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 callstack · MCP Server · ★ 4.9k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is agent-device safe to install? View the security audit →
agent-device Mobile app verification for AI agents. A device automation CLI for real apps on iOS, Android, TV, and desktop. Agents get token-efficient snapshots, semantic refs, and evidence captured only when needed. lets coding agents open apps, inspect the current UI, interact with visible elements, and collect debugging evidence through one CLI. Use it when an agent needs to verify what actually happens on a device, not just reason about code. If you know Vercel's agent-browser, is the same idea for mobile, TV, and desktop apps. It works with native iOS and Android apps, plus apps b
| Stars | 4,875 |
| Forks | 322 |
| Language | TypeScript |
| Category | MCP Server |
| License | MIT |
| Quality Score | 70.4856436484931/100 |
| Open Issues | 140 |
| Last Updated | 2026-10-03 |
| Created | 2026-01-30 |
| Platforms | browser, cli, mcp, node |
| Est. Tokens | ~16k |
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agent-device is Mobile app automation and verification for AI coding agents. CLI, MCP server, and typed Node.js API for iOS, Android, HarmonyOS, TV, web, macOS, and Linux.. It is categorized as a MCP Server with 4.9k GitHub stars.
agent-device is primarily written in TypeScript. It covers topics such as adb, agentic-ai, agents.
You can find installation instructions and usage details in the agent-device GitHub repository at github.com/callstack/agent-device. The project has 4.9k stars and 322 forks, indicating an active community.
agent-device is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to agent-device on Agent Skills Hub include agent-device, bytebot, lamda. 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: