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 YeJe-cpu · Agent Tool · ★ 75
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is SeeCut safe to install? View the security audit →
SeeCut · 网感口播精剪 An AI editor that watches its own cut — see, cut, loop. 中文 · English · 日本語 数字人 / 真人口播 → AI 自动精剪成"网感"动效短视频 → 可选推成剪映分层草稿微调。 一个给 coding agent（Claude Code 等）用的 skill：它自己理解素材、自己去找真证据、自己编排和渲染，再用 AI 看视频给自己质检、自己迭代，最后交你肉眼终审。 两条都有声音，点开即可播放。右边的画面由 agent 全程自动完成（真证据截图、卡片、手绘圈注、人像在全屏和小窗之间调度），最后只在剪映里调了音量。原始文件在 Releases · demo-v1。 "精剪"不是去气口的粗剪；"口播"是有真人或数字人出镜；"网感"是画面有料、动效跟着每句话走。 亮点 See：让 AI 真正"看"视频。 Claude、GPT 这类模型通常要先把视频抽成几张图片再看，看不出动效和节奏。SeeCut 接入 Gemini（通过 Antigravity CLI）直接看视频，素材理解和质检都靠它。 看得准：评委是校准过的。 同一条视频换个提示词，AI 能从"标准"说成"土味"。我们用答案已知的片子测评委，发现"打绝对分"不可靠、"两条比哪条好"才可靠，所以质检用两两对比，交换位置各跑一次都赢才算。 Loop：自己改、自己挑。 每版先过硬伤检查，再和上一版两两对比，赢了才采纳；最多 3 版，每版必须有肉眼可见的进步。眼睛准了，循环才会越改越好，而不是越改越偏。 找真证据。 口播提到什么，就去截真的网页、界面、案例当画面证据，禁止用假界面凑满画面。没有额外 B-roll 也能做得有料。 有章法，不是堆特效。 一套成体系的风格规范：设计令牌 → 组件库 → 编排决策表（"这句话是什么功能 → 画面怎么处理"）→ 逐镜施工单，层层有据。 数字人也给了实打实的做法。 HeyGen AV5 数字分身出人像，配音不用 HeyGen 内置音色（偏假），改用豆包语音合成，demo 里的声音就是这么来的。脚本和步骤都在 digital-human/。 能在剪映里微调。 交成片的同时交分层工程包；装了剪映草稿引擎还能直接推成剪映里分轨可改的草稿，音效也能自动配。 最快跑通 准备一个文件夹：一段 20-30 秒竖屏半身口播（自己拍的就行）+ 3-6 张口播里提到的东西的截图（可选）。 装好下面
| Stars | 75 |
| Forks | 14 |
| Language | JavaScript |
| Category | Agent Tool |
| Quality Score | 57.1023133533595/100 |
| Last Updated | 2026-09-27 |
| Created | 2026-09-25 |
| Platforms | claude-code, gemini, node |
| Est. Tokens | ~14k |
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SeeCut is An AI editor that watches its own cut: talking-head / AI-avatar video → auto-edited short video. 网感口播精剪：数字人/真人口播 → AI 自动精剪成网感动效短视频 → 可选剪映分层草稿. It is categorized as a Agent Tool with 75 GitHub stars.
SeeCut is primarily written in JavaScript. It covers topics such as agent-skill, ai-avatar, ai-video.
You can find installation instructions and usage details in the SeeCut GitHub repository at github.com/YeJe-cpu/SeeCut. The project has 75 stars and 14 forks, indicating an active community.
The top alternatives to SeeCut on Agent Skills Hub include kinocut, mcp-video, fullstack-mkt-skills. 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: