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 agentmods add skills/ai-dashboad/flutter-skill/flutter-skillnpx skills add ai-dashboad/flutter-skill --skill flutter-skillgit clone --depth 1 https://github.com/ai-dashboad/flutter-skillWhat 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 | $0.00073 | $0.00971 |
| Opus 5 | $0.00036 | $0.00485 |
| Sonnet 5 | $0.00015 | $0.00194 |
| Haiku 4.5 | $0.00007 | $0.00097 |
Grade A, and why
flutter-skill 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 2d 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 — 117 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Flutter Skill
Control running Flutter applications for testing, debugging, and automation. Connects AI agents to Flutter apps via the Dart VM Service Protocol, exposing tools for UI inspection, gestures, state validation, screenshots, and log access.
Installation
Option 1: npx (Recommended)
{
"mcpServers": {
"flutter-skill": {
"command": "npx",
"args": ["flutter-skill"]
}
}
}
Option 2: Global Install
dart pub global activate flutter_skill
{
"mcpServers": {
"flutter-skill": {
"command": "flutter_skill",
"args": ["server"]
}
}
}
Key Tools
| Category | Tools | Purpose |
|---|---|---|
| Connection | launch_app, connect_app |
Start or attach to a Flutter app |
| Inspection | inspect, get_widget_tree, find_by_type |
Discover UI elements and widget structure |
| Interaction | tap, enter_text, swipe, scroll_to, long_press, drag |
Perform gestures and input |
| Validation | wait_for_element, wait_for_gone, get_text_value, get_checkbox_state |
Assert UI state |
| Screenshots | screenshot, screenshot_element |
Capture visual state |
| Navigation | go_back, get_current_route, get_navigation_stack |
Control and inspect navigation |
| Debug | get_logs, get_errors, hot_reload, get_performance |
Diagnose issues |
Workflow
Core Testing Loop
launch_app(project_path: "/path/to/app")
→ screenshot()
→ inspect()
→ tap(key: "element_key") / enter_text(key: "field_key", text: "value")
→ screenshot()
→ verify with wait_for_element / get_text_value
Example: Login Flow
launch_app(project_path: "/path/to/app")
screenshot()
inspect()
enter_text(key: "email_field", text: "[email protected]")
enter_text(key: "password_field", text: "password123")
tap(key: "login_button")
wait_for_element(key: "home_screen", timeout: 5000)
screenshot()
If wait_for_element times out: Call screenshot() to see the current state, then get_errors() to check for crashes or failed network requests.
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.
- 2d ago First seen · 117 lines · 73 tokens per session scan A 06c22a08e242
flutter-skill is a skill published in the GitHub repository ai-dashboad/flutter-skill (362 stars, last pushed 11d ago), licensed MIT. It adds 73 tokens to every session and 971 once invoked, about $0.0004 per session on Opus 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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