Mobile Design To Code

Community

Xinvtech

mobile-design-to-code

Convert validated mobile product, UX, wireframe, design-system, UI, prototype, and platform contracts into an implementation-ready Flutter handoff by default, or another explicitly requested stack. Use when Codex needs route/state mapping, token and component bindings, accessibility semantics, platform variants, implementation tasks, runnable scaffolding, or code-readiness review.

Overview

Publisher	Xinvtech
Repository	`mobile-design-os`
Skill name	`mobile-design-to-code`
Stars	4
Forks	0
Bundled files	8
License	MIT
Links	Skill source Repository Homepage

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.
8 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by Xinvtech on GitHub. Read the source before you install it.

Installation

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

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/Xinvtech/mobile-design-os/tree/main/skills/mobile-design-to-code
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.

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/Xinvtech/mobile-design-os.git /tmp/mobile-design-os
mkdir -p .claude/skills
cp -r /tmp/mobile-design-os/skills/mobile-design-to-code .claude/skills/mobile-design-to-code

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Mobile Design To Code 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 Mobile Design To Code 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 Mobile Design To Code 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.

Mobile Design to Code

Preserve design intent as executable implementation contracts. Map meaning, states, behavior, and verification—not just screenshots or CSS-like coordinates.

Select the handoff mode

Plan: create a framework-specific implementation map and task breakdown before coding.
Scaffold: create a narrow runnable implementation for the declared platform and core flow.
Review: compare implementation against the design package, prototype tests, and platform contract.

Declare the target framework, OS versions, build entry point, persistence/network boundaries, and whether code changes are authorized. If the user has not selected a stack, use Flutter and record frameworkSelectionSource: project-default; switch to SwiftUI, UIKit, Jetpack Compose, React Native, or another stack only when the user explicitly asks for it, recording frameworkSelectionSource: user-specified.

Establish the input contract

Prefer the validated Design Package plus wireframe.json, design-system.json, prototype.json, and platform-guidelines.json when available. Preserve upstream IDs and provenance. Report missing contracts before writing code.

Build the mapping

Map each design token to a semantic code token and source file.
Map each component to a code component, props, state machine, accessibility semantics, and platform variant.
Map each Screen/State to a route/view, data dependencies, loading/error/recovery behavior, and acceptance signals.
Map gestures, motion, haptics, permissions, system surfaces, keyboard, lifecycle, and deep links to platform APIs or explicit fallbacks.
Define test cases for core flows, state continuity, large text, assistive technology, reduced motion, offline, duplicate submission, persistence, and destructive recovery.
Separate generated scaffolding from product decisions that require an engineer or designer.

For the project-default Flutter path, use references/flutter-implementation.md and the scaffold helper at scripts/scaffold_flutter_app.py. The helper creates a small runnable Flutter app from a valid handoff; it does not silently turn an untested prototype into production code.

Read references/handoff-rules.md and references/handoff-schema.md before creating structured output.

Produce and validate

Create:

code-handoff.md using assets/code-handoff-template.md.
code-handoff.json using assets/code-handoff-template.json.

Run python3 scripts/validate_code_handoff.py <path-to-code-handoff.json> and fix every error. A valid handoff is not proof that the app builds or that runtime behavior passes; keep implementation and device checks explicit.

When a Flutter scaffold is requested, run python3 scripts/scaffold_flutter_app.py --handoff <path-to-code-handoff.json> --output <new-project-directory>, then run flutter analyze and flutter test in the generated project. Keep those results separate from the static handoff evidence.

Enforce the output order

Stack (Flutter by default unless explicitly overridden), scope, source contracts, and assumptions.
Token/component bindings.
Route/screen/state mapping.
Platform and system API mapping.
Tests, build/run instructions, ownership, and open risks.

Do not produce a screenshot-only handoff, invent inaccessible semantics, or claim implementation readiness when state, platform, or test contracts are missing.

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 Mobile Design To Code AI skill do?

Why use Mobile Design To Code on TypingMind?

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

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

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 Mobile Design To Code?

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

Is the Mobile Design To Code AI skill free?

Yes. It is published on GitHub by Xinvtech 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Mobile Design to Code

Select the handoff mode

Establish the input contract

Build the mapping

Produce and validate

Enforce the output order

Bundled files

Frequently asked questions

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