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short-drama-publisher短剧出版商

Agent Skill

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。

总安装

11,832

周安装

493

GitHub Stars

公开资料未说明

下载量

3,944
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:short-drama-publisher(短剧出版商)
来源仓库:https://github.com/ye4wzp/short-drama-publisher
安装命令:
openclaw skills install short-drama-publisher
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install short-drama-publisher

简介

Short drama publisher 自动化下载短剧内容并利用 AI 剪辑精彩片段发布。

  • 可将长视频转为 15 秒竖屏短视频用于多平台分发。
  • 通过 clawhub 安装,需接入 MoboBoost 等平台的内容源接口。
  • 使用前应核实版权许可协议及平台上传频率限制规则。
  • 建议设置内容审核环节以避免违规素材自动发布风险。

SKILL.md

name
short-drama-publisher
version
1.0.0
description
>
Triggers
短剧发布", "short drama", "MoboBoost", "video publisher",
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

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

  1. 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:

SignalWeightMethod
Scene Change30%OpenCV frame-by-frame difference analysis
Audio Peak25%Librosa audio amplitude analysis
Subtitle Emotion25%Text sentiment analysis on subtitles
Motion Intensity20%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

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

OpenClaw

75.04%
按下载量换算2,960

安全审计

VirusTotal

可疑

ClawScan

可疑

Static analysis

通过

权限和风险

操作浏览器

该 Skill 可能涉及浏览器控制能力,使用时可能读取或操作网页内容,需要在受控环境中确认权限边界。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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