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git clone https://github.com/alibaba-damo-academy/FunClip.gitTaken from the project's README on GitHub. Check the security audit before running it.
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. Full audit →
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 | 6,481 |
| Forks | 769 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 67/100 |
| Open Issues | 17 |
| Last Updated | 2026-11-01 |
| Created | 2023-05-17 |
| Platforms | cli, python |
| Est. Tokens | ~16k |
「简体中文 | English」 ⚡ Open-source, accurate and easy-to-use video clipping tool 🧠 Explore LLM based video clipping with FunClip What's New ｜ On Going ｜ Install ｜ Usage ｜ Community FunClip is a fully open-source, locally deployed automated video clipping tool. It leverages Alibaba TONGYI speech lab's open-source FunASR Paraformer series models to perform speech recognition on videos.
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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: