No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by erickjtorres · Agent Tool · ★ 69
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is app-use safe to install? View the security audit →
Enable AI to control your mobile apps 🤖 📱 App Use is the easiest way to connect AI agents with mobile applications. Our goal is to provide a powerful yet simple interface for AI agent app automation. Quick start With pip (Python=3.11): For memory functionality (requires Python<3.13 due to PyTorch compatibility): Install the necessary drivers and software: Check out our environment setup docs for more info! Or feel free to try out our cli for a seamless setup: Define the app and mobile device you want to target: python app = App( platformname="ios", device
| Stars | 69 |
| Forks | 6 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 68.2823986407451/100 |
| Last Updated | 2026-08-07 |
| Created | 2025-05-16 |
| Platforms | python |
| Est. Tokens | ~56k |
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app-use is 📱 Make apps accessible for AI agents. It is categorized as a Agent Tool with 69 GitHub stars.
app-use is primarily written in Python. It covers topics such as ai-agents, ai-tools, app-use.
You can find installation instructions and usage details in the app-use GitHub repository at github.com/erickjtorres/app-use. The project has 69 stars and 6 forks, indicating an active community.
app-use is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to app-use on Agent Skills Hub include claudepro-directory, eval-view, AppClaw. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: