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Figma Design Tool Workflows

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phazurlabs
figma-design-tool-workflows

Figma mastery and design-to-code: Auto Layout, component architecture, variable modes, Dev Mode handoff, Figma MCP server integration, Code Connect, and token pipelines. Use when working in Figma, converting designs to code, or setting up a design-to-code flywheel.

Overview

Publisherphazurlabs
Repositorysumi
Skill namefigma-design-tool-workflows
Stars
58
Forks
11
Bundled files
3
LicenseApache-2.0
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.

  • 3 bundled files

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

  • Open source

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

Installation

Install the Figma Design Tool Workflows 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/phazurlabs/sumi/tree/main/skills/figma-design-tool-workflows
  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/phazurlabs/sumi.git /tmp/sumi
mkdir -p .claude/skills
cp -r /tmp/sumi/skills/figma-design-tool-workflows .claude/skills/figma-design-tool-workflows
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Figma Design Tool Workflows 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 Tool Workflows 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 Tool Workflows 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 Tool Workflows — From Canvas to Production Code

Figma as the Design System of Record

Figma is not a drawing tool. It is the single source of truth for design intent, component architecture, token definitions, interaction specifications, and handoff documentation. When used properly, a Figma file becomes a living specification that machines can read, humans can interpret, and CI/CD pipelines can validate against.

The shift in thinking is fundamental: every layer name, every component property, every variable definition in Figma is data. That data flows downstream into code generation, token pipelines, accessibility audits, and visual regression testing. A sloppy Figma file produces sloppy code. A well-architected Figma file produces components that are correct on the first pass.

This skill covers the full Figma workflow from component architecture through AI-assisted code generation and automated validation.

Component Architecture in Figma

Variants and Properties

Figma components support four property types that map directly to code component APIs:

  • Boolean properties control visibility of child layers. A button's showIcon boolean maps to a React showIcon?: boolean prop. The icon layer is present or absent based on this toggle, which means the generated code conditionally renders the icon.
  • Text properties expose editable text content. A button's label text property maps to a children: string or label: string prop. The designer controls the default, the developer overrides at the call site.
  • Instance swap properties define composition slots. A button's icon instance swap maps to an icon?: ReactNode prop. The slot accepts any icon component from the icon library, and the generated code renders the passed component.
  • Preferred values constrain instance swap choices to a curated set. Rather than showing every component in the file, preferred values limit the swap picker to relevant options (icon library components only, for example).

Variant Architecture

Use variants when a component has visually distinct states that change multiple properties simultaneously. A Button component might have variants across two axes: variant (primary, secondary, ghost, danger) and size (small, medium, large). Each combination is a distinct visual design within the same component set.

Use component properties (booleans, text, instance swaps) when a single property toggles independently. Whether a button shows an icon is independent of its variant and size. Making this a boolean property rather than doubling the variant count keeps the component manageable.

Use separate components when two patterns share no structural DNA. A Button and a Card are separate components, not variants of each other.

Nested Components and Slot Patterns

Production-grade Figma components use nested sub-components to create composition points. A Card component contains a CardHeader sub-component, a CardBody sub-component, and a CardFooter sub-component. Each sub-component is a separate Figma component that the Card references via instance swap slots. This mirrors the compound component pattern in React (<Card><Card.Header /><Card.Body /><Card.Footer /></Card>).

Naming Conventions

Follow a Category/Type/State naming hierarchy:

  • Button/Primary/Default
  • Button/Primary/Hover
  • Button/Secondary/Disabled
  • Input/Text/Default
  • Input/Text/Error
  • Card/Elevated/Default

This naming convention creates automatic grouping in the Figma assets panel and maps cleanly to code organization: the Button directory contains Primary and Secondary variant files, each handling their own states.

Variable Modes

Figma variables replace static styles with dynamic, mode-switchable tokens. Variables organize into collections with multiple modes.

Token Layer Architecture

Structure variable collections in three tiers:

  1. Primitive collection — raw values: blue-500: #3b82f6, spacing-4: 16, radius-md: 8. A single mode; these values never change.
  2. Semantic collection — contextual meaning with modes for light/dark/high-contrast: color-bg-primary resolves to white in light mode and neutral-900 in dark mode. color-text-primary resolves to neutral-900 in light mode and neutral-50 in dark mode.
  3. Component collection — component-specific bindings: button-primary-bg references color-action-primary, which references blue-500 in the primitive layer. Modes here handle density (compact/default/comfortable) or brand switching (brand-a/brand-b).

Mode Switching

Variable modes enable designers to preview every combination without duplicating frames. A single button component, bound to semantic variables, previews as light primary, dark primary, high-contrast primary, light compact, dark comfortable, and every other permutation — all by switching the mode on the enclosing frame.

This is the design equivalent of CSS custom property theming. The structure must match: Figma variable names should correspond directly to CSS custom property names or token identifiers in the codebase.

Auto Layout

Auto Layout is the mechanism that makes Figma components developer-friendly. A component without Auto Layout is a static drawing. A component with proper Auto Layout is a responsive specification that communicates padding, spacing, alignment, and resize behavior to developers.

Key principles:

  • Every component uses Auto Layout. No exceptions. If a component cannot be built with Auto Layout, the design needs to be reconsidered.
  • Padding and spacing use variables. Bind Auto Layout padding and item spacing to spacing variables so that density modes work automatically.
  • Fill container for responsive elements. Text blocks and content areas use "Fill container" width so they stretch to available space, matching CSS flex: 1 behavior.
  • Hug contents for intrinsic sizing. Icons, badges, and labels use "Hug contents" so they size to their content, matching CSS width: auto behavior.
  • Absolute positioning sparingly. Badges positioned on avatar corners and floating action buttons use absolute positioning within Auto Layout. This maps to CSS position: absolute within a position: relative container.

Dev Mode

Figma Dev Mode is the bridge between design and development. When a developer inspects a component in Dev Mode, they see:

  • Component properties mapped to code props
  • CSS values for every selected layer (spacing, colors, typography)
  • Variable references showing the token name rather than the raw value
  • Annotations added by designers explaining behavior, interaction rules, and edge cases
  • Code snippets generated from component configuration
  • Measurements between elements (spacing, alignment guides)

Dev Mode effectiveness depends entirely on how well the designer structured the component. Proper naming, variable binding, and Auto Layout produce clean Dev Mode output. Poor structure produces noise.

Figma MCP — The AI-Assisted Game Changer

The Figma Model Context Protocol (MCP) server allows AI assistants like Claude to read Figma design files programmatically. This is the single most impactful development in design-to-code workflow since component libraries.

With Figma MCP, Claude can:

  • Read the complete structure of a design file (pages, frames, components)
  • Extract component properties, variants, and their values
  • Read variable definitions and mode configurations
  • Understand Auto Layout specifications (padding, spacing, alignment, sizing)
  • Extract color, typography, and effect styles
  • Read prototype flows and interaction definitions

This means a designer can build a component in Figma, and Claude can read that component's specification, generate production-quality code (React, SwiftUI, CSS), and produce accompanying tests — all without a human translating the design spec into a ticket.

The Design-to-Code Flywheel

The flywheel operates in six steps:

  1. Design — Designer builds a component in Figma with proper architecture: Auto Layout, variables, component properties, correct naming.
  2. Read — Claude reads the component specification via Figma MCP: layout structure, token bindings, property API, responsive behavior.
  3. Generate — Claude produces code: React component with TypeScript types, CSS Module with token references, Storybook story with all variants, unit test covering props and accessibility.
  4. Validate — Visual regression testing (Chromatic, Percy) compares the rendered component against the Figma source. Accessibility testing (axe-core) validates WCAG compliance.
  5. Iterate — Designer updates the component in Figma. The MCP read detects changes. Claude generates an incremental code update. Visual regression catches any drift.
  6. Ship — CI/CD pipeline deploys validated components. Token pipeline syncs updated variables. Consuming applications pull the latest version.

Each cycle gets faster. The first pass through the flywheel for a new component might save 50% of development time compared to manual implementation. Subsequent iterations on existing components save 70-80% because the structure, tests, and stories already exist and only need updating.

Routing

For working in Figma — Auto Layout in depth, component architecture, variable modes, prototyping, Dev Mode and handoff, library management, naming and file organization, the plugin essentials, and the shortcuts that actually save time: read references/figma-mastery-workflows.md.

For getting Figma into production code — pipeline architecture, Style Dictionary configuration, token transformation examples, CI/CD integration, visual regression testing, component naming conventions, Storybook integration, multi-brand and white-label architecture, and accessibility in the pipeline: read references/design-to-code-pipeline.md.

For the MCP flywheel specifically: read references/figma-mcp-ai-flywheel.md.

Cross-References

  • Component code output and patterns — reference component-patterns-code for React, SwiftUI, and CSS component architecture, compound components, render props, and polymorphic patterns that Figma MCP-generated code should follow.
  • Design system governance — reference design-systems-architecture for token hierarchy (primitive, semantic, component), versioning strategy, contribution models, and change management processes that govern the Figma library.
  • Visual design tokens — reference ui-visual-design-system for color theory, typography scale, spacing system, and elevation definitions that underpin Figma variable collections.
  • Accessibility in Figma — reference accessibility-inclusive-design for Figma accessibility plugins (Stark, A11y Annotation Kit), color contrast checking, focus order annotation, and screen reader text specification within design files.
  • Interaction and motion — reference interaction-motion-design for Figma prototyping best practices, smart animate specifications, and motion token definitions that map to CSS transitions and animations.

Key Sources

  • Figma official documentation: Components, Variables, Auto Layout, Dev Mode, Code Connect
  • Figma MCP server documentation and API reference
  • Style Dictionary documentation for multi-platform token transformation
  • Tokens Studio (Figma plugin) documentation for token management
  • Chromatic documentation for visual regression testing with Storybook
  • W3C Design Tokens Community Group specification for token format

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 Tool Workflows AI skill do?

Figma mastery and design-to-code: Auto Layout, component architecture, variable modes, Dev Mode handoff, Figma MCP server integration, Code Connect, and token pipelines. Use when working in Figma, converting designs to code, or setting up a design-to-code flywheel.

Why use Figma Design Tool Workflows on TypingMind?

Because you install it once and use it with any model. Figma Design Tool Workflows 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 Tool Workflows in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/phazurlabs/sumi/tree/main/skills/figma-design-tool-workflows. 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 Tool Workflows?

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 Tool Workflows?

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

Is the Figma Design Tool Workflows AI skill free?

Yes. It is published on GitHub by phazurlabs under the Apache-2.0 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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