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 →
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is mobile-jev safe to install? View the security audit →
Mobile Jev ▶ Watch the demo — Jev opens Uber, enters a route from San Francisco Airport to the Golden Gate Bridge, and reaches payment selection. The recorded task timer shows about 21 seconds for 9 actions. A completed booking is not demonstrated. One goal. A real Android phone. Jev makes the decisions. A standalone mobile agent for Mobilerun, powered by TypeSafe's Jev and the Mobilerun API. Includes a live React studio, a CLI, execution traces, and request-level latency measurements. No ADB connection is required. Mobilerun · Mobilerun docs · TypeSafe · Jev docs · Demo guide Run it Requirements: Node.js 24 recommended (22.16+ supported), pnpm 10.30.1, curl 7.70+, a ready Mobilerun Android device, and API keys for Mobilerun and TypeSafe. Tested locally on macOS; CI runs offline tests and the build on Linux. Add and to .env
| Stars | 260 |
| Forks | 34 |
| Language | JavaScript |
| Category | AI Tool |
| License | MIT |
| Quality Score | 53.6658052024418/100 |
| Open Issues | 3 |
| Last Updated | 2026-09-17 |
| Created | 2026-09-17 |
| Platforms | node |
| Est. Tokens | ~15k |
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mobile-jev is an open-source ai tool by droidrun with 260 GitHub stars.
mobile-jev is primarily written in JavaScript.
You can find installation instructions and usage details in the mobile-jev GitHub repository at github.com/droidrun/mobile-jev. The project has 260 stars and 34 forks, indicating an active community.
mobile-jev is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to mobile-jev on Agent Skills Hub include pivotnacci, MedgeClaw, SciToolAgent. 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: