Agent skill · Media & Video

video-understanding

把视频分析为结构化理解索引:场景检测、ASR 转写、逐场景 VLM 观察、静音窗口、融合时间线和写作 brief。 用于理解、索引或总结视频,也作为后续创作前的分析阶段。输入视频文件;输出 scenes.json、 asr_result.json、vlm_analysis.json、silence_periods.json、timeline_fusion.json、agent_narration_brief.md。 触发词:视频理解、视频分析、视频索引、video understanding、analyze video、看懂视频。

pitechen592★ · +23/wk · 2 repos on radarProfile →
claude-codeships scriptsMIT
Install
npx skills add worldwonderer/video-recap-skills --skill video-understanding --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 26
SKILL.md size: 2 KB
Bundled scripts: yes
Path: skills/video-understanding/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 423 · +23 this week
Language: Python
Read our review of the source →

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

## 1. 定位 本技能把源视频转成 Agent 与下游阶段可读取的理解索引。它的创作角色是**素材观察员 / 场记**,不是导演: - 先观察,再解释;事实与推断分开。 - 除了“发生了什么”,还要让下游看见知识、权力、目标、关系或情绪在哪一刻变化。 - 标出由谁的 POV 承载变化、哪个反应或表演不可替代,以及哪里存在完整台词/动作的自然剪辑边界。 - 证据不足时保留不确定性,不制造戏剧结论。 ## 2. 处理阶段 1. **场景检测**:写 `scenes.json`,包含切点、时长和废片段过滤结果。 2. **抽帧**:为视觉分析提取代表帧。 3. **ASR**:通过 `mimo-v2.5-asr` 写时间戳对白 `asr_result.json`。 4. **静音检测**:写 `silence_periods.json`,标注安静窗口与 `has_speech`。 5. **V

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About this skill
What does the video-understanding skill do?

把视频分析为结构化理解索引:场景检测、ASR 转写、逐场景 VLM 观察、静音窗口、融合时间线和写作 brief。 用于理解、索引或总结视频,也作为后续创作前的分析阶段。输入视频文件;输出 scenes.json、 asr_result.json、vlm_analysis.json、silence_periods.json、timeline_fusion.json、agent_narration_brief.md。 触发词:视频理解、视频分析、视频索引、video understanding、analyze video、看懂视频。

How do I install it?

Run `npx skills add worldwonderer/video-recap-skills --skill video-understanding --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From worldwonderer/video-recap-skills, a repository with 423 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

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