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git clone https://github.com/FBddcz/embodied-jev.gitTaken from the project's README on GitHub.
None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours.
The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.
| Stars | 333 |
| Forks | 14 |
| Language | Python |
| Category | AI Tool |
| License | MIT |
| Quality Score | 58/100 |
| Open Issues | 4 |
| Last Updated | 2026-09-22 |
| Created | 2026-09-20 |
| Platforms | python |
| Est. Tokens | ~16k |
🤖 EmbodiedJev · 行知 把具身 AI 实验，搬到你的浏览器里。 📦 开箱体验三大仿真任务 · 🧠 本地小模型 / 云端 API · 🎮 看得见的每一步决策 无需机械臂，无需先训练模型。跟着步骤启动，在自己的电脑上体验「观察 → 决策 → 执行 → 反馈」。 🚀 快速上手 · 🧠 接入模型 · 📊 实测结果 · 🤝 一起维护 行而有据，知而能行。 如果这个项目帮你迈出了具身 AI 的第一步，欢迎点一颗 ⭐ Star，让更多人一起玩、一起改！ ✨ 打开行知，你能做什么？ 📦 先玩起来，再接模型：内置规则基线，无需 API Key、GPU 或模型权重，即可运行三个任务。首次安装依赖需要联网。 🦾 真实物理交互：MuJoCo + Franka Panda，夹爪接触、抓取、搬运和放置都有物理反馈。 🧠 模型入口放在界面里：可接 TypeSafe...
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Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. 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: