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 modelscope · Agent Tool · ★ 6.3k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is FunClip safe to install? View the security audit →
「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. Then, users can freely choose text segments or speakers from the recognition results and click the clip button to obtain the video clip corresponding to the selected segments (Quick Experience Modelscope⭐ [Hugging
| Stars | 6,317 |
| Forks | 752 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 67.4864488538126/100 |
| Open Issues | 13 |
| Last Updated | 2026-09-16 |
| Created | 2023-05-17 |
| Platforms | cli, python |
| Est. Tokens | ~16k |
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FunClip is FunASR-powered video transcription, subtitle generation, and LLM-assisted clipping tool with a local Gradio UI.. It is categorized as a Agent Tool with 6.3k GitHub stars.
FunClip is primarily written in Python. It covers topics such as ai-tools, ai-video-editing, asr.
You can find installation instructions and usage details in the FunClip GitHub repository at github.com/modelscope/FunClip. The project has 6.3k stars and 752 forks, indicating an active community.
FunClip is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to FunClip on Agent Skills Hub include autoclip, awesome-openclaw-usecases-zh, bailing. 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: