iphone-use
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.
- Automate repetitive manual testing tasks on a physical iPhone device
- Generate step-by-step app interaction flows for quality assurance testing
- Find UI bugs by letting Claude navigate through your mobile application
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.
https://github.com/leeguooooo/iphone-use
By leeguooooo
How to Get It
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.
After installing, paste this into Claude:
Help me automate repetitive manual testing tasks on a physical iPhone device
Trust Signals Auto-scanned
Community Pulse Growing
Discussed on Hacker News
- Android and iPhone users can now share files, starting with the Pixel 10 — Hacker News · 886 pts
- T-Mobile begins blocking iPhone users from enabling iCloud Private Relay in US — Hacker News · 692 pts
- TikTok wants to keep tracking iPhone users with state-backed workaround — Hacker News · 358 pts
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.
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Evaluation
Scored from trust signals (evidence-eval-v1): 397 GitHub stars; contributors unknown; last commit 0d ago; license no license.