Design To Code Handoff logo

Design To Code Handoff

Organization
memi-design
design-to-code-handoff

Carry Figma or Paper design evidence into an existing web codebase, design system, Storybook, and browser verification. Use when an agent must implement a supplied design without losing source context or inventing components.

Overview

Publishermemi-design
Repositorydesign-skills
Skill namedesign-to-code-handoff
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 memi-design on GitHub. Read the source before you install it.

Installation

Install the Design To Code 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/memi-design/design-skills/tree/main/skills/design-to-code-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/memi-design/design-skills.git /tmp/design-skills
mkdir -p .claude/skills
cp -r /tmp/design-skills/skills/design-to-code-handoff .claude/skills/design-to-code-handoff
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Design To Code 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 Design To Code 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 Design To Code 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.

Design-to-code handoff

When to use

Use this workflow for a bounded screen or component when a Figma selection, Paper canvas, or exported design must become code in an existing web product. Use component-catalog to plan a library without a supplied design and token-architecture for a cross-surface token change. A link alone is not a specification: the codebase is the implementation authority and the design is evidence for the intended result.

1. Capture the source

Record the exact Figma file and node, Paper canvas and selection, export hash, or screenshot URL; include the source revision or source timestamp. Capture dimensions, variants, interaction states, responsive constraints, text, assets, and linked tokens. Establish acceptance criteria for the requested screen and states with the user or task before coding. If access is unavailable, use supplied exports and mark missing states as unresolved. Do not infer measurements or behavior from a static screenshot.

2. Map to the existing system

Read local instructions, tokens, components, Storybook stories, routes, and test conventions before editing. Make a short mapping table for each significant element:

Design element and sourceExisting component/tokenDecisionConfidence and open difference

Mark a decision as reuse, extend, or new. Reuse only when semantics, behavior, and supported states fit; a visual resemblance alone is insufficient. Before extending or adding, check the component API, callers, stories, and accessibility contract. If the design conflicts with an existing token or component, record the conflict and resolve it against the system owner or task requirements; do not quietly fork the system. Give confidence as high, medium, or low and name the supporting evidence.

3. Implement a thin slice

Start with the smallest representative state and use the repository's component API, token names, accessibility conventions, and test setup. Add or update Storybook stories for applicable default, interactive, empty, loading, error, and disabled states; omit inapplicable states explicitly. If the project has no Storybook, use its existing component preview or test fixture and record that substitution. Keep source assets traceable and check their use rights. Stop and surface a decision when interaction, asset rights, or responsive behavior is unknown and would change the implementation contract.

4. Verify the result

Run relevant unit and interaction tests, then inspect the rendered page in a browser at the design viewport and at a narrow viewport. Compare layout, type, color, spacing, assets, focus, keyboard behavior, and responsive behavior. Exercise each applicable story or preview state, not just the default screen. Use screenshot comparison only with repeatable viewport, browser, font, and data settings. Fix material differences against the acceptance criteria; list any unresolved difference with its reason and owner. A passing build or single screenshot does not prove visual or interaction parity.

Handoff receipt

Return the source links or export hashes, design timestamp, component/token mapping, files and stories changed, commands and browser viewports checked, links or paths to visual evidence, confidence for ambiguous mappings, and unresolved differences. State explicitly when the design source, Storybook, or browser was unavailable. Route Figma-specific extraction through figma-implement-design; use memoire-design-tooling when a Memi installation is available for the local brief and audit.

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 Design To Code Handoff AI skill do?

Carry Figma or Paper design evidence into an existing web codebase, design system, Storybook, and browser verification. Use when an agent must implement a supplied design without losing source context or inventing components.

Why use Design To Code Handoff on TypingMind?

Because you install it once and use it with any model. Design To Code 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 Design To Code Handoff in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/memi-design/design-skills/tree/main/skills/design-to-code-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 Design To Code 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 Design To Code Handoff?

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

Is the Design To Code Handoff AI skill free?

Yes. It is published on GitHub by memi-design 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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