flutter-mcp-cli-runtime-validation

Name: flutter-mcp-cli-runtime-validation
Author: tomevault-io

AI Community

Repo stars 0
Author updated Live
Author repo skills-registry

Fluxly profile Facts only: domain, agents, trust score, runtime, permissions and network

Domain: AI
Compatible agents: Claude Code
Cursor
Cline
Codex
Windsurf
Gemini CLI

+20
Trust score: 88 / 100 · community maintained
Author / version / license: @tomevault-io · no license declared
Token usage: Lean
Setup complexity: Guided setup
External API key: Not required
Operating systems: macOS
Runtime requirements: No special requirements
Permissions: Read-only
Write / modify
Shell exec
Network behavior: Local-only
Install commands: 26 variants

Profile is derived at build time from SKILL.md and install vectors. Subject to drift from author intent.

Heads up: 未限定 allowed-tools，默认拥有全部工具权限。

Output preview flutter-mcp-cli-runtime-validation.preview

---
name: flutter-mcp-cli-runtime-validation
description: Run Flutter MCP runtime validation from CLI in two steps (launch app, then run validate-runtime)…
category: ai
runtime: no special runtime
---

# flutter-mcp-cli-runtime-validation output preview

## PART A: Task fit
- Use case: Run Flutter MCP runtime validation from CLI in two steps (launch app, then run validate-runtime), including toolkit-extension gating, screenshot/layout capture, app error collection, optional reload verification, and retry handling for transient first-connect failures. Use when this capability is needed..
- Inputs: target material, constraints, expected output, and acceptance criteria.
- Evidence boundary: follow “Two-Step Flow / What validate-runtime Must Prove / Output Handling” and do not present inference as author intent.

## PART B: Execution result
- **01** The card summarizes the use case; runtime output centers on “Run Flutter MCP runtime validation from CLI in two steps (launch app, then run validate-runtime), including toolkit-extension gating, screenshot/layout capture, app error collection, optional reload verification, and retry handling for transient first-connect failures. Use when this capability is needed.”.
- **02** When the source has headings, the agent prioritizes “Two-Step Flow / What validate-runtime Must Prove / Output Handling” so the result follows the author’s structure.
- **03** Typical output includes task judgment, concrete steps, required commands or file edits, validation, and follow-up options.
- **04** Risk context follows the fingerprint: read files, write/modify files, run shell commands; mostly runs locally; usually needs no extra API key.

## Running Rules
- read files, write/modify files, run shell commands; mostly runs locally; usually needs no extra API key.
- Validate with a small sample before expanding scope.
- Return the result, validation criteria, and next iteration options.

SKILL.md SKILL.md

---
name: flutter-mcp-cli-runtime-validation
description: Run Flutter MCP runtime validation from CLI in two steps (launch app, then run validate-runtime)…
category: ai
source: tomevault-io/skills-registry
---

# flutter-mcp-cli-runtime-validation

## When to use
- Run Flutter MCP runtime validation from CLI in two steps (launch app, then run validate-runtime), including toolkit-ex…
- Use it when the task has clear inputs, repeatable steps, and validation criteria.

## What to provide
- Target material, scope, expected result, and forbidden changes.
- Whether network, commands, file writes, or external services are allowed.

## Execution rules
- Organize steps around “Two-Step Flow / What validate-runtime Must Prove / Output Handling” and keep inference separate from source facts.
- read files, write/modify files, run shell commands; mostly runs locally; usually needs no extra API key.
- Validate with a small sample before expanding the task.

## Output requirements
- Return the deliverable, key evidence, validation method, and next action.
- Mark missing information as unknown; do not invent commands, platforms, or dependencies.

Evidence boundary and execution chain

The author source anchors workflow facts; repository files anchor sources and commands; Fluxly only adds fit, limitations, and quality judgment.

skill "flutter-mcp-cli-runtime-validation" {
  input      -> user goal + target files + boundaries + acceptance criteria
  context    -> Two-Step Flow / What validate-runtime Must Prove / Output Handling
  rules      -> SKILL.md triggers / order / output contract
  runtime    -> no special runtime | read files, write/modify files, run shell commands | mostly runs locally
  guardrails -> usually needs no extra API key + small-sample validation + diff/log review
  output     -> copyable result + checklist + next iteration
}

Interpretation is structured for decision-making; original keeps the upstream SKILL.md unchanged.

Decide Fit First

Core job: Run Flutter MCP runtime validation from CLI in two steps (launch app, then run validate-runtime), including toolkit-extension ga…
Best fit: Use it when the task has reusable inputs, steps, and validation criteria rather than a one-off answer.
Avoid forcing it: If the source lacks commands, platform support, or external-service evidence, keep those fields unknown instead of guessing.

Design Intent

Structure: The skill is organized around “Two-Step Flow”, “What validate-runtime Must Prove”, “Output Handling”, “Failure Rules”, showing how the author expects the agent to judge fit, collect context, and produce verifiable output.
Trigger evidence: Prioritize the author’s wording around when to use it, what context to collect, and what output shape to produce.
Evidence boundary: Author text states facts, repository files prove commands and paths, and Fluxly only adds fit, limits, and usage judgment.

How To Use It

Inputs: Provide target material, scope, expected result, forbidden changes, and validation method.
Invocation: Name flutter-mcp-cli-runtime-validation directly; if the source includes slash commands, start with the command and then add task context.
Validation: Start small and check whether the result follows “Two-Step Flow / What validate-runtime Must Prove / Output Handling” before expanding.

Boundaries And Review

Dependencies: It usually needs no extra API key, so start with a small validation task.
Permissions: Declared permissions include read / write / shell-exec; ask the agent to state file, command, and rollback boundaries before acting.
Quality bar: A useful result names the deliverable, evidence, and next action. Generic prose means the task needs tighter context.

Flutter MCP CLI Runtime Validation

Use this skill when you need agent-style runtime validation through flutter-mcp-toolkit with minimal operator steps.

Two-Step Flow

Launch the Flutter app in debug mode.
Run one CLI command:

dart run mcp_server_dart/bin/flutter_mcp_toolkit.dart --save-images --output-dir .flutter_mcp/runtime_validation validate-runtime \
  --target ws://127.0.0.1:8181/<token>/ws \
  --timeout-ms 10000 \
  --post-reload-delay-ms 500 \
  --after-reload

Optional skill install in the same command:

dart run mcp_server_dart/bin/flutter_mcp_toolkit.dart validate-runtime \
  --target ws://127.0.0.1:8181/<token>/ws \
  --install-skill

Permission behavior for this flow:

validate-runtime stays read/write only for visual capture and defaults to auto_request_once.
doctor remains read-only.
On macOS, Screen Recording permission belongs to the host process running flutter-mcp-toolkit.
On web, flutter_layer is the only supported truth path and no OS permission prompt is expected.
If the first capture_ui_snapshot attempt uses host desktop_window and fails (common on macOS when the window is not foregrounded, or for iOS Simulator), validate-runtime automatically retries once with flutter_layer.
You may pass the VM URI as global --vm-service-uri instead of validate-runtime --target when only one URI is needed.

What `validate-runtime` Must Prove

Doctor preflight passes critical checks.
Required toolkit extensions exist:
- ext.mcp.toolkit.app_errors
- ext.mcp.toolkit.view_details
- ext.mcp.toolkit.view_screenshots
- ext.mcp.toolkit.inspect_widget_at_point
Screenshot capture works.
View details (layout metadata) are available.
App errors are retrievable.
If --after-reload is enabled, post-reload screenshot also works.

Output Handling

Use data.summary as pass/fail status for automation.
Use data.summary.captureFallbackUsed to see whether the flutter_layer retry succeeded after a failed desktop_window attempt.
Use data.steps for per-step evidence and retries.
Use data.doctor.checks to explain setup blockers.
Use data.summary.screenshotFiles for saved screenshot paths when --save-images is enabled.
When --save-images is enabled, read screenshot file URLs from step data.
For visual debugging reports, also run:
- exec --name capture_ui_snapshot --args '{"errorsCount":4,"compress":true,"includeViewDetails":true,"includeErrors":true}'
- exec --name inspect_widget_at_point --args '{"x":<int>,"y":<int>}'

Failure Rules

If toolkit extensions are missing, stop and report instrumentation gap with exact fix:
- add mcp_toolkit to app dependencies
- ensure MCPToolkitBinding.instance.bootstrapFlutter(...) or equivalent manual initialization runs before runApp
- hot restart or rerun the app
If first explicit URI connect fails, retry is automatic for retryable connection errors.
If screenshots are blank, verify app window is visible and retry.
If macOS visual capture is denied, use:
- dart run mcp_server_dart/bin/flutter_mcp_toolkit.dart permissions status
- dart run mcp_server_dart/bin/flutter_mcp_toolkit.dart permissions request
- dart run mcp_server_dart/bin/flutter_mcp_toolkit.dart permissions open-settings
If app cannot be instrumented, do not claim screenshot/layout/error inspection success.

Visual QA + Source Mapping Rules

Always compare before/after screenshot evidence around changes.
For each reported visual issue, provide coordinate + inspect_widget_at_point output.
Map defects to source using get_app_errors top stack frame (file, line, column) when available.
Do not use debug_dump_* unless explicitly requested.

Challenge Cases (Always Call Out Explicitly)

No running debug app: doctor critical failure on vm_target_reachable; request app launch before continuing.
Wrong target URI/token: treat as connection mismatch and retry with exact app.debugPort.wsUri.
Toolkit added but still missing extensions: hot reload is often insufficient, require hot restart/full rerun.
Non-modifiable app (cannot add toolkit): report inspection as unavailable instead of guessing.

Source: Arenukvern/mcp_flutter — distributed by TomeVault.

Install command

One-click installPick a runner, click Copy, paste into your terminal, press enter

npx skills add tomevault-io/skills-registry/arenukvern--mcp-flutter--flutter-mcp-cli-runtime-validation -y

Open general install guide

View skill source

Works with

Claude Code
Cursor
Cline
Codex
Windsurf
Gemini
OpenCode
Roo Code
AAide
+Augment
GitHub Copilot
OpenClaw
KKiro
Junie
WWarp
Goose
Trae
Kilo Code
OpenHands
Amp
Zencoder
CContinue
AIAider
CodeBuddy
CodeGeeX
Replit Agent

Security audit

88 / 100

Community

未声明 license/许可。
未声明 version。
未限定 allowed-tools，默认拥有全部工具权限。

Metadata

Source: github
tomevault-io/skills-registry
中文

More by @tomevault-io

Category & tags

Category AI