video-chapters

video-chapters is a skill for Claude Code, Codex from ZJU-REAL/Easel. It costs 158 tokens per session (1,090 once invoked), scanned A, original, Apache-2.0.

A chapter and timestamp generator for longer videos. It creates a clickable list of topic sections, such as the chapters shown in a YouTube description.

In plain words
What is it for?
Use it to produce chapter lists for Bilibili or YouTube tutorials, reviews, lectures, livestream recordings, and other long videos.
Why use it?
It saves you from watching through the recording and manually finding and naming every topic change.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Needs its repository: it runs a file that does not travel with it, so clone the repository first. The line is python skills/shared/scripts/asr.py transcribe -i <视频> --format json \.

Good fit Use it to produce chapter lists for Bilibili or YouTube tutorials, reviews, lectures, livestream recordings, and other long videos.

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/ZJU-REAL/Easel
agentmods
npx agentmods add skills/zju-real/easel/video-chapters

Made for: Claude Code, Codex.

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-chapters

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

Your own site · 80×15
<a href="https://agentmods.dev/skills/zju-real/easel/video-chapters"><img src="https://agentmods.dev/badge/skills/zju-real/easel/video-chapters.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 158 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,090 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 original No closer match found 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 $0.00158 $0.01090
Opus 5 $0.00079 $0.00545
Sonnet 5 $0.00032 $0.00218
Haiku 4.5 $0.00016 $0.00109

Measured 8d ago against content hash 2af4df61055c, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

video-chapters 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 8d ago.

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.

skills/openclaw/video-chapters/SKILL.md · 75 lines

What it actually says

视频章节 / 时间戳目录

给中长视频生成章节时间戳目录(B站/YouTube 描述区可用)。带时间轴转录走 asr.py章节切分与命名由你(LLM)完成

出成篇图文见 video-to-article;出字幕见 auto-subtitle;切成短视频见 video-highlights

输入

字段 必填 说明
视频文件 中长视频(教程/评测/讲座/直播回放;没给就问)
目标平台 B站 / YouTube / 通用(影响格式与措辞)
章节数 期望章节数(默认按内容自然划分,通常 5-12 段)

输出(outputs/主题名/

  • chapters.txt — 时间戳目录(每行 mm:ss 章节名,可直接贴描述区)
  • chapters.json — 结构化(start 秒 + 标题),供程序化使用
  • transcript.txt — 转录原文(备查)

执行步骤

脚本路径(相对项目根):skills/shared/scripts/asr.py

1. 带时间轴转录

python skills/shared/scripts/asr.py transcribe -i <视频> --format json \
  -o outputs/主题名/transcript.json

(首次跑 ASR 需外网代理下模型,见 auto-subtitle 前置说明。)

2. 划分章节(你来做)

读 transcript.json(每段带 start/end),按话题转折划分章节:

  • 找主题切换点作为章节边界(不是均匀切时间,而是按内容)。
  • 第一章从 00:00 开始(平台要求,否则章节功能不生效)。
  • 每章名 6-16 字,动词开头或点明看点(如"实测续航翻车了""3 分钟教你上手"),不写"第一部分"。
  • 章节数适中(太碎观众烦,太粗没用),一般 5-12 段;短视频(<3 分钟)通常不需要章节。
  • 章节最短 ≥10 秒(平台 YouTube 要求相邻章节间隔 ≥10s)。

3. 输出目录

chapters.txt(每行 mm:ss 章节名,首行必须 00:00):

00:00 开场|今天聊什么
01:24 第一个坑:xxx
03:50 实测环节
...

同时写 chapters.json[{"start": 0, "title": "开场|今天聊什么"}, ...]

平台格式差异

  • YouTube:贴在视频描述区,首个必须 0:00,≥3 章、每章 ≥10s 自动生效。
  • B站:作为"看点/进度条章节"或分P说明,格式 mm:ss 标题
  • 通用chapters.txt 通用可读。

规则

  1. 章节边界按话题转折定,不是均匀切时间。
  2. 首章必须 00:00;相邻章节间隔 ≥10s。
  3. 章节名点明看点、简洁有吸引力,不用"第一部分"这类空名。
  4. 不编造视频没讲的内容;依据转录如实划分。
  5. 产物统一进 outputs/主题名/

参考来源

章节时间戳是 YouTube/B站 提升完播与检索的标准做法(首章 0:00、≥10s 间隔为平台硬规则)。 转录用 faster-whisper(asr.py)出时间轴,话题切分交给 LLM——确定性 IO 与语义划分分层。

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. 8d ago First seen · 75 lines · 158 tokens per session scan A 2af4df61055c

Subscribe to this mod's changes

video-chapters is a skill published in the GitHub repository ZJU-REAL/Easel (841 stars, last pushed yesterday), licensed Apache-2.0. It adds 158 tokens to every session and 1,090 once invoked, about $0.0008 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.

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