youtube-clipper
by op7418The youtube-clipper skill is an installable workflow for clipping YouTube videos into usable segments, subtitle variants, and short summaries. It supports youtube-clipper usage for video editing, bilingual subtitles, and export-ready clips with less manual work than a generic prompt.
This skill scores 78/100, which means it is a solid listing candidate for directory users who want a real YouTube clipping workflow rather than a generic prompt. The repository provides enough operational detail, install guidance, scripts, and troubleshooting to make adoption reasonably low-guesswork, though setup still depends on external tooling and the repo is stronger on execution than on concise quick-start clarity.
- Strong workflow coverage: download video/subtitles, analyze chapters, clip segments, translate to bilingual subtitles, burn subtitles, and generate summaries.
- Good triggerability and install path: SKILL.md includes a clear use case plus an npx skills install command and manual install script.
- Substantial operational support: 10 scripts plus references for yt-dlp, ffmpeg, and subtitle formatting indicate real execution guidance, not a placeholder.
- Setup has external dependency friction: it requires yt-dlp, FFmpeg with libass/ffmpeg-full, and Python packages, so installation may fail without environment readiness.
- The frontmatter description is very short, and the repo appears more execution-heavy than discovery-heavy, so users may need to read supporting docs to understand fit quickly.
Overview of youtube-clipper skill
What youtube-clipper does
The youtube-clipper skill is an installable workflow for turning a YouTube video into usable clips, subtitle variants, and a short summary. It is built for users who want more than a generic prompt: they want a repeatable way to download, analyze, trim, translate, and export video segments with less manual handling.
Best fit for this skill
This youtube-clipper skill is a strong fit if you regularly work with interviews, lectures, podcasts, tutorials, or creator videos and need clean segments for republishing, review, or note-taking. It is especially useful for youtube-clipper for Video Editing when the real task is “find the best moment, clip it, and make it publishable,” not just “download a file.”
Main differentiators
Unlike a one-off prompt, youtube-clipper combines video download, semantic chaptering, subtitle translation, and subtitle burning in one workflow. The practical value is that it helps the agent make editorial decisions from the content, then carry those decisions through to export-ready clips and bilingual output.
How to Use youtube-clipper skill
Install and verify the setup
Use the repository’s install path first, then confirm the supporting tools are available. The recommended youtube-clipper install is:
npx skills add https://github.com/op7418/Youtube-clipper-skill
After install, check README.md for the setup path, then inspect SKILL.md and the helper files that drive behavior. This skill depends on yt-dlp, Python packages such as pysrt and python-dotenv, and FFmpeg with libass support if you plan to burn subtitles.
Give the skill the right input
For best youtube-clipper usage, provide:
- the YouTube URL
- the target outcome: clips, bilingual subtitles, burned-in subtitles, or a summary
- any time constraints, language preferences, or clip length preferences
- whether you want chapter-based selection or a specific segment
A weak request is “clip this video.” A stronger request is: “Use youtube-clipper to analyze this 45-minute interview, identify 3 to 5 meaningful segments around product strategy, and export the best 2-minute clips with bilingual subtitles.”
Read these files first
Start with SKILL.md to understand the workflow, then review:
README.mdfor install and usage contextreferences/yt-dlp-guide.mdfor download behaviorreferences/ffmpeg-guide.mdfor subtitle and export requirementsreferences/subtitle-formatting.mdfor subtitle styling expectationsscripts/download_video.py,scripts/analyze_subtitles.py, andscripts/clip_video.pyfor the operational path
This order matters because it shows where youtube-clipper depends on local tooling versus where it adds AI-driven selection.
Workflow tips that improve output
Use youtube-clipper guide mode in a staged workflow: download first, inspect chapter suggestions, then clip only the segments that match your goal. If you plan to burn subtitles, verify FFmpeg support before asking for export. If the source has poor captions, mention that upfront so the skill can fall back to transcript-driven analysis instead of assuming clean subtitle input.
youtube-clipper skill FAQ
Is youtube-clipper better than a normal prompt?
Usually yes when you need a repeatable editing pipeline. A normal prompt can describe a clip, but youtube-clipper gives the agent a practical path for download, analysis, clipping, subtitle translation, and export. That reduces guesswork and helps with youtube-clipper usage across different videos.
Do I need video editing experience?
No. The skill is useful for beginners because it structures the task, but you still need to tell it what “good” looks like. If you only want a rough social clip, say so. If you need precise chapter-based extraction, say that too.
When should I not use it?
Do not use youtube-clipper if you only need a quick note or a single timestamp quote. It is also a poor fit if you cannot install local tooling, especially FFmpeg and yt-dlp, or if the video source is not a YouTube URL the workflow can access.
Does it fit bilingual or repurposing workflows?
Yes. The repository is designed for translation, bilingual subtitles, and publishable clip output, so it fits creators who need downstream assets rather than just a trimmed MP4. That is the main reason to choose this youtube-clipper skill over a generic video prompt.
How to Improve youtube-clipper skill
Give clearer editorial intent
The biggest quality lever is your selection brief. Instead of “make clips,” specify the audience, tone, and purpose: highlight reel, educational cut, quote clip, or summary excerpt. That helps youtube-clipper choose more relevant segments and avoid mechanically interesting but editorially weak moments.
Provide constraints early
Mention clip length, subtitle language, and whether you want hardcoded subtitles. If you know the video contains multiple speakers, noisy audio, or auto-generated captions, include that too. These details reduce rework because the skill can plan around subtitle quality and export limitations before clipping starts.
Watch for common failure modes
The most common issues are tool mismatch and unclear source quality:
- FFmpeg without
libassblocks subtitle burning - missing
yt-dlpblocks download - weak subtitles reduce chapter quality
- vague prompts lead to broad, unusable clips
If the first output is too broad, tighten the brief by asking for fewer clips, shorter segments, or a specific topic window.
Iterate from analysis to export
Use the first pass as a decision layer, not the final answer. Ask youtube-clipper to refine around the best segment boundaries, then request export only after you approve the chaptering. For youtube-clipper for Video Editing, that two-step loop usually produces cleaner cuts than trying to generate the final deliverable in one pass.