CSS file:
:root {
/* Border Radius - From Figma Design System */
--radius: 0.375rem; /* 6px - Border radius/m from Figma */
/* Standard shadcn/ui Variables - Mapped to Figma Colors */
--background: hsl(0 0% 100%); /* surfaces/bgGeneral: #ffffff */
--foreground: hsl(240 26% 26%); /* onSurfaces/onGeneral: #323255 */
--primary: hsl(252 73% 64%); /* Colors/Base/primary: #7d5ce6 */
--secondary: hsl(252 73% 97%); /* Colors/Base/primary-accent: #f5f2fd */
--border: hsl(240 5% 89%); /* Colors/Base/input-stroke: #e3e3e6 */
}
/* Figma Typography Classes */
.title-large {
font-family: 'Mulish', sans-serif;
font-size: 20px;
font-weight: 700;
line-height: 24px;
}
.body-large {
font-family: 'Mulish', sans-serif;
font-size: 14px;
font-weight: 400;
line-height: 20px;
}
Key insight: The LLM understands both the standard shadcn structure (from Context7) and our Figma values (from extraction), then intelligently maps them together.
Why This Approach Works
No Complex Mapping System: We don’t create custom variable names – we use standard --primary, --background, --border, etc.
LLM Intelligence: The AI understands which Figma color should map to which shadcn variable based on usage context and naming patterns.
Flexible Adaptation: When Figma has additional values not in standard shadcn, the LLM can add them appropriately (like our --figma-primary-accent for specific design needs).
Future-Proof: As shadcn/ui evolves, Context7 MCP provides updated documentation, so our mapping stays current automatically.
The beauty is in the simplicity – extract raw values from Figma, provide standard documentation via Context7, let the LLM do the intelligent mapping.
The Systematic Approach That Actually Works
With proper designer setup and automated extraction, here’s our three-phase workflow:
Phase 1: Systematic Figma Extraction (2 minutes)
Instead of hoping AI understands our design, we extract complete specifications automatically.
# Run extraction script
npm run extract-components -- --file-id=ABC123 --batch=select-components.json
# Generated output:
docs/components/select-menu-item.md
docs/components/select-trigger.md
docs/components/select-content.md
assets/select-menu-item.png
assets/select-trigger.png
Our script processes Figma components and generates:
// Generated TypeScript interfaces
interface SelectMenuItemProps {
itemText?: string;
showIcon?: boolean;
endIcon?: React.ReactNode | null;
state?: "Default" | "Selected" | "Click";
}
// Extracted design variables
--figma-foreground: #ffffff (default background)
--figma-primary-accent: #f5f2fd (selected background)
--figma-secondary-accent: #e8e3fb (hover background)
--figma-primary-text: #323255 (text color)
--figma-input-stroke: #e3e3e6 (border color)
Key insight: When you provide complete context instead of vague requests, AI generates code that integrates perfectly with existing projects.
Phase 2: AI Implementation with Context (15 minutes)
We use RooCode with systematic workflow rules, but the principle applies to any AI coding tool: provide complete specifications, not wishful prompts.
Instead of: “Build a select component”
We provide:
// Complete specification from Figma extraction
interface AddressInputSystemProps {
onAddressComplete?: (address: any) => void;
onViewElections?: () => void;
onShowOnMap?: () => void;
}
// Integration requirements
const { regions, settlements, streets, houses,
isLoadingRegions, fetchRegions } = useAddressStore();
const { currentPollingStation, fetchPollingStation } = usePollingStationStore();
// Design token mapping
"bg-[var(--figma-primary-accent)]" // Instead of "bg-[#f5f2fd]"
"text-[var(--figma-primary-text)]" // Instead of "text-[#323255]"
Phase 3: Triple-Gate Verification (8 minutes)
This is what prevents AI-generated technical debt:
Gate 1: Working Code
- Compiles without TypeScript errors
- Basic functionality works as expected
- Renders properly in development
Gate 2: Verified Functionality
- Matches Figma design exactly
- All variants and states work correctly
- Integrates with existing codebase
Gate 3: Developer Understanding
- Can explain how the component works
- Understands architectural decisions
- Ready to maintain and extend code
No gate can be skipped. This prevents accumulating code you don’t understand.
Real Implementation: The Select Component
Here’s the actual code AI generated using our systematic approach:
function SelectItem({
className,
children,
...props
}: React.ComponentProps<typeof SelectPrimitive.Item>) {
return (
<SelectPrimitive.Item
className={cn(
// Base layout matching Figma: px-3 py-2, gap-2, h-[33px]
"relative flex h-[33px] w-full cursor-default select-none items-center gap-2 rounded px-3 py-2 outline-none",
// Typography: Mulish Regular 14px, 20px line height
"font-['Mulish'] text-[14px] font-normal leading-[20px] text-[var(--figma-primary-text)]",
// Default state: white background
"bg-[var(--figma-foreground)]",
// Hover state: secondary accent background (#e8e3fb)
"hover:bg-[var(--figma-secondary-accent)]",
// Selected state: primary accent background (#f5f2fd) with semibold font
"data-[state=checked]:bg-[var(--figma-primary-accent)] data-[state=checked]:font-semibold",
// Disabled state
"data-[disabled]:pointer-events-none data-[disabled]:opacity-50",
className
)}
{...props}
>
<SelectPrimitive.ItemText className="flex-1 truncate">
{children}
</SelectPrimitive.ItemText>
<SelectPrimitive.ItemIndicator className="flex size-4 items-center justify-center">
<div className="flex size-4 items-center justify-center [&>svg]:size-full">
<CheckIcon />
</div>
</SelectPrimitive.ItemIndicator>
</SelectPrimitive.Item>
)
}
What’s remarkable: AI generated mobile-optimized touch targets (33px height), proper CSS variable usage, and automatic icon sizing—all because we provided complete context.
The Address Input System: Complex Logic Made Simple
Our most complex component handles 4-level hierarchical selection with auto-selection logic:
// Auto-selection logic (AI-generated)
const handleRegionSelect = async (region: Region) => {
setSelectedRegion(region);
setSelectedSettlement(null);
setSelectedStreet(null);
setSelectedHouse(null);
try {
const loadedSettlements = await fetchSettlements(region.name);
// Auto-select if only one settlement
if (loadedSettlements.length === 1) {
handleSettlementSelect(loadedSettlements[0]);
}
} catch (error) {
console.error('Failed to fetch settlements:', error);
}
};
// Complete address creation
const address = {
region: selectedRegion.name,
regionId: selectedRegion.id,
settlement: selectedSettlement.name,
settlementId: selectedSettlement.id,
street: selectedStreet.name,
streetId: selectedStreet.id,
houseNumber: house.name,
houseId: house.id,
pollingPlaceId: house.pollingPlace.id,
fullAddress: `${selectedRegion.name}, ${selectedSettlement.name}, ${selectedStreet.name}, ${house.name}`,
};
Integration: The component seamlessly connects with our existing Zustand stores and maintains proper TypeScript types throughout.
What Actually Breaks (And How We Fix It)
Even with systematic preparation, common issues arise:
Problem 1: Color Hardcoding
// ❌ AI's first attempt
className="bg-[#f5f2fd] text-[#323255] border-[#e3e3e6]"
// ✅ After systematic correction
className="bg-[var(--figma-primary-accent)] text-[var(--figma-primary-text)] border-[var(--figma-input-stroke)]"
Problem 2: Tailwind Version Conflicts
// ❌ LLMs default to Tailwind 3
className="h-9 w-full rounded-md border border-input"
// ✅ Tailwind 4 + Figma variables
className="h-9 w-full rounded border border-[var(--figma-input-stroke)]"
Problem 3: State Management Complexity
// ❌ Over-engineered
const [complexState, setComplexState] = useState({...})
// ✅ Simple integration
const { regions, fetchRegions } = useAddressStore();
The systematic advantage: Problems become predictable and manageable through proper verification gates.
Performance Results: 300+ Items Without Virtualization
Our select component handles massive datasets efficiently:
// Performance optimization through native browser capabilities
function SelectContent({ className, children, position = "popper", ...props }) {
return (
<SelectPrimitive.Portal>
<SelectPrimitive.Content
className={cn(
// Base container with native scroll performance
"relative z-50 max-h-[var(--radix-select-content-available-height)] overflow-hidden",
// Mobile optimization: proper touch targets
"rounded-lg border border-[var(--figma-input-stroke)] bg-[var(--figma-foreground)]",
className
)}
position={position}
{...props}
>
<SelectScrollUpButton />
<SelectPrimitive.Viewport className="p-1">
{children}
</SelectPrimitive.Viewport>
<SelectScrollDownButton />
</SelectPrimitive.Content>
</SelectPrimitive.Portal>
)
}
Results:
- Small lists (8 items): Instant rendering
- Medium lists (70 regions): Smooth scrolling
- Large lists (300+ streets): Excellent mobile performance
- Stress test (500+ items): Maintains smooth operation
Why this works: AI chose native browser capabilities over complex virtualization because our documentation specified performance requirements.
Storybook: The Visual Verification Layer
Every component gets comprehensive Storybook documentation:
// Real Storybook story from our implementation
export const RegionsList: Story = {
args: {
placeholder: "Select region...",
children: russianRegions.map((region) => (
<SelectItem key={region} value={region}>
{region}
</SelectItem>
)),
},
parameters: {
viewport: {
defaultViewport: 'mobile1',
},
},
}
export const LongStreetsList: Story = {
args: {
placeholder: "Select street...",
children: streetNames.map((street, index) => (
<SelectItem key={index} value={street}>
{street}
</SelectItem>
)),
},
}
Purpose: Visual verification against Figma designs catches discrepancies immediately, eliminating costly revision cycles.
Team Scalability: Junior → Senior Results
The systematic approach transforms team productivity:
Before:
- Junior developers struggle with design implementation
- Component quality varies across team members
- Design-dev handoff requires constant clarification
- Technical debt from poorly understood AI code
After:
- Any developer implements complex components consistently
- Design system compliance becomes automatic
- Clear documentation eliminates guesswork
- Quality gates prevent technical debt
Real impact: Our junior developer implemented the entire address system using this workflow—components that would typically require senior-level expertise.
Tools Evolution: Future-Proof Methodology
Today’s stack:
- Figma MCP for extraction
- RooCode for AI assistance
- Storybook for verification
- shadcn/ui for design foundation
Tomorrow’s tools: Different AI models, enhanced platforms, evolved frameworks
The constant: Systematic preparation, structured documentation, and human oversight will always be essential.
Key insight: Master the principles, not just the tools. Teams focused on systematic approaches adapt quickly to new technologies.
Implementation Guide: Start Today
Phase 1: Foundation (30 minutes)
# Install shadcn/ui and Figma kit
npx create-next-app@latest my-project
npx shadcn-ui@latest init
npx shadcn-ui@latest add button select input
# Set up design tokens in globals.css
# Configure Figma variables as CSS custom properties
Phase 2: Documentation Templates (1 hour)
Create systematic extraction processes:
- Component specification templates
- TypeScript interface standards
- Design token mapping procedures
- Integration requirement checklists
Phase 3: Verification Workflows (30 minutes)
- Set up Storybook for visual validation
- Create triple-gate verification checklists
- Establish AI tool configuration with context
- Train team on systematic processes
Economic Reality: Cost vs. Value
Traditional approach costs:
- 2-3 weeks developer time ($8,000-12,000)
- Multiple design revision cycles ($2,000-4,000)
- Technical debt maintenance (ongoing)
- Inconsistent implementations (team efficiency loss)
Systematic approach investment:
- Initial setup: 2-3 hours ($200-400)
- Per component: 30 minutes ($50-100)
- Ongoing maintenance: Minimal (well-understood code)
- Team scalability: Immediate
ROI: 1000%+ in first quarter for most teams.
Real Project Results
Our election information system components:
Address Input System
- 4-level hierarchical selection
- Auto-selection logic
- Real backend integration
- Mobile-optimized performance
Select Component
- 300+ item support
- Multiple states and variants
- Touch-friendly mobile design
- Zero virtualization complexity
Button System
- 3 variants, 4 states
- Proper loading indicators
- Icon integration
- Accessibility compliance
Form Integration
- Complete TypeScript types
- Zustand store integration
- Error handling
- Validation patterns
Deployment: Same day from Figma to production
Maintenance: Zero issues after 3 months
Team adoption: Immediate productivity gains
Key Takeaways
- Designer component thinking is non-negotiable for AI success
- shadcn/ui + Figma kit eliminates design-code translation
- Figma MCP enables systematic documentation extraction
- AI doesn’t need to be smart—it needs complete context
- Triple-gate verification prevents technical debt accumulation
- Shared vocabulary between design and code enables seamless integration
- Tools will change, but systematic approaches remain essential
What’s Next?
This systematic approach scales beyond single components:
- Multi-platform generation (React Native, Flutter)
- Automated accessibility testing
- Performance optimization
- Design token synchronization
- Team collaboration frameworks
The methodology adapts and improves as teams build systematic habits.
Try It Yourself
Start small with one component:
- Set up shadcn/ui foundation
- Train designer on component thinking
- Configure Figma MCP extraction
- Extract complete specifications from Figma
- Provide full context to AI tools
- Follow triple-gate verification
- Document what works for your context
Remember: This isn’t about replacing human expertise—it’s about focusing human attention on decisions that matter while automating the mechanical implementation work.
The systematic approach transforms AI assistance from expensive experimentation into predictable, professional development.
This content originally appeared on DEV Community and was authored by opcheese