Figma Bridge logo

Figma Bridge

Organization
staple-lab
figma-bridge

Use when connecting the design system to Figma - pulling Figma variables into the token layer, checking token drift between Figma and code, generating Code Connect mappings from the registry, or pushing the built system into Figma as a library. Triggers on "sync with Figma", "Figma variables", "import tokens from Figma", "Code Connect", "push to Figma", "design tool sync", a figma.com URL in a design-system context.

Overview

Publisherstaple-lab
Repositorydesign-system-skills
Skill namefigma-bridge
Stars
7
Forks
0
Bundled files
1
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.

  • 1 bundled files

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

  • Open source

    Published by staple-lab on GitHub. Read the source before you install it.

Installation

Install the Figma Bridge 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/staple-lab/design-system-skills/tree/main/plugins/design-system/skills/figma-bridge
  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/staple-lab/design-system-skills.git /tmp/design-system-skills
mkdir -p .claude/skills
cp -r /tmp/design-system-skills/plugins/design-system/skills/figma-bridge .claude/skills/figma-bridge
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Figma Bridge 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 Bridge 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 Bridge 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.

The Figma bridge

Three flows, in descending order of payoff-per-effort: pull tokens (Figma variables → DTCG), connect components (registry → Code Connect), push a library (code → Figma, one-way bootstrap). Each works better with the Figma MCP server connected, and each has a file-based fallback that works with none.

The degradation contract — check this first

Probe for the Figma MCP tools (any mcp__*figma* / claude.ai Figma tool in the session).

  • Connected → use the tools: get_variable_defs for pulls, the Code Connect tool family (list_file_components_for_code_connect, get_code_connect_suggestions, send_code_connect_mappings) for mappings, generate_figma_design for pushes.
  • Not connected → say exactly that, and how to fix it: connect the Figma server in the client's MCP settings (claude.ai's Figma connector, or Figma's desktop MCP server), or proceed file-based: any Figma variables JSON export — REST GET /v1/files/:key/variables/local, a variables-export plugin, Tokens Studio — flows through ${CLAUDE_PLUGIN_ROOT}/templates/figma/variables-to-dtcg.mjs with no MCP at all. Never silently skip the Figma work because the tools are absent.

Flow 1 — pull tokens from Figma variables

bash
node figma/variables-to-dtcg.mjs variables.json            # write DTCG files
node figma/variables-to-dtcg.mjs variables.json --diff     # report drift, write nothing
node tokens/build.mjs                                      # the gate arbitrates the pull

The converter (copy from ${CLAUDE_PLUGIN_ROOT}/templates/figma/) maps single-mode collections → primitive.tokens.json, multi-mode collections → one semantic.<mode>.tokens.json per mode, aliases → {dot.path} references. Full mapping rules and what does not round-trip: references/variable-mapping.md.

The judgement, which the script cannot make:

  • Decide the direction of truth per tier and write it in the config's rationale. The workable contract is: Figma owns primitive values (designers tune ramps in the tool they live in), code owns semantic structure and everything Figma has no concept of (density, brands, component knobs, motion). A pull that rewrites your semantic layer because a designer renamed a group is not sync, it is a hostile takeover — use --diff and cherry-pick.
  • The contrast gate arbitrates every pull. A designer nudging accent lightness in Figma can silently kill button-label contrast in code; the gate failing is the bridge working. Report the failure back to the design side with the measured ratio — that number is the argument.
  • --diff is the ongoing-sync story. There is no watch mode and no webhook in this bridge on purpose: a scheduled --diff (CI cron, exit 1 on drift) that opens a small PR beats live sync, because every change lands reviewed and gated. Live token sync ships a designer's work-in-progress to production CSS.

Flow 2 — Code Connect from the registry

Code Connect is what makes Figma's Dev Mode show your component's real usage instead of auto-generated div soup. The registry already holds every component's props, variants and defaults extracted from the real types — so mappings are a view over registry.json, never hand-authored per component (hand-authored mappings are the docs-drift problem wearing a new hat).

  • MCP path (preferred — zero user-project dependencies): list the file's components (list_file_components_for_code_connect), match to registry components by name (take get_code_connect_suggestions as hints, not truth), send with send_code_connect_mappings. Confirm the match list with the user before sending — a wrong mapping shows false code in every dev's inspect panel.
  • CLI path (fallback, works headless): node figma/registry-to-codeconnect.mjs --map figma/node-map.json emits <Name>.figma.tsx files for npx figma connect publish (@figma/code-connect is a user-project dev dependency — never a template one). Without --map the files carry FIXME URLs: they parse, they document the prop mapping, they will not publish. Enum props emit as figma.enum, booleans as figma.boolean — a starting point to adjust, since Figma property names rarely match code prop names exactly.

Regenerate after registry changes; mappings drift exactly as fast as docs otherwise.

Flow 3 — push the system into Figma (bootstrap, not sync)

For teams whose code is ahead of their design file (common after /design-system:init): generate a Figma library from the built system — Figma's own figma-generate-library skill (served by the MCP server) is the executor; feed it tokens/dist/tokens.json for variables and the registry for the component list, wave 1 first.

Be honest about what this is: one-way, once. What survives the trip: variables, component names, variant grammar, basic anatomy. What does not: interaction states' exact rendering, motion, responsive behaviour, the escape hatches. After the bootstrap, the design file is a living artifact with its own history — pushing again overwrites designer work, so subsequent traffic flows the other way (flow 1 pulls, flow 2 keeps Dev Mode truthful). If both sides keep evolving independently, the bridge you need is process (a shared owner for the token layer), not more tooling.

Verification honesty

The fixture round-trip (variables fixture → DTCG → token build → contrast gate) is verified automatically by this repo's check script. The MCP flows are manually verified against a connected Figma account — they depend on a live design file and an authenticated server, so claim exactly that and no more when reporting status.

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 Bridge AI skill do?

Use when connecting the design system to Figma - pulling Figma variables into the token layer, checking token drift between Figma and code, generating Code Connect mappings from the registry, or pushing the built system into Figma as a library. Triggers on "sync with Figma", "Figma variables", "import tokens from Figma", "Code Connect", "push to Figma", "design tool sync", a figma.com URL in a design-system context.

Why use Figma Bridge on TypingMind?

Because you install it once and use it with any model. Figma Bridge 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 Bridge in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/staple-lab/design-system-skills/tree/main/plugins/design-system/skills/figma-bridge. 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 Bridge?

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 Bridge?

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

Is the Figma Bridge AI skill free?

Yes. It is published on GitHub by staple-lab 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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