universal-video-clipper

name	universal-video-clipper
description	Use when the user wants viral short clips from either a YouTube URL or an uploaded/local video file for Instagram Reels or TikTok.

Universal Video Clipper

Transform long-form videos into viral-worthy short clips optimized for Instagram Reels and TikTok using AI-powered analysis. Supports both YouTube URLs and local/uploaded video files.

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How to Talk to This Tool

Use natural language to create clips:

For YouTube Videos

"Create viral clips from this YouTube video: https://www.youtube.com/watch?v=VIDEO_ID"

"Turn this YouTube video into Instagram Reels"

"Extract the best highlights from this tutorial for TikTok"

For Local/Uploaded Videos

"Create short clips from this video file: /path/to/video.mp4"

"Make Reels from my uploaded video"

With Additional Options

"Create 5 viral clips from this video with animated captions"

"Extract highlights from this video, skip downloading since I already have it"

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What You Get

For each video processed:

• 3-7 viral-worthy clips (15-60 seconds each)
• Dual formats - Standard (original aspect ratio) + Instagram 9:16 vertical
• Optional animated captions - CapCut-style word-by-word highlighting
• Summary report with:
  • Virality scores (1-10)
  • Suggested captions with emojis
  • Hashtag recommendations
  • Target audience insights

Sample Output Structure

downloads/{video_id}/
├── original.mp4                    # Downloaded/copied video
├── original.json                   # Whisper transcript with timestamps
├── metadata.json                   # Video info
├── analysis_request.md             # AI analysis prompt
├── clip_recommendations.json       # AI-generated clip suggestions
├── SUMMARY.md                      # Final report
└── clips/
    ├── clip_001_hook.mp4           # Standard clip
    ├── clip_001_hook_instagram.mp4 # 9:16 vertical
    ├── clip_001_hook_captioned.mp4 # With animated captions
    └── ...

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How It Works

Step 1: Video Ingest

• YouTube URLs: Downloads via yt-dlp
• Local files: Copies to working directory
• Automatically detects source type

Step 2: Transcription

• Uses Whisper for speech-to-text
• Generates timestamps for each segment
• Falls back from "base" to "tiny" model if needed

Step 3: AI Analysis (Interactive)

The script pauses for AI analysis:

1. Review analysis_request.md (contains transcript + instructions)
2. Run AI on this prompt to generate clip recommendations
3. Save results as clip_recommendations.json
4. Press Enter to continue

Step 4: Generate Clips

• Validates clip timestamps
• Creates standard and Instagram 9:16 versions
• Optionally adds animated captions

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Prerequisites

Verify these are installed:

# Check installations
ffmpeg -version                      # Video processing
yt-dlp --version                    # YouTube downloader (only for YouTube URLs)
whisper --help                      # Audio transcription

If missing, install:

# Install Python dependencies
pip install -r assets/requirements.txt

# Install ffmpeg (macOS)
brew install ffmpeg

# Install ffmpeg (Linux)
sudo apt install ffmpeg

# Install Node.js 18+ (for animated captions)
brew install node

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Technical Reference

Skill Package Layout

universal-video-clipper/
├── SKILL.md
├── assets/requirements.txt
└── scripts/
    ├── ai_clip_generator.py
    ├── prompt_templates/clip_analysis_prompt.md
    └── remotion-captions/            # Remotion caption renderer project

CLI Command

# YouTube input
python3 scripts/ai_clip_generator.py "https://www.youtube.com/watch?v=VIDEO_ID"

# Local/uploaded file input
python3 scripts/ai_clip_generator.py "/absolute/path/to/uploaded_video.mp4"

# Skip download if video exists
python3 scripts/ai_clip_generator.py <source> --skip-download

# Skip transcription if done
python3 scripts/ai_clip_generator.py <source> --skip-transcription

# With animated captions
python3 scripts/ai_clip_generator.py <source> --add-captions
python3 scripts/ai_clip_generator.py <source> --add-captions --caption-style scaling
python3 scripts/ai_clip_generator.py <source> --add-captions --caption-color "#FF6600"

Caption Styles

Style	Description	Best For
background	Animated highlight box behind words (default)	CapCut-style, modern IG look
scaling	Words scale up with spring animation	Energetic, punchy content
colored	Active word highlighted in accent color	Clean, professional look

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Clip Selection Criteria

The AI analysis identifies viral-worthy moments based on:

1. Strong Hooks (0-3 seconds) - Bold claims, surprising statements, visual demonstrations
2. Value Bombs - Actionable tips, "aha!" moments, problem-solution demos
3. Emotional Peaks - Excitement, surprise, humor, impressive demonstrations
4. Story Arcs - Complete narratives with setup → demonstration → payoff

Constraints:

• Duration: 15-60 seconds (optimal: 20-45 seconds)
• Self-contained: Each clip makes sense independently
• Natural boundaries: No mid-sentence cuts
• Platform: Optimized for Instagram Reels and TikTok (9:16 mobile vertical)

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JSON Output Format

The AI analysis must return JSON in this format:

{
  "clips": [
    {
      "clip_number": 1,
      "start_time": 5.2,
      "end_time": 32.8,
      "duration": 27.6,
      "title": "Hook: AI Creates Apple Shortcuts",
      "description": "Opens with bold claim and immediate demonstration",
      "virality_score": 9,
      "virality_factors": ["strong_hook", "visual_demo", "trending_topic"],
      "suggested_caption": "🤯 I made AI create Apple Shortcuts!",
      "content_type": "hook",
      "target_audience": "iOS users, automation enthusiasts",
        
    
  
   
   
   
     

See references/clip_analysis_prompt.md for the complete prompt template.

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Troubleshooting

Quick Fixes

Issue	Solution
Download fails	brew upgrade yt-dlp
Transcription fails	pip install openai-whisper
FFmpeg not found	brew install ffmpeg (macOS) / sudo apt install ffmpeg (Linux)
Invalid JSON from AI	Remove markdown code blocks (json), ensure all required fields present

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Key Functions

In scripts/ai_clip_generator.py:

• is_youtube_url(input) - Detect source type
• parse_youtube_url(url) - Extract video ID
• prepare_local_video(path, video_id, output_dir) - Ingest local file input
• download_video(url, video_id, output_dir) - Download with cookie fallbacks
• transcribe_video(video_path, output_dir) - Whisper transcription
• generate_analysis_prompt(transcript, metadata, output_dir) - Create Claude prompt
• validate_clip_recommendations(json_path, duration) - Validate AI output
• generate_clips(video_path, recommendations, output_dir) - FFmpeg clip generation
• generate_captions_for_clip(...) - Remotion caption generation
• convert_whisper_to_captions(...) - Whisper JSON to Remotion format
• generate_summary_report(...) - Create final report

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Design Decisions

1. File-based AI integration - Simple, no API costs, easy debugging
2. Interactive analysis step - User reviews prompt and AI output before generation
3. Dual input support - Handles both YouTube and local uploads
4. Whisper base model - Balance of speed and accuracy
5. Dual clip formats - Standard landscape + Instagram 9:16 vertical
6. Remotion for captions - React-based rendering for animated word-by-word captions
7. Caption positioning - Captions positioned within video content (not black bars) for 9:16 letterboxed videos

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Next Steps After Clip Generation

1. Review clips - Check quality and content
2. Add captions - Use --add-captions flag or video editing software
3. Add music - Use trending audio in Instagram/TikTok editor
4. Post with optimized captions - Use suggestions from SUMMARY.md
5. Track performance - Monitor which clips perform best