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 bilibili · Agent Tool · ★ 123
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is carocut safe to install? View the security audit →
CaroCut AI 驱动的视频制作工作流系统，基于 OpenCode AI SDK 和 Remotion 构建。 简介 CaroCut 是端到端的自动化视频生产平台。通过多 Agent 协作（orchestrator + planner/media/builder/reviewer），将原始素材（PDF、图片、文本）转化为专业视频。 核心特性： 多 Agent 协作：5 个专业 Agent，12 个 Skill，职责隔离 8 步标准工作流：素材分析 → 策划 → 脚本润色 → 视觉素材 → 音频素材 → 资产管道 → 组件实现 → 预览渲染 自动化环境初始化：Bootstrap 一次，多项目共享 Remotion 模板和浏览器（session 创建时自动兜底） Remotion 驱动：React 组件编程式生成视频，逐帧精确控制 断点续做： 状态持久化，支持中断恢复和增量修改 案例： 从极简主义到极繁主义 - 日式网页为何如此花哨？ 【论文讲解】Bézier Splatting for Fast and Differentiable Vector Graphics Rendering 调度架构 ┌─────────┐ │ User │ └────┬────┘ │ request / confirm ▼ ┌───────────────────┐ │ Orchestrator │ │ │ │ · 读取进度状态 │ │ · 调度 subagent │ │ · 验证产出物 │ │ · 更新 progress │ └──┬────┬────┬───┬──┘ │ │ │ │ dispatch │ │ │ │ callback ┌───────────────┘ │ │ └───────────────┐ │ ┌──────────┘ └──────────┐ │ ▼ ▼ ▼ ▼ ┌─────────┐ ┌───────
| Stars | 123 |
| Forks | 12 |
| Language | TypeScript |
| Category | Agent Tool |
| Quality Score | 60.2892304310514/100 |
| Last Updated | 2026-04-20 |
| Created | 2026-03-20 |
| Platforms | node |
| Est. Tokens | ~84k |
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carocut is Multi-Agent 的视频制作助手，基于 OpenCode AI SDK 和 Remotion 构建。. It is categorized as a Agent Tool with 123 GitHub stars.
carocut is primarily written in TypeScript. It covers topics such as ai-agent, ai-tools, ai-video-editor.
You can find installation instructions and usage details in the carocut GitHub repository at github.com/bilibili/carocut. The project has 123 stars and 12 forks, indicating an active community.
The top alternatives to carocut on Agent Skills Hub include CapCut-Pro-Unlocker-2026, CapCut-Pro, Agentfy. 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: