QiYongchuan

QiYongchuan / video-chapter-splitter

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基于字幕语义自动识别章节边界，精准切割视频的 Claude Code Skill

23
5
100% credibility
Found Mar 05, 2026 at 23 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

A tool that transcribes video speech into timed notes for users to define chapters and then splits the video into those segments.

How It Works

1
🔍 Find the splitter

You discover a simple tool that turns long videos into short, organized chapters by understanding the spoken words.

2
📥 Set it up

You get the tool on your computer and make sure everything is ready to go with a few quick preparations.

3
🎥 Choose your video

Pick the video file you want to break into chapters, like a tutorial or talk.

4
📝 Get the word timeline

The tool listens to your video and creates a friendly list of all the words spoken, with start and end times for each part.

5
✏️ Plan your chapters

Review the word list and mark where each new topic or section begins and ends, adding simple titles.

6
✂️ Slice it up

Run the tool again to neatly cut your video into those exact chapter pieces.

✅ Chapters ready!

You now have a folder of short, standalone video clips, each focused on one clear topic, perfect for sharing or rewatching.

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AI-Generated Review

What is video-chapter-splitter?

This Python tool splits long videos into semantic chapters by extracting subtitles with local Whisper transcription, then letting you define boundaries in a simple JSON file based on content cues like "next case" or "summary." It solves the pain of manually scrubbing timelines for precise cuts, outputting clean MP4 clips via FFmpeg with frame-accurate seeking. Run it via CLI: extract subtitles first, tweak chapters.json, then split.

Why is it gaining traction?

Unlike fixed-interval splitters, it leverages subtitle semantics for natural breaks, paired with Whisper's offline accuracy and FFmpeg's output-seeking precision—no drift or cloud dependency. Built as a Claude code skill in minutes, it demos claude code cli power, claude code skills for quick prototypes, and claude github integration potential, outpacing claude code vs codex in practical video tasks. Devs dig the minimal setup: pip install openai-whisper, grab FFmpeg, and go.

Who should use this?

Video educators chopping lectures into digestible segments, YouTubers prepping chaptered uploads, or data scientists segmenting training footage by topic. Podcasters turning talks into clips, or indie devs automating content pipelines without GUI bloat.

Verdict

Grab it for quick experiments—solid docs and MIT license make claude code download/install a breeze, even claude code kostenlos. At 17 stars and 1.0% credibility, it's an early prototype lacking tests or auto-chapter gen; fork and polish for production.

(178 words)

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