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Figma Design Handoff

Community
yonatangross
figma-design-handoff

Figma-to-code design handoff patterns including Figma Variables to design tokens pipeline, component spec extraction, Dev Mode inspection, Auto Layout to CSS Flexbox/Grid mapping, and visual regression with Applitools. Use when converting Figma designs to code, documenting component specs, setting up design-dev workflows, or comparing production UI against Figma designs.

Overview

Publisheryonatangross
Repositoryorchestkit
Skill namefigma-design-handoff
Stars
298
Forks
35
Bundled files
11
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 11 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by yonatangross on GitHub. Read the source before you install it.

Installation

Install the Figma Design Handoff AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/yonatangross/orchestkit/tree/main/plugins/ork/skills/figma-design-handoff
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/yonatangross/orchestkit.git /tmp/orchestkit
mkdir -p .claude/skills
cp -r /tmp/orchestkit/plugins/ork/skills/figma-design-handoff .claude/skills/figma-design-handoff
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Figma Design Handoff in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Figma Design Handoff on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Figma Design Handoff is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Figma Design Handoff

Figma dominates design tooling in 2026, with the majority of product teams using it as their primary design tool. A structured handoff workflow eliminates design drift — the gap between what designers create and what developers build. This skill covers the full pipeline: Figma Variables to design tokens, component spec extraction, Dev Mode inspection, Auto Layout to CSS mapping, and visual regression testing.

Quick Reference

RuleFileImpactWhen to Use
Figma Variables & Tokensrules/figma-variables-tokens.mdCRITICALConverting Figma Variables to W3C design tokens JSON
Component Specsrules/figma-component-specs.mdHIGHExtracting component props, variants, states from Figma
Dev Mode Inspectionrules/figma-dev-mode.mdHIGHMeasurements, spacing, typography, asset export
Auto Layout → CSSrules/figma-auto-layout.mdHIGHMapping Auto Layout to Flexbox/Grid
Visual Regressionrules/figma-visual-regression.mdMEDIUMComparing production UI against Figma designs

Total: 5 rules across 1 category

Figma Dev Mode MCP Server (2026 default path)

The Figma Dev Mode MCP Server replaces most manual REST + Dev Mode inspection. Configure it once and any Claude Code session with Figma access can pull design context, tokens, and code mappings directly.

Key tools (16 documented — developers.figma.com/docs/figma-mcp-server/tools-and-prompts):

ToolReturnsUse for
get_design_contextComponent tree + layout + typography + spacingFirst call on any Figma node → grounds every other tool
get_variable_defsToken collections (variables + modes + aliases)Straight export to W3C DTCG JSON — skip the REST pipeline
get_code_connect_mapFigma node → codebase component mappingLinking generated code to existing repo components
get_screenshotPNG of a node or frameScreenshot for visual diffing / embed in chat
search_design_systemToken/component search across librariesFinding existing tokens before generating new ones
use_figma (beta)Writes back to the Figma canvasCode-to-design round-trip (write-to-canvas)
generate_figma_design (beta)Creates a design frame from a promptAI-generated design stubs for handoff

Install: Enable in the Figma desktop app under Preferences → Dev Mode MCP Server, or use the remote MCP endpoint (no desktop required for read tools). Pair with Code Connect UI (GA 2026) to map Figma node IDs → codebase components without manual JSON wiring.

jsonc
// .claude/mcp-servers/figma.json — sample config
{
  "figma": {
    "command": "figma-mcp",
    "args": ["--mode=dev"],
    "env": { "FIGMA_ACCESS_TOKEN": "${FIGMA_TOKEN}" }
  }
}

Quick Start

bash
# 1. Export Figma Variables (Figma REST API)
curl -s -H "X-Figma-Token: $FIGMA_TOKEN" \
  "https://api.figma.com/v1/files/$FILE_KEY/variables/local" \
  > tokens/figma-local.json

# 2. Convert that response to W3C Design Tokens. This skill does NOT ship a
#    converter — the mapping (variables, modes, aliases, and the $value/$type
#    shape) is specified in rules/figma-variables-tokens.md, and the right
#    implementation depends on which collections and modes your file uses.
#    Earlier revisions of this Quick Start piped into a
#    `scripts/figma-to-w3c-tokens.js` that has never existed in this skill.
#    Result: tokens/figma-raw.json

# 3. Transform with Style Dictionary
npx style-dictionary build --config sd.config.js

# 4. Output: CSS custom properties + Tailwind theme
# tokens/
#   figma-raw.json        ← W3C Design Tokens format
#   css/variables.css     ← --color-primary: oklch(0.65 0.15 250);
#   tailwind/theme.js     ← module.exports = { colors: { primary: ... } }
json
// W3C Design Tokens Format (DTCG)
{
  "color": {
    "primary": {
      "$type": "color",
      "$value": "{color.blue.600}",
      "$description": "Primary brand color"
    },
    "surface": {
      "$type": "color",
      "$value": "{color.neutral.50}",
      "$extensions": {
        "mode": {
          "dark": "{color.neutral.900}"
        }
      }
    }
  }
}
typescript
// Style Dictionary config for Figma Variables
import StyleDictionary from 'style-dictionary';

export default {
  source: ['tokens/figma-raw.json'],
  platforms: {
    css: {
      transformGroup: 'css',
      buildPath: 'tokens/css/',
      files: [{ destination: 'variables.css', format: 'css/variables' }],
    },
    tailwind: {
      transformGroup: 'js',
      buildPath: 'tokens/tailwind/',
      files: [{ destination: 'theme.js', format: 'javascript/module' }],
    },
  },
};

Handoff Workflow

The design-to-code pipeline follows five stages:

  1. Design in Figma — Designer creates components with Variables, Auto Layout, and proper naming
  2. Extract Specs — Use Dev Mode to inspect spacing, typography, colors, and export assets
  3. Export Tokens — Figma Variables → W3C tokens JSON via REST API or plugin
  4. Build Components — Map Auto Layout to CSS Flexbox/Grid, apply tokens, implement variants
  5. Visual QA — Compare production screenshots against Figma frames with Applitools
┌─────────────┐     ┌──────────────┐     ┌───────────────┐
│  Figma File  │────▶│  Dev Mode    │────▶│  tokens.json  │
│  (Variables, │     │  (Inspect,   │     │  (W3C DTCG    │
│  Auto Layout)│     │   Export)    │     │   format)     │
└─────────────┘     └──────────────┘     └───────┬───────┘
                                                  │
                                                  ▼
┌─────────────┐     ┌──────────────┐     ┌───────────────┐
│  Visual QA  │◀────│  Components  │◀────│ Style         │
│  (Applitools,│     │  (React +   │     │ Dictionary    │
│   Chromatic) │     │  Tailwind)   │     │ (CSS/Tailwind)│
└─────────────┘     └──────────────┘     └───────────────┘

Rules

Each rule is loaded on-demand from the rules/ directory:

Auto Layout to CSS Mapping

Quick reference for the most common mappings:

Figma Auto LayoutCSS EquivalentTailwind Class
Direction: Horizontalflex-direction: rowflex-row
Direction: Verticalflex-direction: columnflex-col
Gap: 16gap: 16pxgap-4
Padding: 16padding: 16pxp-4
Padding: 16, 24padding: 16px 24pxpy-4 px-6
Align: Centeralign-items: centeritems-center
Justify: Space betweenjustify-content: space-betweenjustify-between
Fill containerflex: 1 1 0%flex-1
Hug contentswidth: fit-contentw-fit
Fixed width: 200width: 200pxw-[200px]
Min width: 100min-width: 100pxmin-w-[100px]
Max width: 400max-width: 400pxmax-w-[400px]
Wrapflex-wrap: wrapflex-wrap
Absolute positionposition: absoluteabsolute

Visual QA Loop

typescript
// Applitools Eyes + Figma Plugin — CI integration
import { Eyes, Target } from '@applitools/eyes-playwright';

const eyes = new Eyes();

await eyes.open(page, 'MyApp', 'Homepage — Figma Comparison');

// Capture full page
await eyes.check('Full Page', Target.window().fully());

// Capture specific component
await eyes.check(
  'Hero Section',
  Target.region('#hero').ignoreDisplacements()
);

await eyes.close();

The Applitools Figma Plugin overlays production screenshots on Figma frames to catch:

  • Color mismatches (token not applied or wrong mode)
  • Spacing drift (padding/margin deviations)
  • Typography inconsistencies (font size, weight, line height)
  • Missing states (hover, focus, disabled not implemented)

Key Decisions

DecisionRecommendation
Token formatW3C Design Tokens Community Group (DTCG) JSON
Token pipelineFigma REST API → Style Dictionary → CSS/Tailwind
Color formatOKLCH for perceptually uniform theming
Layout mappingAuto Layout → CSS Flexbox (Grid for 2D layouts)
Visual QA toolApplitools Eyes + Figma Plugin for design-dev diff
Spec formatTypeScript interfaces matching Figma component props
Mode handlingFigma Variable modes → CSS media queries / class toggles

Anti-Patterns (FORBIDDEN)

  • Hardcoded values: Never hardcode colors, spacing, or typography — always reference tokens
  • Skipping Dev Mode: Do not eyeball measurements — use Dev Mode for exact values
  • Manual token sync: Do not manually copy values from Figma — automate with REST API
  • Ignoring modes: Variables with light/dark modes must map to theme toggles, not separate files
  • Screenshot-only QA: Visual comparison without structured regression testing misses subtle drift
  • Flat token structure: Use nested W3C DTCG format, not flat key-value pairs

References

ResourceDescription
references/figma-to-code-workflow.mdEnd-to-end workflow, toolchain options
references/design-dev-communication.mdPR templates, component status tracking
references/applitools-figma-plugin.mdSetup, CI integration, comparison config

Related Skills

  • ork:design-system-tokens — W3C token architecture and Style Dictionary transforms
  • ork:ui-components — shadcn/ui and Radix component patterns
  • ork:accessibility — WCAG compliance for components extracted from Figma

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Figma Design Handoff AI skill do?

Figma-to-code design handoff patterns including Figma Variables to design tokens pipeline, component spec extraction, Dev Mode inspection, Auto Layout to CSS Flexbox/Grid mapping, and visual regression with Applitools. Use when converting Figma designs to code, documenting component specs, setting up design-dev workflows, or comparing production UI against Figma designs.

Why use Figma Design Handoff on TypingMind?

Because you install it once and use it with any model. Figma Design Handoff is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Figma Design Handoff in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/yonatangross/orchestkit/tree/main/plugins/ork/skills/figma-design-handoff. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Figma Design Handoff?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Figma Design Handoff?

As many as you like. As long as a model supports skills, you can use Figma Design Handoff with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Figma Design Handoff AI skill free?

Yes. It is published on GitHub by yonatangross under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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