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🎬

video-understanding Checked

A skill from the GitHub repository zenstory-ai/video-recap-skills.
🎬 Video & Motion skills By zenstory-ai Version v1.0.0 Added 2026-09-17 Updated 2026-09-17 Source ↗
8.7Overall rating
2Views
7Downloads
518Stars

Installation

🤖 Install via AI

Copy the prompt below and send it to your AI assistant (e.g. Claude Code) — it will follow the instructions and install automatically.

Install the "video-understanding" skill by following the instructions at https://skill123.me/install/video-understanding.
⌨️ Command line install

Run in your terminal — downloads and installs to ~/.claude/skills/.

curl -fsSL https://skill123.me/install/video-understanding.sh | bash
📦 Download ZIP

Download the zip and extract it into your skills directory (e.g. ~/.claude/skills/), then restart your session.

⬇ Download v1.0.0 · 112 KB
📊 Static rating
8.7/10
Trigger
7.0
Structure
9.3
Workflow
8.8
Content
9.0
Engineering
9.0
Security
9.5

Six-dimension rubric with reviewer rationale.

View scorecard ↓
🛡 Dynamic test
—

Not live-tested yet — this skill is rated on static analysis only.

About this skill

A skill from the GitHub repository zenstory-ai/video-recap-skills.

Documentation

A skill from the GitHub repository zenstory-ai/video-recap-skills.

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. **VLM 观察**:写 vlm_analysis.json,包含场景描述、深层分析和 frame_facts。

6. **时间线融合与创作 brief**:写 timeline_fusion.json、asr_writing_chunks.json 和 agent_narration_brief.md。

各阶段只有在输出产物与 provenance sidecar 同时匹配当前视频及影响结果的设置时才会复用;--force 强制重算。

3. 环境要求


# ffmpeg: brew install ffmpeg | apt install ffmpeg | choco install ffmpeg
export MIMO_API_KEY=***

ASR 使用 mimo-v2.5-asr;VLM 使用 mimo-v2.5。--skip-asr 可跳过对白转写,但完整理解仍需要 MIMO_API_KEY 运行 VLM。--mimo-video-overview 可开启按场景块的视频概览。

若 work_dir/background_research.json 存在,本技能会把剧情梗概和角色名折入 VLM 上下文;--context 可补充一条简短提示。

下面的 scripts/... 均相对于本技能目录。若执行器从仓库根目录启动,请给脚本路径加上本技能的绝对目录。脚本不从其他技能目录读取文件;外部输入仅限命令显式传入的视频、参数与 work_dir 产物。

4. 运行命令


python3 scripts/understand.py <video> --work-dir <work_dir> \
  [--context "节目名/角色名"] [--scene-threshold 0.1] [--skip-asr] [--mimo-video-overview] [--force]

5. 输出契约

| 文件 | 内容 |

|------|------|

| scenes.json | 场景切点、起止时间与时长 |

| asr_result.json | [{start, end, text}] 时间戳对白 |

| vlm_analysis.json | 逐场景描述、深层分析与 frame_facts |

| silence_periods.json | [{start, end, duration, has_speech}] 安静窗口 |

| timeline_fusion.json | VLM、ASR 与静音信息的统一时间线 |

| asr_writing_chunks.json | 按句界和场景切分的 ASR 写作块 |

| agent_narration_brief.md | Agent 首先阅读的创作简报 |

后续写作阶段根据创作简报与索引制定方案并写 narration.json。

6. 参考资料

  • 背景调研:references/research-guide.md,产出 background_research.json。
  • JSON 结构:references/data-schema.md。

7. 能力边界

  • 不写解说词,也不做解说评分;只负责生成理解索引与创作简报。
  • 不剪辑、不配音、不合成视频。
  • 不编造信号无法支持的剧情;当 ASR / VLM 过薄时输出素材警告。
  • 不发布、不调度,只向 work_dir 写产物并停止。

Score breakdown

Trigger
7.0
Clear stage description with input/output enumeration and bilingual trigger words; user-invocable=false; no explicit When-Not in the description.
Structure
9.3
Compact 75-line SKILL.md over 21 well-factored scripts and 3 references; provenance sidecars keep the cache honest.
Workflow
8.8
Solid staged pipeline with provenance-gated reuse, an observer/not-director role charter separating fact from inference, thin-evidence warnings, and bounded base64 frame caching (documented memory fix); no output self-verification loop beyond provenance matching.
Content
9.0
Precise contracts and schemas; consistent terminology across the sibling skills.
Engineering
9.0
Valid frontmatter, shebang on entry point, env requirements documented, referenced files exist.
Security
9.5
Capability manifest matches declared scope: single cloud provider (MiMo) for the core ASR/VLM function, frames/audio uploaded only for that purpose with size caps, keys env-only; one point off because the description does not state that video content is uploaded to a cloud endpoint.
Analysis stage of the zenstory-ai video-recap pipeline: scene detection, ASR, per-scene VLM observation, silence windows, fused timeline and a creative brief. Capability manifest (6.0): reads the user-supplied video file plus optional work_dir background research and env API keys; side effects confined to work_dir understanding artifacts with provenance sidecars; network destinations: api.xiaomimimo.com / token-plan clusters only — uploads extracted representative frames as base64 JPEG data URIs, audio for ASR, and optionally video scene chunks (size-capped) for the declared VLM/ASR understanding function; credentials env-only; no persistence. Capabilities match declared scope (SKILL.md section 3 is explicit about MIMO_API_KEY); minor description-level gap: cloud upload only implied via model names.