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 cartesiancs · Agent Tool · ★ 640
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is cartcut safe to install? View the security audit →
CartCut The finest AI video editor English | 한국어 View Demo · Report Bugs · Download · Suggest Features Video editing software designed for motion effects and versatility. In addition to essential features like basic cut editing, animation, sound mixing, external library extensions, project management, and text editing, our software offers a wide range of powerful tools. It also supports layer-based editing, which differs from traditional track-based editing. This approach makes it easier to apply multiple effects to individual assets, providing greater flexibility and creative control. [!IMPORTANT] 📢 <str
| Stars | 640 |
| Forks | 59 |
| Language | TypeScript |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 70.9837680108916/100 |
| Open Issues | 2 |
| Last Updated | 2026-09-12 |
| Created | 2022-08-26 |
| Platforms | node |
| Est. Tokens | ~14k |
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cartcut is Video Editor for AI agents, built on the belief that open source can beat commercial tools. It is categorized as a Agent Tool with 640 GitHub stars.
cartcut is primarily written in TypeScript. It covers topics such as editor, electron, ffmpeg.
You can find installation instructions and usage details in the cartcut GitHub repository at github.com/cartesiancs/cartcut. The project has 640 stars and 59 forks, indicating an active community.
cartcut is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to cartcut on Agent Skills Hub include mkeditor, ChattyPlay-Agent, project-raven. 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: