Auto Clipper

• Screen recording highlights: Auto-clip moments from Loom/obsidian recordings
• Meeting recaps: Extract key segments from meeting recordings
• Content creation: Batch-process raw footage into short clips
• Security camera clips: Pull motion-triggered segments from camera feeds
• Gaming highlights: Auto-clip "best of" moments from recordings

# Run once (scan and process)
python3 scripts/auto_clipper.py run

# Dry run (show what would be processed)
python3 scripts/auto_clipper.py run --dry-run

# Force reprocess all files
python3 scripts/auto_clipper.py run --force

# Start continuous watcher (not cron-based)
python3 scripts/auto_clipper.py watch

# Show status
python3 scripts/auto_clipper.py status

Purpose

AutoClipper enables OpenClaw agents to automatically:

• Monitor a watch folder for new media files (videos, screen recordings, camera clips)
• Analyze media to understand what's worth clipping (via Agent Swarm delegation)
• Generate clips using ffmpeg (highlights, segments, trimmed videos)
• Produce compilations by stitching multiple clips together
• Schedule runs via cron for fully automated workflows

Architecture

┌─────────────────────────────────────────────────────────────┐
│                      AutoClipper Skill                       │
├─────────────────────────────────────────────────────────────┤
│  1. Watch Folder (configurable input path)                  │
│         ↓                                                   │
│  2. Media Scanner (find new files, filter by extension)    │
│         ↓                                                   │
│  3. Agent Swarm delegation (analyze → clip strategy)             │
│         ↓                                                   │
│  4. Clip Engine (ffmpeg operations)                         │
│         ↓                                                   │
│  5. Output Organizer (save to output folder, optional SNS)  │
└─────────────────────────────────────────────────────────────┘

Components

1. Watch Folder Scanner

• Monitors a configured input directory
• Filters by file extensions: .mp4, .mov, .mkv, .avi, .webm
• Tracks processed files (to avoid re-processing)
• Configurable: watchFolder, fileExtensions, processedLog

2. Media Analyzer (via Agent Swarm)

• Delegates analysis to appropriate model (MiniMax for code/technical, Kimi for creative)
• Determines:
  • Which segments to clip (timestamp ranges)
  • Clip duration targets
  • Output format preferences
• Returns structured clip plan: [{start, end, label, priority}]

3. Clip Engine (ffmpeg)

• Trim: Extract segments without re-encoding (fast)
• Transcode: Convert to target format/codec
• Highlight: Auto-detect "interesting" segments (via scene detection)
• Compile: Stitch multiple clips into single video
• Overlay: Add watermarks, timestamps, captions

4. Output Manager

• Organized output folder structure: output/YYYY-MM-DD/
• Configurable naming: {original}-{timestamp}-{index}.mp4
• Optional: Notify via OpenClaw message (Discord, WhatsApp, etc.)

5. Cron Scheduler

• Standalone script for cron integration
• Configurable schedule: 0 * * * * (hourly), 0 9 * * * (daily at 9am)
• Dry-run mode for testing
• Lock file to prevent overlapping runs

Configuration (config.json)

{
  "watchFolder": "~/Downloads/Recordings",
  "outputFolder": "~/Videos/Clips",
  "fileExtensions": [".mp4", ".mov", ".mkv"],
  "processedLog": "logs/processed.json",
  "clipSettings": {
    "defaultDuration": 60,
    "minClipDuration": 10,
    "maxClipDuration": 300,
    "outputCodec": "h264",
    "outputFormat": "mp4"
  },
  "intentRouter": {
    "enabled": true,
    "model": "openrouter/minimax/minimax-m2.5"
  },
  "cron": {
    "schedule": "0 * * * *",
    "enabled": false
  },
  "notifications": {
    "enabled": false,
    "channel": "discord"
  }
}

Tools Needed

Agent Swarm integration

When AutoClipper finds new media, it delegates analysis:

User task: "Analyze video and suggest clip timestamps for meeting highlights"
→ router.spawn() → sessions_spawn(task, model)
← Returns: [{start: "00:05:30", end: "00:07:45", label: "action item discussion"}, ...]

Prompt template for media analysis:

Analyze this video file: {filename}
Duration: {duration_seconds} seconds
Extract: Key moments worth clipping as short highlights (30-90 seconds each)
Output: JSON array of {start_timestamp, end_timestamp, description}

Directory Structure

auto-clipper/
├── SKILL.md              # This file
├── _meta.json            # Skill metadata
├── config.json           # Configuration
├── README.md             # Setup instructions
├── scripts/
│   ├── auto_clipper.py   # Main entry point
│   ├── scanner.py        # Watch folder scanner
│   ├── clipper.py        # ffmpeg wrapper
│   ├── analyzer.py       # Agent Swarm integration
│   └── run.sh            # Cron launcher
└── logs/
    └── processed.json    # Track processed files

Keywords

• video, clip, clips, highlight, highlights
• trim, cut, extract, segment
• ffmpeg, transcode, encode, convert
• folder, watch, monitor, automation
• cron, schedule, batch, process
• screen recording, meeting, recording

Skill Name Ideas

1. AutoClipper ✓ (chosen)
2. ClipForge
3. MediaMason
4. VideoHarvest
5. HighlightHub
6. ClipStream
7. MediaSnip
8. AutoTrim

Implementation Phases

Phase 1: Core (MVP)

• Folder scanner with extension filtering
• Basic ffmpeg trim operation
• Simple processed file tracking
• CLI entry point

Phase 2: Intelligence

• Agent Swarm integration for clip planning
• Scene detection for auto-highlighting
• Metadata extraction with ffprobe

Phase 3: Automation

• Cron launcher script
• Continuous watcher mode
• Notification system
• Output organization

Phase 4: Advanced

• Multi-clip compilation
• Overlay/watermark support
• Custom clip templates
• Node camera integration

Notes

• Performance: Use -c copy for fast trimming (no re-encode)
• Storage: Auto-cleanup processed files or move to archive
• Error handling: Skip corrupted files gracefully, log failures
• Idempotency: Same input file should not produce duplicate output