短剧出版商

SKILL研究检索

原名 short-drama-publisher

研究检索 浏览器控制 许可证明确 OpenClaw
安装这个技能

short-drama-publisher 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

累计安装 11,832 本周 493
GitHub 星标 未标注 仓库 short-drama-publisher
下载量 3,944 技能市场下载数
权限级别 浏览器控制 安装前请确认风险范围
许可证 许可证明确 MIT-0

安装

ClawHub 市场
在支持 Skills 的客户端里执行,装完问一句「你有哪些技能」验证
openclaw skills install short-drama-publisher

技能档案

研究检索
字段来自技能仓库与公开索引,中文说明由本站标注
作者 / 维护方ye4wzp仓库 short-drama-publisher · 版本 1.0.0
宿主客户端OpenClaw技能可被这些客户端加载
安装方式ClawHub 市场安装包 ye4wzp/short-drama-publisher
权限级别浏览器控制安装前请确认风险范围
许可证MIT-0许可证明确,商用前请自行确认条款
仓库可用性available技能仓库当前是否可访问
最近核验2026-05-01上游最后核验时间 · 数据更新 未标注
正文体量4.7 KBSKILL.md 原文大小,越大说明说明书写得越细

能做什么

5 项能力
上游给出的能力概览与适用场景
  • 按任务关键词查找相关 Skills
  • 展示可复制的安装命令
  • 保留来源站点、仓库和原始说明,方便继续核验
  • 补充不同宿主或平台的使用分布数据
  • 展示第三方安全扫描或审计结果
OpenClaw 用户查找和安装 Skill 时用户想查找某类 Agent Skill 时需要根据任务场景推荐可安装能力包时需要对比不同来源的安装命令和来源信息时

权限与风险

浏览器控制
装之前先读完这段

安装与使用指南

安装步骤

本站只展示命令,不托管安装包,也不自动执行。

怎么用

安装后应在对应宿主中按原始 README 的触发条件使用;当前草稿只做目录说明,具体调用方式由规则抽取,异常条目再复核。

SKILL.md 全文

4.7 KB
模型实际读到的说明书原文,来自技能仓库

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

安全信息

审计结论
未标注不代表已通过审计
安全审计VirusTotal、ClawScan、Static analysis第三方安全审计平台给出的结论

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技能内容以作者仓库为准;分类、权限级别与许可证状态的中文说明为中文整理,最近核验 2026-05-01。