/e2e-testing
AI-powered E2E testing for any app — Flutter, React Native, iOS, Android, Electron, Tauri, KMP, .NET MAUI. Connects via MCP to running apps so the agent can take screenshots, tap elements, enter text, scroll, inspect UI trees, and verify state with natural language. Use when the
$ npx -y skills add ai-dashboad/flutter-skill --skill e2e-testing --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
- You can call itInvoke it directly when you want it.
- Slash command
/e2e-testing
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AI-powered E2E testing for any app — Flutter, React Native, iOS, Android, Electron, Tauri, KMP, .NET MAUI. Connects via MCP to running apps so the agent can take screenshots, tap elements, enter text, scroll, inspect UI trees, and verify state with natural language. Use when the
SKILL.md
e2e-testing.SKILL.mdname: e2e-testing
description: "AI-powered E2E testing for any app — Flutter, React Native, iOS, Android, Electron, Tauri, KMP, .NET MAUI. Connects via MCP to running apps so the agent can take screenshots, tap elements, enter text, scroll, inspect UI trees, and verify state with natural language. Use when the user wants to test an app's UI end-to-end, automate cross-platform testing, run smoke tests, validate form flows, or verify navigation without writing test code."
AI E2E Testing — 8 Platforms, Zero Test Code
flutter-skill is an MCP server that connects AI agents to running apps across 8 platforms. The agent takes screenshots, taps elements, enters text, scrolls, navigates, inspects UI trees, and verifies state — all through natural language.
Supported Platforms
| Platform | Setup | |----------|-------| | Flutter (iOS/Android/Web) | `flutter pub add flutter_skill` | | React Native | `npm install flutter-skill-react-native` | | Electron | `npm install flutter-skill-electron` | | iOS (Swift/UIKit) | SPM: `FlutterSkillSDK` | | Android (Kotlin) | Gradle: `flutter-skill-android` | | Tauri (Rust) | `cargo add flutter-skill-tauri` | | KMP Desktop | Gradle dependency | | .NET MAUI | NuGet package |
Install
# npm (recommended)
npm install -g flutter-skill
# Homebrew
brew install ai-dashboad/flutter-skill/flutter-skill
# Or download binary from GitHub Releases
MCP Configuration
Add to your AI agent's MCP config (Claude Desktop, Cursor, Windsurf, OpenClaw, etc.):
{
"mcpServers": {
"flutter-skill": {
"command": "flutter-skill",
"args": ["server"]
}
}
}Quick Start
1. Initialize your app (one-time)
flutter-skill init
Auto-detects project type and patches your app with the testing bridge.
**Verify:** Output should confirm the project type was detected and main entry point was patched. If it fails, check that you are in the project root and the framework is supported.
2. Launch and connect
flutter-skill launch .
**Verify:** A VM Service URI appears in the output (e.g. `ws://127.0.0.1:50000/ws`). If no URI appears, check that Flutter/the target framework is installed and the app compiles.
3. Test with natural language
The agent follows this core loop:
1. `screenshot()` — see the current screen 2. `inspect_interactive()` — discover all tappable/typeable elements with semantic refs 3. `tap(ref: "button:Login")` — tap using stable semantic reference 4. `enter_text(ref: "input:Email", text: "admin@test.com")` — type into field 5. `wait_for_element(key: "Dashboard")` — verify navigation succeeded 6. `screenshot()` — confirm final state
**If `inspect_interactive()` returns no elements:** Take a screenshot to confirm the screen loaded, then check `get_logs()` for errors. The app may still be loading — retry after a short wait.
Available MCP Tools
Core Actions
| Tool | Description | |------|-------------| | `screenshot` | Capture current screen as image | | `tap` | Tap element by key, text, ref, or coordinates | | `enter_text` | Type text into a field | | `scroll` | Scroll up/down/left/right | | `swipe` | Swipe gesture between points | | `long_press` | Long press an element | | `drag` | Drag from point A to B | | `go_back` | Navigate back | | `press_key` | Send keyboard key events |
Inspection
| Tool | Description | |------|-------------| | `inspect_interactive` | Get all interactive elements with semantic ref IDs | | `get_elements` | List all elements on screen | | `find_element` | Find element by key or text | | `wait_for_element` | Wait for element to appear (with timeout) | | `get_element_properties` | Get detailed properties of an element |
Text Manipulation
| Tool | Description | |------|-------------| | `set_text` | Replace text in a field | | `clear_text` | Clear a text field | | `get_text` | Read text content |
App Control
| Tool | Description | |------|-------------| | `get_logs` | Read app logs | | `clear_logs` | Clear log buffer |
Semantic Refs
`inspect_interactive` returns elements with stable semantic reference IDs:
button:Login → Login button
input:Email → Email text field
toggle:Dark Mode → Dark mode switch
button:Submit[1] → Second Submit button (disambiguated)
Format: `{role}:{content}[{index}]`
7 roles: `button`, `input`, `toggle`, `slider`, `select`, `link`, `item`
Use refs for reliable element targeting that survives UI changes:
tap(ref: "button:Login")
enter_text(ref: "input:Email", text: "test@example.com")
Testing Workflow
Core Loop
screenshot() → inspect_interactive() → tap/enter_text → screenshot() → verify
Always call `screenshot()` before and after actions. Use `wait_for_element()` after navigation — apps need time to transition.
Validation Checkpoints
- **After `screenshot()`**: Confirm the expected screen is visible before acting.
- **After `tap()` or `enter_text()`**: Call `screenshot()` to verify the UI responded.
- **After navigation**: Use `wait_for_element(key: "target_screen")` with a timeout. If it times out, call `screenshot()` and `get_logs()` to diagnose.
- **On unexpected state**: Call `get_logs()` and `inspect_interactive()` to understand what elements are present.
Element Targeting Priority
1. **`ref:`** (most reliable) — semantic ref from `inspect_interactive()` 2. **`key:`** — widget key set by the developer 3. **`text:`** — visible text content (fragile if text changes) 4. **Coordinates** — last resort, breaks on different screen sizes
Links
- [GitHub](https://github.com/ai-dashboad/flutter-skill)
- [npm](https://www.npmjs.com/package/flutter-skill)
- [Documentation](https://github.com/ai-dashboad/flutter-skill/blob/main/docs/USAGE_GUIDE.md)
- [pub.dev](https://pub.dev/packages/flutter_skill)
- [VSCode Extension](https://marketplace.visualstudio.com/items?itemName=AIDashboard.flutter-skill)
Read more
name: e2e-testing description: "AI-powered E2E testing for any app — Flutter, React Native, iOS, Android, Electron, Tauri, KMP, .NET MAUI. Connects via MCP to running apps so the agent can take screenshots, tap elements, enter text, scroll, inspect UI trees, and verify state with natural language. Use when the user wants to test an app's UI end-to-end, automate cross-platform testing, run smoke tests, validate form flows, or verify navigation without writing test code."
AI E2E Testing — 8 Platforms, Zero Test Code
flutter-skill is an MCP server that connects AI agents to running apps across 8 platforms. The agent takes screenshots, taps elements, enters text, scrolls, navigates, inspects UI trees, and verifies state — all through natural language.
Supported Platforms
| Platform | Setup | |----------|-------| | Flutter (iOS/Android/Web) | `flutter pub add flutter_skill` | | React Native | `npm install flutter-skill-react-native` | | Electron | `npm install flutter-skill-electron` | | iOS (Swift/UIKit) | SPM: `FlutterSkillSDK` | | Android (Kotlin) | Gradle: `flutter-skill-android` | | Tauri (Rust) | `cargo add flutter-skill-tauri` | | KMP Desktop | Gradle dependency | | .NET MAUI | NuGet package |
Install
# npm (recommended) npm install -g flutter-skill # Homebrew brew install ai-dashboad/flutter-skill/flutter-skill # Or download binary from GitHub Releases
MCP Configuration
Add to your AI agent's MCP config (Claude Desktop, Cursor, Windsurf, OpenClaw, etc.):
{
"mcpServers": {
"flutter-skill": {
"command": "flutter-skill",
"args": ["server"]
}
}
}Quick Start
1. Initialize your app (one-time)
flutter-skill init
Auto-detects project type and patches your app with the testing bridge.
**Verify:** Output should confirm the project type was detected and main entry point was patched. If it fails, check that you are in the project root and the framework is supported.
2. Launch and connect
flutter-skill launch .
**Verify:** A VM Service URI appears in the output (e.g. `ws://127.0.0.1:50000/ws`). If no URI appears, check that Flutter/the target framework is installed and the app compiles.
3. Test with natural language
The agent follows this core loop:
1. `screenshot()` — see the current screen 2. `inspect_interactive()` — discover all tappable/typeable elements with semantic refs 3. `tap(ref: "button:Login")` — tap using stable semantic reference 4. `enter_text(ref: "input:Email", text: "admin@test.com")` — type into field 5. `wait_for_element(key: "Dashboard")` — verify navigation succeeded 6. `screenshot()` — confirm final state
**If `inspect_interactive()` returns no elements:** Take a screenshot to confirm the screen loaded, then check `get_logs()` for errors. The app may still be loading — retry after a short wait.
Available MCP Tools
Core Actions
| Tool | Description | |------|-------------| | `screenshot` | Capture current screen as image | | `tap` | Tap element by key, text, ref, or coordinates | | `enter_text` | Type text into a field | | `scroll` | Scroll up/down/left/right | | `swipe` | Swipe gesture between points | | `long_press` | Long press an element | | `drag` | Drag from point A to B | | `go_back` | Navigate back | | `press_key` | Send keyboard key events |
Inspection
| Tool | Description | |------|-------------| | `inspect_interactive` | Get all interactive elements with semantic ref IDs | | `get_elements` | List all elements on screen | | `find_element` | Find element by key or text | | `wait_for_element` | Wait for element to appear (with timeout) | | `get_element_properties` | Get detailed properties of an element |
Text Manipulation
| Tool | Description | |------|-------------| | `set_text` | Replace text in a field | | `clear_text` | Clear a text field | | `get_text` | Read text content |
App Control
| Tool | Description | |------|-------------| | `get_logs` | Read app logs | | `clear_logs` | Clear log buffer |
Semantic Refs
`inspect_interactive` returns elements with stable semantic reference IDs:
button:Login → Login button input:Email → Email text field toggle:Dark Mode → Dark mode switch button:Submit[1] → Second Submit button (disambiguated)
Format: `{role}:{content}[{index}]`
7 roles: `button`, `input`, `toggle`, `slider`, `select`, `link`, `item`
Use refs for reliable element targeting that survives UI changes:
tap(ref: "button:Login") enter_text(ref: "input:Email", text: "test@example.com")
Testing Workflow
Core Loop
screenshot() → inspect_interactive() → tap/enter_text → screenshot() → verify
Always call `screenshot()` before and after actions. Use `wait_for_element()` after navigation — apps need time to transition.
Validation Checkpoints
- **After `screenshot()`**: Confirm the expected screen is visible before acting.
- **After `tap()` or `enter_text()`**: Call `screenshot()` to verify the UI responded.
- **After navigation**: Use `wait_for_element(key: "target_screen")` with a timeout. If it times out, call `screenshot()` and `get_logs()` to diagnose.
- **On unexpected state**: Call `get_logs()` and `inspect_interactive()` to understand what elements are present.
Element Targeting Priority
1. **`ref:`** (most reliable) — semantic ref from `inspect_interactive()` 2. **`key:`** — widget key set by the developer 3. **`text:`** — visible text content (fragile if text changes) 4. **Coordinates** — last resort, breaks on different screen sizes
Links
- [GitHub](https://github.com/ai-dashboad/flutter-skill)
- [npm](https://www.npmjs.com/package/flutter-skill)
- [Documentation](https://github.com/ai-dashboad/flutter-skill/blob/main/docs/USAGE_GUIDE.md)
- [pub.dev](https://pub.dev/packages/flutter_skill)
- [VSCode Extension](https://marketplace.visualstudio.com/items?itemName=AIDashboard.flutter-skill)
AI-powered E2E testing for 10 platforms. 253 MCP tools. Zero config. Works with Claude, Cursor, Windsurf, Copilot. Test Flutter, React Native, iOS, Android, Web, Electron, Tauri, KMP, .NET MAUI — all from natural language.
Repo: ai-dashboad/flutter-skill

