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video-understanding

video-understanding is a skill for Claude Code from zenstory-ai/video-recap-skills. It costs 126 tokens per session (2,455 once invoked), scanned A, a copy of video-understanding, MIT.

A video analysis tool that turns a video into a structured index of scenes, spoken words, visual observations, quiet sections, and a writing brief. ASR means automatic speech recognition, which converts speech in the video into timestamped text.

In plain words
What is it for?
Use it to detect scenes, transcribe dialogue, inspect representative frames, find silent sections, combine these findings into a timeline, and prepare a narration brief.
Why use it?
It gives later editing and writing steps a time-based understanding of what happens, what is said, and where meaningful visual or audio changes occur.

Skill for Claude Code

Written for Claude Code: user-invocable in frontmatter.

Part of the video-recap-skills plugin — 7 skills shipped together

not rated 592repo +7 changed 12d ago A scan SkillSpector: pass 126 tokens copy · 100% MIT

Good fit Use it to detect scenes, transcribe dialogue, inspect representative frames, find silent sections, combine these findings into a timeline, and prepare a narration brief.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/zenstory-ai/video-recap-skills/video-understanding
View source ↗ zenstory-ai/video-recap-skills zenstory.ai
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add zenstory-ai/video-recap-skills --skill video-understanding
Clone the repo
git clone --depth 1 https://github.com/zenstory-ai/video-recap-skills

Made for: Claude Code.

Or install video-recap-skills, the plugin that ships this one along with the rest of its 7 skills.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for video-understanding

README.md
[![agentmods](https://agentmods.dev/badge/skills/zenstory-ai/video-recap-skills/video-understanding/github.svg)](https://agentmods.dev/skills/zenstory-ai/video-recap-skills/video-understanding)
Your own site
<a href="https://agentmods.dev/skills/zenstory-ai/video-recap-skills/video-understanding"><img src="https://agentmods.dev/badge/skills/zenstory-ai/video-recap-skills/video-understanding/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for video-understanding

Your own site · 80×15
<a href="https://agentmods.dev/skills/zenstory-ai/video-recap-skills/video-understanding"><img src="https://agentmods.dev/badge/skills/zenstory-ai/video-recap-skills/video-understanding.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 126 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,455 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin 100% copy Near-identical to another mod in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00126 $0.02455
Opus 5.5 $0.00050 $0.00982
Sonnet 5.5 $0.00025 $0.00491
Haiku 4.5 $0.00013 $0.00246

Measured 12d ago against content hash e0a9612667bf, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

video-understanding scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.

The scan reads SKILL.md. This mod also ships 21 executable files (scripts/agent_text.py, scripts/asr_timing_evidence.py, scripts/asr.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

This is a copy

100% identical to video-understanding — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

skills/video-understanding/SKILL.md · 107 lines

How it starts

The opening of the file, as written. The whole thing — 107 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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，以素材观察员 / 场记的角色记录证据：

  • 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 写粗分段对白 asr_result.json，并写 asr_timing_evidence.json 说明可用性、有限时间精度与文本修正来源。
  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。未设置 key 时重跑会复用已缓存的转写、画面分析、概览与故事索引（key 决定的默认 endpoint 不参与比对）；需要请求模型的 consolidation 记为 skipped_no_key，不发请求。缓存对不上（例如复制 work_dir 时没保留文件时间）而已有转写时，运行停下并保留转写：用 cp -p / cp -Rp / rsync -t 保留时间重新复制，或设置 key 后重跑（会重新转写）。不要用 --skip-asr 绕过，它会把现有转写替换成 []。

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。

远程调用与本地读取

ASR 把分窗音频发给 MiMo ASR；逐场景 VLM 把代表帧、帧时间和分析提示发给 MiMo；启用 --mimo-video-overview 时，还会把按场景截取的视频片段发给 MiMo 视频模型。故事索引汇总和可选 ASR 清洗发送的是画面描述、转写与背景资料文字。

请求使用 MiMo 的 chat 接口。MIMO_API_URL 可指定地址；ASR 与视频概览可分别用 MIMO_ASR_API_URL、MIMO_VIDEO_API_URL 覆盖，凭据同样支持各自的 key。未覆盖时，按量 key 使用 https://api.xiaomimimo.com/v1，Token Plan key 使用 MIMO_TOKEN_PLAN_CLUSTER 选择的集群。逐场景帧分析与汇总使用 MIMO_API_KEY。

--skip-asr 只关闭音频转写，--no-consolidate 只关闭索引汇总；它们不会关闭 VLM。需要只在本地处理已有产物时，使用 --brief-only 或 --edited-storyboard-only，这两条路径不调用 API。

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。

4. 运行命令

python3 scripts/understand.py <video> --work-dir <work_dir> [选项]
选项 默认 作用
<video> 必填 源视频
--work-dir 必填 产物目录；不存在时创建
--context "..." 空 补充给 VLM 的简短上下文（节目名、角色名），与 background_research.json 合并
--scene-threshold 0.1 场景检测阈值
--style 纪录片 写进创作简报的解说风格
--edit-mode full|cut 不设 写进简报的 recap 模式；cut 时按剪后时长估算旁白预算，已有 edited_source.mp4 时句末锚点改用剪后时间
--target-duration 不设 写进简报的 cut 目标时长；尚无 clip_plan_validated.json 时用它估算旁白预算
--skip-asr 关 不转写对白，把 asr_result.json 写成 []（已有转写会被覆盖），ASR 证据标为显式跳过
--mimo-video-overview 关 按场景块运行 MiMo 视频概览，并作为逐场景主描述
--force 关 忽略缓存，全部重算
--brief-only 关 只用现有产物重建 agent_narration_brief.md，不抽帧、不调 API
--edited-storyboard-only 关 只按 clip_plan_validated.json 写剪后时间线 storyboard/edited_storyboard.*，并在已有的 agent_narration_brief.md 顶部加 storyboard 指引；多源计划（带 sources）从各来源 source_work_dir 的 frames/ 按其 frames_manifest.json 的 fps 取帧，tile 标 S1/S2…；不抽帧、不调 API，故事板生成失败只记日志。与 --brief-only 互斥
--consolidate / --no-consolidate 开 生成全局故事索引 understanding_index.*
--consolidate-asr 关 另外清洗 ASR 文本，写 asr_clean.json

Read the full file on GitHub · 107 lines

Files

What ships with it

24 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 12d ago Changed · +8 lines e0a9612667bf
  2. 13d ago Changed · -4 lines 67bd01f8a7b7
  3. 24d ago Changed · +25 lines e62600a4194f
  4. 1mo ago Changed · -6 lines 3cd5bc643ea2
  5. 1mo ago Changed · +8 lines 01aae7b1f69d
  6. 1mo ago First seen · 76 lines · 126 tokens per session scan A 86c395f032d9

Subscribe to this mod's changes

video-understanding is a skill published in the GitHub repository zenstory-ai/video-recap-skills (592 stars, last pushed 7d ago), licensed MIT. It adds 126 tokens to every session and 2,455 once invoked, about $0.0005 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to video-understanding, differing in 0 lines, and is treated as a copy.

Claude CodeCreative and mediaResearchai-agentasrttsvideo-narrationvideo-recap
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