Quality Engineering Appium Mcp

Community

HoangNguyen0403

quality-engineering-appium-mcp

Drives iOS/Android mobile devices via Appium MCP. Runs a preflight, picks local emulator/simulator/USB or a device cloud, captures screenshots/page source into a named evidence dir. Use for verifying mobile bugs, E2E tests, and navigating real device clouds (LambdaTest/BrowserStack).

Overview

Publisher	HoangNguyen0403
Repository	`agent-skills-standard`
Skill name	`quality-engineering-appium-mcp`
Stars	573
Forks	166
Bundled files	6
License	MIT
Links	Skill source Repository Homepage

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.
6 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by HoangNguyen0403 on GitHub. Read the source before you install it.

Installation

Install the Quality Engineering Appium Mcp AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/HoangNguyen0403/agent-skills-standard/tree/develop/.agents/skills/quality-engineering/quality-engineering-appium-mcp
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/HoangNguyen0403/agent-skills-standard/tree/develop/.agents/skills/quality-engineering/quality-engineering-appium-mcp

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

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/HoangNguyen0403/agent-skills-standard.git /tmp/agent-skills-standard
mkdir -p .claude/skills
cp -r /tmp/agent-skills-standard/.agents/skills/quality-engineering/quality-engineering-appium-mcp .claude/skills/quality-engineering-appium-mcp

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Quality Engineering Appium Mcp 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 Quality Engineering Appium Mcp 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 Quality Engineering Appium Mcp 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.

📱 Appium MCP (Mobile Automation)

Priority: P1 (HIGH)

[!IMPORTANT] Tier 0 (Infrastructure): Session creation, device farm connectivity, basic OS interactions. Tier 1 (Core Gestures): Taps, swipes, text input for native mobile elements. Tier 2 (Flutter/Single-Canvas): Visual-first automation via screenshot + coordinate taps.

🔌 Activation

Triggers: appium, appium-mcp, appium doctor, mobile verify, android verify, ios verify, lambdatest, real device cloud, flutter widget tap.

🪜 Driver Ladder

sh scripts/preflight.sh — prints MODE: local | local,remote | remote | none; exit 0 usable, 2 not.
Local: select_device (Android) or prepare_ios_simulator (iOS), embedded drivers. Needs Node 22+, JDK 8+ / Xcode.
Cloud: remoteServerUrl (LambdaTest/BrowserStack). URL must match REMOTE_SERVER_URL_ALLOW_REGEX.
Neither: ask for exported screenshots or cloud video link; label human-provided.
Nothing exported: return BLOCKED (driver: appium), continue other lanes.

NO_UI=true default; AI_VISION_ENABLED=true only for Flutter/canvas. Local single-user or trusted CI only. generate_locators / appium_generate_tests feed test-loop step 4. Full table: driver-ladder.

🛠 Core Workflow (Goal-Oriented)

Step	Tool	Purpose
0	`select_device` / `prepare_ios_simulator`	Pick target per ladder rung; cloud skips this.
1	`appium_session_management` (`create`)	Open session. Resiliency: Retry with `noReset: true` on failure.
2	`appium_get_window_size`	Scale coordinates for high-density displays.
3	`appium_screenshot`	Capture visual state for Semantic Reasoning.
4	`appium_gesture` / `appium_set_value`	Interact. Self-Healing: Re-scan hierarchy if UUID stale.
5	`appium_session_management` (`delete`)	MANDATORY Cleanup.

💡 AI-Driven Methodologies

Semantic Intent: Find element in hierarchy (e.g., "Login button") instead of raw coordinates. Resilient to layout shifts.
Dynamic Handling: Unexpected pop-up appears? Pause, reason about alert, dismiss, resume.
Visual Anchors: Find stable "Anchor" (e.g., Header) and derive coordinates relative to it.

🚫 Anti-Patterns (Zero-Tolerance)

Hardcoded XPaths: Use accessibility id or uiautomator. XPath flaky.
Implicit Wait-Only: Never assume page loaded. Poll for "Source of Truth" element.
Ignoring QoS: Audit CPU/Memory via appium_mobile_performance_data. Prevent lag/crashes.
Orphaned Sessions: Teardown mandatory. Call delete in cleanup block.

📁 Evidence

.appium-mcp/<session>/: <AC|step>-<before|after>.png (nav shots at maxWidth budget, verdict full-res), <label>.source.xml from appium_get_page_source, perf.json, video.url. Walkthrough records driver: + evidence_dir:.

✅ Evaluation Criteria

Cleanup Rate: 100% session closure.
Visual Accuracy: Coordinate-based taps land within 5% of target center.
Security: No secrets in tool arguments or logs.

🔗 References

Visual Testing: common-mobile-visual-testing — Methodology for what to verify.
Driver Ladder: driver-ladder — prerequisites per rung, server env, canonical tools.
Setup: setup — install, MCP registration snippet, security, alternatives not adopted.
LambdaTest Setup: lambdatest-cloud-setup — RDC configuration.
Tool Cheatsheet: tool-cheatsheet — Fast copy-paste args.
Project Context: project-context — Project-specific overlays/macros.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

references/driver-ladder.md4 KB
references/lambdatest-cloud-setup.md3 KB
references/project-context.md3 KB
references/setup.md3 KB
references/tool-cheatsheet.md4 KB
scripts/preflight.sh3 KB

Frequently asked questions

What does the Quality Engineering Appium Mcp AI skill do?

Why use Quality Engineering Appium Mcp on TypingMind?

Because you install it once and use it with any model. Quality Engineering Appium Mcp 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 Quality Engineering Appium Mcp in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/HoangNguyen0403/agent-skills-standard/tree/develop/.agents/skills/quality-engineering/quality-engineering-appium-mcp. 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 Quality Engineering Appium Mcp?

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 Quality Engineering Appium Mcp?

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

Is the Quality Engineering Appium Mcp AI skill free?

Yes. It is published on GitHub by HoangNguyen0403 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

📱 Appium MCP (Mobile Automation)

Priority: P1 (HIGH)

🔌 Activation

🪜 Driver Ladder

🛠 Core Workflow (Goal-Oriented)

💡 AI-Driven Methodologies

🚫 Anti-Patterns (Zero-Tolerance)

📁 Evidence

✅ Evaluation Criteria

🔗 References

Bundled files

Frequently asked questions

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