| name | batch-processor |
| description | Process multiple videos in batch mode for efficiency. Supports batch download from YouTube URLs, batch autocut for multiple videos, and batch export to multiple platforms. Generates consolidated reports with all clips. |
| allowed-tools | Bash(ffmpeg:*) Bash(yt-dlp:*) Bash(python:*) |
| compatibility | Requires all trimer-clip dependencies |
| metadata | {"version":"1.0"} |
Batch Processor
This skill enables batch processing of multiple videos for maximum efficiency.
When to Use
- Processing multiple YouTube videos at once
- Batch converting podcast episodes to shorts
- Repurposing entire video libraries
- Creating content at scale
- Processing seasonal content (holiday, event videos)
Available Scripts
scripts/batch_process.py
Process multiple videos in batch.
Usage:
python skills/batch-processor/scripts/batch_process.py --input <json_file> [options]
Input JSON Format:
[
{
"source": "https://youtube.com/watch?v=VIDEO1",
"source_type": "youtube",
"num_clips": 5,
"platform": "tiktok"
},
{
"source": "./videos/video2.mp4",
"source_type": "file",
"num_clips": 3,
"platform": "shorts"
}
]
Options:
--input: JSON file with video list--output-dir: Output directory (default: ./batch_output/)--parallel: Number of parallel processes (default: 1)--transcription-model: Transcription model (auto, whisper, gemini)
Example:
python skills/batch-processor/scripts/batch_process.py --input videos.json --parallel 2
scripts/batch_from_urls.py
Download and process multiple YouTube URLs.
Usage:
python skills/batch-processor/scripts/batch_from_urls.py --urls <file> [options]
URLs File Format:
https://youtube.com/watch?v=VIDEO1
https://youtube.com/watch?v=VIDEO2
https://youtube.com/watch?v=VIDEO3
Options:
--urls: File with YouTube URLs (one per line)--num-clips: Clips per video (default: 5)--platform: Target platform (default: tiktok)- All other autocut options
Example:
python skills/batch-processor/scripts/batch_from_urls.py --urls urls.txt --num-clips 5 --platform shorts
Output
Directory Structure
batch_output/
2024-01-30/
video1/
video1_tiktok_001.mp4
video1_tiktok_002.mp4
report.json
video2/
video2_shorts_001.mp4
video2_shorts_002.mp4
report.json
batch_report.json
Batch Report
{
"batch_id": "batch_20240130_120000",
"total_videos": 10,
"successful": 8,
"failed": 2,
"total_clips": 40,
"output_dir": "./batch_output/2024-01-30/",
"processing_time": 1800.5,
"results": [
{
"source": "https://youtube.com/watch?v=...",
"status": "success",
"clips": 5,
"output_dir": "batch_output/2024-01-30/video1/"
},
{
"source": "https://youtube.com/watch?v=...",
"status":
Performance
- Sequential processing: 1 video at a time
- Parallel processing: 2-4 videos simultaneously (based on CPU cores)
- Estimated time: (video duration × 2) per video
Error Handling
- Individual failures: Continues with next video
- Partial success: Reports successful vs failed
- Retry logic: Retries failed videos once
- Progress tracking: Shows progress during processing
Tips
- Use parallel processing for large batches
- Process overnight for big libraries
- Check batch report for failed videos
- Retry failed videos individually
- Monitor disk space for large batches
References
- Parallel processing in Python
- Batch workflow optimization
