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iphone-use

Skill Development Usable
Works inClaude Code
Usable Scanned — metadata only

Enables AI agents to interact with physical iOS hardware, bridging the gap between code generation and real-world mobile application testing.

响应快 省Token, Let AI agents (Claude Code, Codex, any MCP client) drive a real iPhone · 让 AI agent 操作真实 iPhone:读屏、点击、输入、滑动,USB 或 Wi-Fi,自有 XCTest runner,可回放的 App flow。Open source, self-hosted on macOS.

397 starsFreeQuick setup
Usable rating — This tool is functional but has notable gaps. Review the evaluation notes below before deploying.

Enables AI agents to interact with physical iOS hardware, bridging the gap between code generation and real-world mobile application testing. This reduces manual QA overhead and improves the reliability of automated mobile workflows.

QA engineers and mobile developers automating end-to-end testing on physical iOS devices via AI agents.

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https://github.com/leeguooooo/iphone-use

By leeguooooo

How to Get It

Option 1: Claude Desktop App (Code Mode)Click the + button next to the prompt box → Plugins → Add plugin. Search and click Install. Skills work in Claude Code only.
Option 2: Paste into Claude CodeCopy the command below and paste it into your conversation. Claude will install it.
Command
claude plugins install leeguooooo/iphone-use

Tip: Paste this into a Claude Code conversation. Verify command matches your Claude Code version.

Auto-generated from the tool's public listing — not hands-on verified. Cross-check against the source repo's README before running.

First thing to try

After installing, paste this into Claude:

Help me automate repetitive manual testing tasks on a physical iPhone device
CostFree

Trust Signals Auto-scanned

Stars397Last updated2026-10-29Known CVEsNone foundSources: GitHub Advisory Database + OSV.dev · Scanned 2026-10-29 · scanner v1

Community Pulse Growing

Discussed on Hacker News

3 mentions across 1 sources

Reviewer notes

Auto-scanned review. These are observations, not a security certification.

Scored from trust signals (evidence-eval-v1): 397 GitHub stars; contributors unknown; last commit 0d ago; license no license.

Things to check

  • Scanned, not hands-on tested — this entry was auto-scanned from public metadata (GitHub metrics, license, security flags). No reviewer has run it, and no tool-specific limitations have been documented yet.

How to evaluate tools before deploying →

Data shown here comes from public APIs and automated scanning. Reviewer notes reflect one person's experience. This is not a security certification or legal recommendation. Always evaluate tools according to your own organization's policies.

Evaluation

Ease of Use
1/5
Versatility
4/5
Reliability
3/5
Security
3/5
Overall score2.55 / 5.00 UsableEvaluatedOct 2026
Scored from trust signals (evidence-eval-v1): 397 GitHub stars; contributors unknown; last commit 0d ago; license no license.

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