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🦀 ClawHub

Short Drama Publisher

by @ye4wzp

Automated short drama video publisher. Downloads drama content from MoboBoost, uses AI to identify highlight moments, clips 15-second vertical videos with te...

Versionv1.0.0

Downloads762

#writing #video #legal #automation

View on ClawHub →

TERMINAL

clawhub install short-drama-publisher

📖 About This Skill

name: short-drama-publisher version: 1.0.0 description: > Automated short drama video publisher. Downloads drama content from MoboBoost, uses AI to identify highlight moments, clips 15-second vertical videos with text overlays, and auto-publishes to Facebook. Strawberry TV workflow clone. Triggers: "短剧发布", "short drama", "MoboBoost", "video publisher", "自动发布", "剪辑短剧". tags: [video, automation, short-drama, facebook, social-media, content, publisher, chinese] env: MOBOBOOST_COOKIES: "MoboBoost login cookies (JSON format, exported from browser)" FACEBOOK_COOKIES: "Facebook login cookies (JSON format, exported from browser)" requires: - ffmpeg - python3 - playwright

Short Drama Publisher (短剧自动化发布)

Automated short drama promotion video workflow, inspired by Strawberry TV model:

1. 📥 Download drama content from MoboBoost 2. 🎯 AI-powered highlight detection (scene changes, audio peaks, subtitle emotion) 3. ✂️ Clip 15-second vertical videos with white English title overlay 4. 📤 Auto-publish to Facebook

Prerequisites

System Dependencies

# macOS
brew install ffmpeg
Python dependencies
pip install playwright opencv-python librosa numpy pyyaml
playwright install chromium

Credentials Setup

1. MoboBoost Cookies - Login to https://ckoc.cdreader.com - Export cookies using browser extension (e.g., "EditThisCookie") - Save as config/moboboost_cookies.json

2. Facebook Cookies - Login to Facebook - Export cookies using browser extension - Save as config/facebook_cookies.json

Usage

Full Automated Workflow

python scripts/daily_workflow.py

Individual Modules

Download content:

python scripts/moboboost_downloader.py --drama-code 613815

Detect highlights:

python scripts/highlight_detector.py --input data/downloads/video.mp4

Clip video:

python scripts/video_editor.py --input video.mp4 --start 01:23 --title "Drama Name"

Publish to Facebook:

python scripts/facebook_publisher.py --video data/outputs/clip.mp4 --drama-code 613815 --drama-name "DramaName"

Daily Cron Job

# Run daily at 9am
0 9 * * * cd /path/to/short-drama-publisher && python scripts/daily_workflow.py >> logs/cron.log 2>&1

Configuration

settings.yaml

# Video settings
video:
  duration: 15          # Clip duration (seconds)
  width: 1080           # Width
  height: 1920          # Height (9:16 vertical)
Text overlay settings
text_overlay:
  font: "Arial-Bold"
  size_ratio: 0.05      # Font size as ratio of video width
  color: "#FFFFFF"
  border_color: "#000000"
  border_width: 2
  position_y: 0.75      # Vertical position (ratio from top)
AI highlight detection weights
highlight_weights:
  scene_change: 0.30
  audio_peak: 0.25
  subtitle_emotion: 0.25
  motion_intensity: 0.20
Publishing settings
publishing:
  videos_per_day: 3     # Number of videos per day
  interval_minutes: 120 # Interval between posts (minutes)

Directory Structure

short-drama-publisher/
├── SKILL.md                    # This file
├── config/
│   ├── settings.yaml           # Configuration
│   ├── moboboost_cookies.json  # MoboBoost credentials
│   └── facebook_cookies.json   # Facebook credentials
├── scripts/
│   ├── moboboost_downloader.py # Content downloader
│   ├── highlight_detector.py   # AI highlight detection
│   ├── video_editor.py         # Video clipping
│   ├── facebook_publisher.py   # Facebook publisher
│   └── daily_workflow.py       # Main workflow
├── data/
│   ├── downloads/              # Raw downloaded content
│   ├── outputs/                # Clipped videos
│   └── history.json            # Publishing history
├── fonts/                      # Font files
└── logs/                       # Log files

AI Highlight Detection

The highlight detector uses multiple signals to find the most engaging moments:

| Signal | Weight | Method | |--------|--------|--------| | Scene Change | 30% | OpenCV frame-by-frame difference analysis | | Audio Peak | 25% | Librosa audio amplitude analysis | | Subtitle Emotion | 25% | Text sentiment analysis on subtitles | | Motion Intensity | 20% | Optical flow magnitude calculation |

Each frame gets a composite score, and the highest-scoring 15-second segment is selected.

Important Notes

> [!WARNING] > - MoboBoost and Facebook websites may update, requiring script adjustments > - Recommend 1-3 videos per day to simulate organic posting rhythm > - Ensure you have rights to use MoboBoost content for promotion > - Cookie-based auth may expire; re-export periodically

💡 Examples