🧪 Skills

Short Drama Publisher

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

v1.0.0
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Description


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

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Compatible Platforms

Pricing

Free

Related Configs