Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add ai-dashboad/flutter-skill --skill e2e-testinggit clone --depth 1 https://github.com/ai-dashboad/flutter-skillWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/ai-dashboad/flutter-skill/e2e-testing)<a href="https://agentmods.dev/skills/ai-dashboad/flutter-skill/e2e-testing"><img src="https://agentmods.dev/badge/skills/ai-dashboad/flutter-skill/e2e-testing/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/ai-dashboad/flutter-skill/e2e-testing"><img src="https://agentmods.dev/badge/skills/ai-dashboad/flutter-skill/e2e-testing.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00107 | $0.01508 |
| Opus 5.5 | $0.00043 | $0.00603 |
| Sonnet 5.5 | $0.00021 | $0.00302 |
| Haiku 4.5 | $0.00011 | $0.00151 |
Grade A, and why
e2e-testing scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 171 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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:
screenshot()— see the current screeninspect_interactive()— discover all tappable/typeable elements with semantic refstap(ref: "button:Login")— tap using stable semantic referenceenter_text(ref: "input:Email", text: "[email protected]")— type into fieldwait_for_element(key: "Dashboard")— verify navigation succeededscreenshot()— confirm final state
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 171 lines · 107 tokens per session scan A 277057a71e1b
e2e-testing is a skill published in the GitHub repository ai-dashboad/flutter-skill (391 stars, last pushed 1mo ago), licensed MIT. It adds 107 tokens to every session and 1,508 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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android-ui-journey-testing
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A command-line framework for testing Android apps and devices with SoloPi, including on-device or cloud AI decision models. It manages devices, test cases, recorded interactions, replays, performance history, and evidence.
argent-test-ui-flow
Autonomously test an app UI (iOS or Android) by running interact-screenshot-verify loops using argent MCP tools. Use when testing UI flows, verifying login works, testing navigation, running end-to-end UI test scenarios, manual QA steps, visible UI changes, or visual behavior.
mobile-automation
Control Android and iOS devices, emulators and simulators — launch apps, tap, swipe, type, take screenshots, read the accessibility tree. Use when a task involves a mobile device or app, mobile UI testing, or reproducing a bug on a phone.