Uno Platform logo

Uno Platform

Organization
managedcode
uno-platform

Build cross-platform .NET applications with Uno Platform targeting WebAssembly, iOS, Android, macOS, Linux, and Windows from a single XAML/C# codebase. USE FOR: building cross-platform apps from a single C# and XAML codebase; targeting WebAssembly, iOS, Android, macOS, Linux, and Windows simultaneously; migrating WPF or UWP. DO NOT USE FOR: unrelated stacks; generic tasks that do not need this specific guidance. INVOKES: inspect the repository context, edit targeted files, and run relevant build, test, lint, or validation commands when changes are made.

Overview

Publishermanagedcode
Repositorydotnet-skills
Skill nameuno-platform
Stars
492
Forks
38
Bundled files
2
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.

  • 2 bundled files

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

  • Open source

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

Installation

Install the Uno Platform 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/managedcode/dotnet-skills/tree/main/catalog/Frameworks/Uno-Platform/skills/uno-platform
  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/managedcode/dotnet-skills.git /tmp/dotnet-skills
mkdir -p .claude/skills
cp -r /tmp/dotnet-skills/catalog/Frameworks/Uno-Platform/skills/uno-platform .claude/skills/uno-platform
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Uno Platform 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 Uno Platform 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 Uno Platform 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.

Uno Platform

Trigger On

  • building cross-platform apps from a single C# and XAML codebase
  • targeting WebAssembly, iOS, Android, macOS, Linux, and Windows simultaneously
  • migrating WPF or UWP applications to cross-platform
  • implementing pixel-perfect UI across all platforms
  • using WinUI/UWP APIs on non-Windows platforms

Documentation

References

See detailed examples in the references/ folder:

  • patterns.md — MVUX, XAML, navigation, and performance patterns

Platform Support

PlatformRenderingNotes
WindowsWinUI 3Native Windows App SDK
WebAssemblySkia/CanvasRuns in browser
iOSSkia/MetalNative iOS app
AndroidSkia/OpenGLNative Android app
macOSSkia/MetalMac Catalyst or AppKit
LinuxSkia/X11GTK or Framebuffer

Workflow

  1. Choose the right template — Uno Platform offers various templates for different scenarios
  2. Understand rendering modes — Skia vs native rendering affects performance and fidelity
  3. Apply MVVM or MVUX patterns — keep views dumb, logic in ViewModels
  4. Handle platform differences — use conditional XAML or partial classes
  5. Test on all target platforms — behavior varies across platforms

Project Structure

MyApp/
├── MyApp/                    # Shared code
│   ├── App.xaml              # Application entry
│   ├── MainPage.xaml         # Main page
│   ├── Presentation/         # ViewModels (MVUX/MVVM)
│   ├── Business/             # Business logic
│   └── Services/             # Platform services
├── MyApp.Wasm/               # WebAssembly head
├── MyApp.Mobile/             # iOS and Android head
├── MyApp.Skia.Gtk/           # Linux head
├── MyApp.Skia.WPF/           # Windows Skia head
└── MyApp.Windows/            # Native WinUI head

MVUX Pattern (Uno Extensions)

Model Definition

csharp
public partial record MainModel
{
    public IListFeed<TodoItem> Items => ListFeed.Async(LoadItems);

    private async ValueTask<IImmutableList<TodoItem>> LoadItems(CancellationToken ct)
    {
        var items = await _todoService.GetAllAsync(ct);
        return items.ToImmutableList();
    }
}

View Binding with FeedView

xml
<Page xmlns:uen="using:Uno.Extensions.Navigation.UI">
    <utu:FeedView Source="{Binding Items}">
        <utu:FeedView.ValueTemplate>
            <DataTemplate>
                <ListView ItemsSource="{Binding}">
                    <ListView.ItemTemplate>
                        <DataTemplate x:DataType="local:TodoItem">
                            <TextBlock Text="{Binding Title}" />
                        </DataTemplate>
                    </ListView.ItemTemplate>
                </ListView>
            </DataTemplate>
        </utu:FeedView.ValueTemplate>
        <utu:FeedView.ProgressTemplate>
            <DataTemplate>
                <ProgressRing IsActive="True" />
            </DataTemplate>
        </utu:FeedView.ProgressTemplate>
    </utu:FeedView>
</Page>

Classic MVVM with MVVM Toolkit

ViewModel

csharp
public partial class MainViewModel(ITodoService todoService) : ObservableObject
{
    [ObservableProperty]
    private ObservableCollection<TodoItem> _items = [];

    [ObservableProperty]
    private bool _isLoading;

    [RelayCommand]
    private async Task LoadItemsAsync()
    {
        IsLoading = true;
        try
        {
            var items = await todoService.GetAllAsync();
            Items = new ObservableCollection<TodoItem>(items);
        }
        finally
        {
            IsLoading = false;
        }
    }
}

Platform-Specific Code

Conditional XAML

xml
<TextBlock Text="Welcome"
           xmlns:win="http://schemas.microsoft.com/winfx/2006/xaml/presentation"
           xmlns:android="http://uno.ui/android"
           xmlns:ios="http://uno.ui/ios"
           xmlns:wasm="http://uno.ui/wasm">

    <win:TextBlock.Foreground>
        <SolidColorBrush Color="Blue" />
    </win:TextBlock.Foreground>

    <android:TextBlock.Foreground>
        <SolidColorBrush Color="Green" />
    </android:TextBlock.Foreground>
</TextBlock>

Partial Classes

csharp
// Services/DeviceService.cs (shared)
public partial class DeviceService
{
    public partial string GetDeviceInfo();
}

// Services/DeviceService.wasm.cs
public partial class DeviceService
{
    public partial string GetDeviceInfo() => "WebAssembly";
}

// Services/DeviceService.Android.cs
public partial class DeviceService
{
    public partial string GetDeviceInfo() =>
        $"Android {Android.OS.Build.VERSION.Release}";
}

Hot Reload and Development

csharp
// Enable Hot Reload in App.xaml.cs
public App()
{
    this.InitializeComponent();

#if DEBUG
    // Enable Hot Reload
    this.EnableHotReload();
#endif
}

Anti-Patterns to Avoid

Anti-PatternWhy It's BadBetter Approach
Platform code in sharedBreaks compilationUse partial classes or #if
Ignoring Skia differencesVisual bugsTest on all renderers
WPF assumptionsNot all APIs existCheck Uno API coverage
Heavy XAMLSlow on WASMVirtualize, simplify
Synchronous loadingUI freezesAlways use async

Performance Best Practices

  1. Use virtualized lists:

    xml
    <ListView ItemsSource="{Binding Items}"
              VirtualizingPanel.VirtualizationMode="Recycling" />
  2. Lazy load resources:

    csharp
    // Load images on demand
    var image = await ImageSource.LoadFromUriAsync(uri);
  3. Minimize XAML complexity:

    • Avoid deep nesting
    • Use compiled bindings (x:Bind where supported)
    • Consider Skia-specific optimizations
  4. WebAssembly specific:

    • Minimize interop calls
    • Use InvokeAsync for JS interop
    • Consider AOT compilation for performance

Uno Extensions

csharp
// Use Uno.Extensions for enhanced patterns
var builder = this.CreateBuilder(args)
    .Configure(host => host
        .UseConfiguration()
        .UseLocalization()
        .UseNavigation()
        .UseMvux()
        .ConfigureServices(services =>
        {
            services.AddSingleton<ITodoService, TodoService>();
        }));

Deliver

  • single codebase running on web, mobile, and desktop
  • consistent UI/UX across all platforms
  • platform-specific optimizations where needed
  • MVVM or MVUX patterns for testability

Validate

  • app builds and runs on all target platforms
  • platform-specific features work correctly
  • performance is acceptable on WebAssembly
  • Hot Reload works during development
  • no WPF/UWP-only APIs are used without fallbacks

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 Uno Platform AI skill do?

Build cross-platform .NET applications with Uno Platform targeting WebAssembly, iOS, Android, macOS, Linux, and Windows from a single XAML/C# codebase. USE FOR: building cross-platform apps from a single C# and XAML codebase; targeting WebAssembly, iOS, Android, macOS, Linux, and Windows simultaneously; migrating WPF or UWP. DO NOT USE FOR: unrelated stacks; generic tasks that do not need this specific guidance. INVOKES: inspect the repository context, edit targeted files, and run relevant build, test, lint, or validation commands when changes are made.

Why use Uno Platform on TypingMind?

Because you install it once and use it with any model. Uno Platform 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 Uno Platform in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/managedcode/dotnet-skills/tree/main/catalog/Frameworks/Uno-Platform/skills/uno-platform. 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 Uno Platform?

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 Uno Platform?

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

Is the Uno Platform AI skill free?

Yes. It is published on GitHub by managedcode 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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