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- Youtube Clipper Skill
- Youtube Clipper Skill
youtube-clipper-skill_skill
- Python
738
GitHub Stars
1
Bundled Files
2 months ago
Catalog Refreshed
4 months ago
First Indexed
Readme & install
Copy the install command, review bundled files from the catalogue, and read any extended description pulled from the listing source.
Installation
Preview and clipboard use veilstrat where the catalogue uses aiagentskills.
npx veilstrat add skill op7418/youtube-clipper-skill --skill youtube-clipper-skill- SKILL.md10.6 KB
Overview
This skill automates downloading, AI-driven chaptering, clipping, subtitle translation, and subtitle burning for YouTube videos. It produces 2–5 minute topic-focused clips with bilingual (English/Chinese) SRTs, hardcoded subtitle videos, and ready-to-use social summaries. The tool emphasizes precise chapter boundaries and efficient batch translation to minimize API usage.
How this skill works
The skill runs a six-stage pipeline: environment checks, video and subtitle download, AI analysis of subtitles to produce fine-grained chapters, user selection of chapters and options, concurrent clip processing (cut, extract/translate subtitles, merge bilingual SRT, burn subtitles, generate summary), and organized output packaging. It uses yt-dlp for downloads, FFmpeg (ffmpeg-full with libass) for clipping and burning, and an AI model to analyze subtitle semantics and create meaningful 2–5 minute chapters.
When to use it
- You need short, topic-focused clips from long YouTube lectures or presentations.
- You want bilingual (English + Chinese) subtitles for clips or full videos.
- You need hardcoded subtitle versions for social platforms without subtitle support.
- You want concise social-media summaries and captions generated automatically.
Best practices
- Run environment detection first to verify yt-dlp, FFmpeg with libass, and Python dependencies.
- Prefer videos with English subtitles; fallback to auto-generated captions if needed.
- Limit each AI chapter to 2–5 minutes for coherent, shareable clips.
- Use batch translation (20 subtitles per batch) to reduce API calls and keep consistency.
- Avoid source file paths with spaces when using FFmpeg; the tool uses a temp directory workaround.
Example use cases
- Create multiple short clips from a 60-minute tech talk for TikTok or YouTube Shorts.
- Produce bilingual clips for bilingual audiences or educational content.
- Generate hardcoded subtitle videos for platforms that do not support external SRT files.
- Extract topic-focused excerpts from interviews for highlights or marketing.
- Quickly produce captioned clips with ready-made social summaries for posting.
FAQ
The skill detects this and prompts installation of ffmpeg-full, explaining the macOS path and providing the install command.
Can I select multiple chapters to process at once?
Yes. The UI supports multi-select; selected chapters are processed in parallel with progress reporting for each step.
How are filenames and special characters handled?
Filenames are sanitized: special characters removed, spaces replaced with underscores, and length capped to avoid filesystem issues.