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 callstackincubator · MCP Server · ★ 2.4k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is agent-device safe to install? View the security audit →
agent-device Device automation CLI for AI agents. Mobile, TV, and desktop apps. lets coding agents run real apps, inspect UI state, interact with visible elements, and collect debugging evidence through one CLI. It is built around token-efficient accessibility snapshots, not pixel-first screenshots. Agents read compact UI trees, locate elements through refs like , perform touch and text actions, and capture screenshots, video, logs, network, CPU/memory/perf, crash-related logs, and React profiles only when evidence is needed. Agents can ingest the current docs from llms-full.txt. The installed CLI help remains authoritative for exact command syntax. Agentic QA And Development Quality Assurance: dogfood flows, validate PR builds, ch
| Stars | 2,396 |
| Forks | 130 |
| Language | TypeScript |
| Category | MCP Server |
| License | MIT |
| Quality Score | 68.728626892422/100 |
| Open Issues | 30 |
| Last Updated | 2026-06-03 |
| Created | 2026-01-30 |
| Platforms | cli, mcp, node |
| Est. Tokens | ~2706k |
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agent-device is CLI to control iOS and Android devices for AI agents. It is categorized as a MCP Server with 2.4k 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/callstackincubator/agent-device. The project has 2.4k stars and 130 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, tapflow, bytebot. 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: