Intelligent YouTube video clipper with AI-powered chapter analysis, subtitle translation, and batch processing.
Works with
Downloads videos and English subtitles, then uses Claude to generate fine-grained chapters (2–5 minute segments) by analyzing subtitle semantics and identifying natural topic transitions
Supports bilingual subtitle generation (English + Chinese), subtitle burning to video with styled overlays, and social media summary generation for each clip
Handles environment detection f
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionyoutube-clipperExecute the skills CLI command in your project's root directory to begin installation:
Fetches youtube-clipper from op7418/youtube-clipper-skill and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate youtube-clipper. Access via /youtube-clipper in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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Installation: If you're installing this skill from GitHub, please refer to README.md for installation instructions. The recommended method is
npx skills add https://github.com/op7418/Youtube-clipper-skill.
你将按照以下 6 个阶段执行 YouTube 视频剪辑任务：
目标: 确保所有必需工具和依赖都已安装
检测 yt-dlp 是否可用
yt-dlp --version
检测 FFmpeg 版本和 libass 支持
# 优先检查 ffmpeg-full（macOS）
/opt/homebrew/opt/ffmpeg-full/bin/ffmpeg -version
# 检查标准 FFmpeg
ffmpeg -version
# 验证 libass 支持（字幕烧录必需）
ffmpeg -filters 2>&1 | grep subtitles
检测 Python 依赖
python3 -c "import yt_dlp; print('✅ yt-dlp available')"
python3 -c "import pysrt; print('✅ pysrt available')"
如果环境检测失败:
brew install yt-dlp 或 pip install yt-dlpbrew install ffmpeg-full # macOS
pip install pysrt python-dotenv注意:
/opt/homebrew/opt/ffmpeg-full/bin/ffmpeg (Apple Silicon)目标: 下载 YouTube 视频和英文字幕
询问用户 YouTube URL
调用 download_video.py 脚本
cd ~/.claude/skills/youtube-clipper
python3 scripts/download_video.py <youtube_url>
脚本会：
向用户展示：
输出:
<id>.mp4（使用视频 ID 命名，避免特殊字符问题）<id>.en.vtt目标: 使用 Claude AI 分析字幕内容，生成精细章节（2-5 分钟级别）
调用 analyze_subtitles.py 解析 VTT 字幕
python3 scripts/analyze_subtitles.py <subtitle_path>
脚本会输出结构化字幕数据：
你需要执行 AI 分析（这是最关键的步骤）：
为每个章节生成：
章节生成原则：
向用户展示章节列表：
📊 分析完成，生成 X 个章节：
1. [00:00 - 03:15] AGI 不是时间点，是指数曲线
核心: AI 模型能力每 4-12 月翻倍，工程师已用 Claude 写代码
关键词: AGI、指数增长、Claude Code
2. [03:15 - 06:30] 中国在 AI 上的差距
核心: 芯片禁运卡住中国，DeepSeek benchmark 优化不代表实力
关键词: 中国、芯片禁运、DeepSeek
... (所有章节)
✓ 所有内容已覆盖，无遗漏
目标: 让用户选择要剪辑的章节和处理选项
使用 AskUserQuestion 工具让用户选择章节
询问处理选项：
确认用户选择并展示处理计划
目标: 并行执行多个处理任务
对于每个用户选择的章节，执行以下步骤：
python3 scripts/clip_video.py <video_path> <start_time> <end_time> <output_path>
<章节标题>_clip.mp4<章节标题>_original.srtpython3 scripts/translate_subtitles.py <subtitle_path>
<章节标题>_translated.srt<章节标题>_bilingual.srtpython3 scripts/burn_subtitles.py <video_path> <subtitle_path> <output_path>
<章节标题>_with_subtitles.mp4python3 scripts/generate_summary.py <chapter_info>
<章节标题>_summary.md进度展示:
🎬 开始处理章节 1/3: AGI 不是时间点，是指数曲线
1/6 剪辑视频片段... ✅
2/6 提取字幕片段... ✅
3/6 翻译字幕为中文... [=====> ] 50% (26/52)
4/6 生成双语字幕文件... ✅
5/6 烧录字幕到视频... ✅
6/6 生成总结文案... ✅
✨ 章节 1 处理完成
目标: 组织输出文件并展示给用户
创建输出目录
./youtube-clips/<日期时间>/
输出目录位于当前工作目录下
组织文件结构：
<章节标题>/
├── <章节标题>_clip.mp4 # 原始剪辑（无字幕）
├── <章节标题>_with_subtitles.mp4 # 烧录字幕版本
├── <章节标题>_bilingual.srt # 双语字幕文件
└── <章节标题>_summary.md # 总结文案
向用户展示：
✨ 处理完成！
📁 输出目录: ./youtube-clips/20260121_143022/
文件列表:
🎬 AGI_指数曲线_双语硬字幕.mp4 (14 MB)
📄 AGI_指数曲线_双语字幕.srt (2.3 KB)
📝 AGI_指数曲线_总结.md (3.2 KB)
快速预览:
open ./youtube-clips/20260121_143022/AGI_指数曲线_双语硬字幕.mp4
询问是否继续剪辑其他章节
问题: FFmpeg subtitles 滤镜无法正确解析包含空格的路径
解决方案: burn_subtitles.py 使用临时目录
问题: 逐条翻译会产生大量 API 调用
解决方案: 每批 20 条字幕一起翻译
目标: 生成 2-5 分钟粒度的章节，避免半小时粗粒度
方法:
区别:
路径:
/opt/homebrew/bin/ffmpeg/opt/homebrew/opt/ffmpeg-full/bin/ffmpeg (Apple Silicon)<章节标题>_clip.mp4<章节标题>_bilingual.srt<章节标题>_with_subtitles.mp4<章节标题>_summary.md文件名处理:
/, \, :, *, ?, ", <, >, |）当用户触发这个 Skill 时：
记住：这个 Skill 的核心价值在于 AI 精细章节分析 和 无缝的技术处理，让用户能快速从长视频中提取高质量的短视频片段。
Make data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
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We added youtube-clipper from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Keeps context tight: youtube-clipper is the kind of skill you can hand to a new teammate without a long onboarding doc.
We added youtube-clipper from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
youtube-clipper is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
youtube-clipper fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
youtube-clipper is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Solid pick for teams standardizing on skills: youtube-clipper is focused, and the summary matches what you get after install.
youtube-clipper has been reliable in day-to-day use. Documentation quality is above average for community skills.
Solid pick for teams standardizing on skills: youtube-clipper is focused, and the summary matches what you get after install.
youtube-clipper reduced setup friction for our internal harness; good balance of opinion and flexibility.
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