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Figma Generative Plugins

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figma
figma-generative-plugins

**MANDATORY prerequisite** — load this skill before calling `create_generative_plugin` or `update_generative_plugin`. Use when the user asks to create, author, change, fix, or extend a reusable generative Figma plugin.

Overview

Publisherfigma
Repositorymcp-server-guide
Skill namefigma-generative-plugins
Stars
2K
Forks
194
Bundled files
1
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 figma on GitHub. Read the source before you install it.

Installation

Install the Figma Generative Plugins 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/figma/mcp-server-guide/tree/main/skills-figquery/figma-generative-plugins
  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/figma/mcp-server-guide.git /tmp/mcp-server-guide
mkdir -p .claude/skills
cp -r /tmp/mcp-server-guide/skills-figquery/figma-generative-plugins .claude/skills/figma-generative-plugins
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Figma Generative Plugins 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 Generative Plugins 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 Generative Plugins 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.

Create and update generative Figma plugins

Load this skill before every create_generative_plugin or update_generative_plugin call. In user-facing language, call the result a “plugin.” The “generative” qualifier only distinguishes this account-library tool from other plugin systems.

Preflight

Before authoring, confirm the request fits this surface:

  • Plugins run in the Figma Design editor.
  • Every plugin must provide functional UI for its core workflow. A plugin with no configurable inputs still needs a clear primary action and useful status or validation feedback.
  • Never embed API keys, OAuth tokens, signed URLs, or other secrets. Plugin source is readable by people who can access it. For authenticated integrations, stop and offer a static dataset, a public no-auth endpoint, or a different architecture.

Create workflow

  1. Resolve the requested workflow and its useful controls. Ask a concise question only if required inputs or behavior are genuinely ambiguous.
  2. Resolve planKey. Reuse one supplied by the user; otherwise call whoami. Use the sole eligible plan automatically, or ask the user to choose when several materially different plans are available.
  3. Call create_generative_plugin once with a concise name, description, and planKey. This creates a runnable square-drawing scaffold, not the requested final plugin.
  4. Call get_generative_plugin with the returned id, then read every source URI. This establishes the scaffold's manifest, UI message contract, and current TypeScript entrypoint.
  5. Replace the scaffold with complete authored files for the requested behavior and UI.
  6. Call update_generative_plugin with the returned id, a files array containing complete replacements for code.ts and, when the UI changes, ui.html, plus a specific commitMessage. Use metadata when changing the name or description.

Never stop after create_generative_plugin: the starter must be replaced with the requested experience.

Update workflow

  1. Identify the plugin. If needed, call list_generative_plugins, then get_generative_plugin.
  2. Read every source URI returned by the get tool before editing. Treat those files as the current source of truth.
  3. Preserve existing UI, controls, relaunch behavior, validation, and user-visible affordances unless the user asks to change them.
  4. Call update_generative_plugin with complete replacement content for every changed existing file. Use { path: "code.ts", content: "..." } for the entrypoint and { path: "ui.html", content: "..." } for the UI. Unspecified files are preserved.

Authoring rules

  • Before writing replacement files, read Plugin source authoring. It covers the file replacement contract, UI/message lifecycle, PropsKit controls, dynamic-page compatibility, fonts, relaunch behavior, bounded work, geometry, and Plugin API gotchas.
  • Keep the plugin’s primary action obvious and make invalid selection or input states understandable.
  • update_generative_plugin can replace existing code.ts and ui.html files, but cannot replace manifest.json or create new files. Keep figma.showUI(__html__, ...) in code.ts and replace ui.html when the requested workflow needs different controls.
  • Avoid destructive canvas changes unless they are the explicit purpose of the plugin and the UI makes that clear.
  • Do not close before asynchronous work and UI messages have completed.
  • Treat a non-error update_generative_plugin result as success. Record the returned version when present; a successful response may omit it.
  • On a build error, use the returned compiler output to make the smallest source correction and retry once. If it still fails, surface the error instead of repeatedly rewriting the plugin.

Completion

Report the plugin name and id, plus the returned version when present and a short description of its UI and primary action. Construct and include a clickable URL that opens a new Design file with the unpublished plugin ready to try, using the exact plugin id as try-tool-resource-content-id:

https://www.figma.com/file/new?try-tool-resource-content-id=<id>&try-tool-resource-type=gen_tool&type=design&mode=design

After presenting the new-file link, ask whether the user wants to open the plugin in an existing Figma Design file instead. If yes, reuse a file URL already provided or ask for one, then add the same try-tool-resource-content-id and try-tool-resource-type query parameters to that URL. Never guess the file URL.

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 Generative Plugins AI skill do?

**MANDATORY prerequisite** — load this skill before calling `create_generative_plugin` or `update_generative_plugin`. Use when the user asks to create, author, change, fix, or extend a reusable generative Figma plugin.

Why use Figma Generative Plugins on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/figma/mcp-server-guide/tree/main/skills-figquery/figma-generative-plugins. 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 Generative Plugins?

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 Generative Plugins?

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

Is the Figma Generative Plugins AI skill free?

It is published on GitHub by figma. Check the repository for licensing terms. 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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