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 AppiumTestDistribution · MCP Server · ★ 91
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is AppClaw safe to install? View the security audit →
AppClaw AI-powered mobile automation agent for Android and iOS. Tell it what to do in plain English — it figures out what to tap, type, and swipe. Prerequisites Node.js 18+ Device connected — USB, emulator, or simulator LLM API key from any supported provider (Anthropic, OpenAI, Google, Groq, or local Ollama) Installation From npm Create a file in your working directory: Local development Edit based on your preferred mode: Vision mode (recommended) Screenshot-first mode using Stark (df-vision + Gemini) for element location. Requires a Gemini API key. env LLMPROVIDER=gemini LLMAPIKEY=your-gemini-api-key LLMMODE
| Stars | 91 |
| Forks | 17 |
| Language | TypeScript |
| Category | MCP Server |
| License | Apache-2.0 |
| Quality Score | 69.3380732598876/100 |
| Open Issues | 4 |
| Last Updated | 2026-07-07 |
| Created | 2026-03-23 |
| Platforms | mcp, node |
| Est. Tokens | ~17k |
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AppClaw is AI-powered mobile automation agent — describe what you want in plain English, AppClaw reads the screen, reasons, and acts. LLM-agnostic, open-source, zero telemetry.. It is categorized as a MCP Server with 91 GitHub stars.
AppClaw is primarily written in TypeScript. It covers topics such as agent, ai-agent, ai-agents.
You can find installation instructions and usage details in the AppClaw GitHub repository at github.com/AppiumTestDistribution/AppClaw. The project has 91 stars and 17 forks, indicating an active community.
AppClaw is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to AppClaw on Agent Skills Hub include AppClaw, agnix, droidmind. 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: