video-recap

video-recap is a skill for Claude Code from zenstory-ai/video-recap-skills. It costs 134 tokens per session (4,878 once invoked), scanned A, a copy of video-recap, MIT.

An end-to-end workflow for turning a video into a Chinese narrated recap. It coordinates video analysis, story planning, editing, voice generation, and final audio and subtitle assembly.

In plain words
What is it for?
Use it for narrated summaries of films, television, documentaries, short dramas, educational videos, and other MP4, MOV, MKV, or WebM files.
Why use it?
It connects the separate stages of making a recap so their files and timing information can be reused when work is resumed.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Needs its repository: it runs a file that does not travel with it, so clone the repository first. The line is python3 tools/measure_subtitle.py <video>.

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

Good fit Use it for narrated summaries of films, television, documentaries, short dramas, educational videos, and other MP4, MOV, MKV, or WebM files.

Compare 6 skills from other repositories ↓
Install

Getting it into your agent

It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.

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

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-recap

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

Your own site · 80×15
<a href="https://agentmods.dev/skills/zenstory-ai/video-recap-skills/video-recap"><img src="https://agentmods.dev/badge/skills/zenstory-ai/video-recap-skills/video-recap.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 134 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 4,878 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 $0.00134 $0.04878
Opus 5.5 $0.00054 $0.01951
Sonnet 5.5 $0.00027 $0.00976
Haiku 4.5 $0.00013 $0.00488

Measured 12d ago against content hash e321596c52db, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

video-recap 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 25 executable files (assets/dashboard/app.js, assets/dashboard/views.js, scripts/dashboard_server.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-recap — 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-recap/SKILL.md · 256 lines

How it starts

The opening of the file, as written. The whole thing — 256 lines — stays where its author put it; the contents beside it link to each section on GitHub.

1. 定位与流程

本技能是五个独立技能的轻量编排器。各技能只通过 work_dir 中的 JSON / MP4 产物通信,不共享代码:

video-understanding ─▶ Agent 按 video-script 制定方案并写稿 ─▶ [video-cut] ─▶ video-voiceover ─▶ video-assemble

把成片拆成制作参考不是生产路径。用户要求时:对成片跑 video-understanding(建议 ASR_SEGMENT_SECONDS=5), 再用 video-reference 做 measure、标注、check、export,把导出的 production_reference.json 复制进下一次运行的 work_dir,或登记成资源库的 production_reference 模板、采纳后经 --project 绑定。recap 不会自动运行它,也不拿新成片与参考做比对。

流程支持断点续跑:写好 narration.json 后重复同一条命令即可继续。第二阶段会比对 recap_run_manifest.json 记录的源视频路径、文件大小/修改时间与运行参数,拒绝复用来自其他源视频或其他参数的旧工作目录;视频理解产物也只在来源一致时复用。 暂停时打印的续跑命令就是原命令:保留原来的写法,视频与路径参数转成绝对路径,补上 --work-dir 和来自环境变量的设置,从任何目录都能直接运行;同一份参数写在 manifest 的 argv。

画面流程 --edit-mode full|cut|dub 与声音策略 --audio-mode 是两个独立开关; narration 保留上述解说流程,source-mix 不做配音,adopted-packet-copy 冻结当前输入的已采用 AAC 音轨。 组合只有下面几条路径,其余组合在启动时直接报错:

输入 --edit-mode --audio-mode Agent 暂停点 流程 详见
单视频 full narration 1:narration.json 理解 → 写稿 → 校验 → 配音 → 合成 §4
单视频 cut narration 2:clip_plan.json,再对着成片写 narration.json 理解 → 剪辑 → 重建输出时间 brief → 写稿 → 配音 → 合成 §4
多视频 cut narration 2:同上,clip 必须带 source_id 逐源理解 → 剪辑 → 写稿 → 配音 → 合成 §4.3
单视频 full source-mix / adopted-packet-copy 无 直接合成当前整段 §4.7
单 / 多视频 cut source-mix / adopted-packet-copy 1:clip_plan.json 理解 → 剪辑 → 合成,不写稿 §4.7
单视频 dub narration 1:dub_script.json 英文转写 → 译稿 → 克隆音色整轨替换 §5
已剪好的母版 full narration + 三个采用 JSON 无 只做严格合成 下文

所有 full/cut 路径共用同一段收尾:(有旁白时)评审 → TTS → 合成 → 成片 QC。使用原声模式时读 references/audio-routing.md。

已有预制画面和本地采用的完整声音三件套时,可走严格 assembly-only 路径:

python3 scripts/recap.py picture.mp4 --edit-mode full --work-dir NEW_WORK \
  --output-dir DELIVERY \
  --tts-meta tts_meta.json \
  --narration-adoption narration_adoption.json \
  --audio-mix-adoption audio_mix_adoption.json

三个 JSON 参数必须同时出现。该入口只接受单视频、full、narration、音轨 0、新工作目录和未存在的 交付文件;不运行理解、写稿、解说评审、TTS、cut 或剪映导出。语义与媒体形状仍由 video-assemble 严格验证,recap 只核对子技能绑定记录引用的是同一批采用文件与母版路径,不把调用方 采用的声音或混音声明成自动创作或发布批准。详见 references/audio-routing.md。

这里的单视频是已经剪好的母版。重剪后可以复用未改动的 WAV 与 tts_meta.json,但必须按新母版 重新写混音采用文件里的落点与准备好的音床;衔接步骤见 references/audio-routing.md 的 “Keep adopted voice after a cut”。

Read the full file on GitHub · 256 lines

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 e321596c52db
  2. 13d ago Changed · -4 lines ef820d5e69e5
  3. 23d ago Changed · +2 lines dcee225f8a0e
  4. 24d ago Changed · +13 lines 940ba0b6621d
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  7. 1mo ago Changed · +13 lines a7544d01218f
  8. 1mo ago Changed · -36 lines a21736abcd6e
  9. 1mo ago Changed · +53 lines · +6 tokens per session b3fe4662ae26
  10. 1mo ago First seen · 217 lines · 128 tokens per session scan A 217913bc6f9e

Subscribe to this mod's changes

video-recap is a skill published in the GitHub repository zenstory-ai/video-recap-skills (592 stars, last pushed 7d ago), licensed MIT. It adds 134 tokens to every session and 4,878 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-recap, differing in 0 lines, and is treated as a copy.

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