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 Ceeon · Claude Skill · ★ 1.8k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is videocut-skills safe to install? View the security audit →
Featured in: AI Video Generation & Editing: Seedance, Kling & Subtitles
Videocut Skills 用 Claude Code Skills 构建的视频剪辑 Agent，专为口播视频设计 为什么做这个？ 剪映的"智能剪口播"有两个痛点： 无法理解语义：重复说的句子、说错后纠正的内容，它识别不出来 字幕质量差：专业术语（Claude Code、MCP、API）经常识别错误 这个 Agent 用 Claude 的语义理解能力解决第一个问题，用自定义词典解决第二个问题。 效果演示 输入：19 分钟口播原片（各种口误、卡顿、重复） 输出： 自动识别 608 处问题（静音 114 + 口误/重复 494） 剪辑后视频 72MB 全程 AI 辅助，人工只需确认 核心功能 快速开始 安装 Skills 配置 API Key 安装环境 打开 Claude Code，输入： AI 会自动： 检查 Python、FFmpeg、Node.js 安装 FunASR（口误识别模型，约 2GB） 安装 Whisper large-v3（字幕模型，约 3GB） 使用流程 ┌─────────────────────────────────────────────────────────┐ │ /videocut:安装 → 首次使用，安装环境和模型 │ └─────────────────────────────────────────────────────────┘ ↓ ┌─────────────────────────────────────────────────────────┐ │ /videocut:剪口播 视频.mp4 │ │ │ │ 1. 提取音频 → 上传云端 │ │ 2. 火山引擎转录 → 字级别时间戳
| Stars | 1,758 |
| Forks | 272 |
| Language | JavaScript |
| Category | Claude Skill |
| Quality Score | 58.3900398810321/100 |
| Open Issues | 17 |
| Last Updated | 2026-05-28 |
| Created | 2026-01-14 |
| Platforms | claude-code, node |
| Est. Tokens | ~5k |
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videocut-skills is 用 Claude Code Skills 做的视频剪辑 Agent. It is categorized as a Claude Skill with 1.8k GitHub stars.
videocut-skills is primarily written in JavaScript.
You can find installation instructions and usage details in the videocut-skills GitHub repository at github.com/Ceeon/videocut-skills. The project has 1.8k stars and 272 forks, indicating an active community.
The top alternatives to videocut-skills on Agent Skills Hub include claude-code-showcase, claude-codex-settings, digital-marketing-pro. 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: