← 返回
未分类

Youtube Clip Curator

Analyze long YouTube videos to extract ranked clip candidates with timestamps, titles, hooks, emotion tags, and optional edit project files.
分析长 YouTube 视频,提取排名剪辑候选(时间戳、标题、钩子、情感标签)及可选的编辑项目文件。
noahcraft-open noahcraft-open 来源
未分类 clawhub v1.0.0 1 版本 100000 Key: 无需
★ 0
Stars
📥 294
下载
💾 0
安装
1
版本
#clip#curator#latest#video#vtuber#youtube

概述

YouTube Clip Curator

Analyze a long-form YouTube video (e.g. VTuber stream, podcast, lecture) and extract a ranked list of "clip candidates" with start/end timestamps, hook descriptions, click-worthy titles, and emotion tags. Optionally generate FCPXML for Premiere Pro / DaVinci Resolve, plus thumbnail briefs ready for an image AI.

When to use

Use this skill when the user needs to:

  • Turn a 1-3 hour stream into 5-10 short YouTube clip candidates
  • Reverse-engineer the editing thinking of a target clip channel
  • Get start/end seconds + title + hook for each clip — ready to cut
  • Emotion-tag each clip for thumbnail and promotion strategy
  • Bridge from raw long video to a Premiere Pro / DaVinci Resolve timeline
  • Suggest the most click-worthy moments without reviewing the full video

How it works

  1. Ask for the source video (YouTube URL, MP4 path, or transcript)
  2. Optionally ask for a "style template" channel (e.g. ちばちゃんねる) to mimic editing thinking
  3. Ask for the target clip count (default: 5) and duration target (short / medium / long)
  4. Analyze the transcript to find hooks, punchlines, emotion peaks, character moments
  5. Score each candidate by:
    • Emotion intensity (laughter, surprise, chaos, cuteness)
    • Clear punchline / story structure
    • Quotable phrases / meme potential
    • Character trait expression
    • Standalone comprehensibility
  6. Output a ranked JSON list with start/end times, titles, hooks, and reasons

Output format

{
  "source_video": {"title": "...", "duration_min": 87},
  "template_style": "@ChannelChiba (chaos+character observation)",
  "clips": [
    {
      "rank": 1,
      "start_sec": 595,
      "end_sec": 932,
      "duration_sec": 337,
      "score": 0.95,
      "title_suggestion": "【ふざけるな】サイコロ振りながら偽フワワに応募してきたフブブにもこ田が本気でブチギレるwww",
      "hook_description": "ふぶき『ホロライブのふわふわしたとこからやってきたフブブです』+直後にカラカラ音→もこ田『ふざけるなよ!』の即ツッコミ",
      "expected_appeal": "笑い・カオス・ツッコミ",
      "emotion_tags": ["笑い", "カオス", "ツッコミ", "天然"],
      "peak_frame_sec": 658,
      "reason": "明確なパンチライン+キャラの濃い側面+ギャップ構造+ビジュアル化しやすい瞬間"
    }
  ]
}

Selection rules (built-in)

The skill uses these criteria when analyzing candidates:

High priority

  • Clear punchline / story structure — must have a beat that closes
  • Strong emotion peak — laughter, surprise, chaos, or cuteness above baseline
  • Character expression —天然 / 暴走 / ツッコミ / 暴言 / drysarcasm shines through
  • Standalone comprehensibility — viewer can understand without prior context

Medium priority

  • Quotable phrases — short impactful lines that meme well
  • Visual moment — facial expression / situation that thumbnails strongly
  • First 3 seconds hook — opens with strong statement or visual
  • Gap / contrast structure — serious situation × silly reaction etc.

Hard avoids

  • Pure gameplay walkthrough with no reaction
  • Long silence or downtime without payoff
  • Inside-joke moments that need 5+ minutes of context
  • Content that risks defamation / harm

Composition rules

  • Target length: 30-90 sec for short, 2-5 min for standard, 5-10 min for story
  • Open with the strongest 3 seconds
  • Maintain フリ→展開→オチ structure
  • Cut tempo: faster during chaos peaks, slower during character moments
  • Caption emphasis on power words: やばい / 神 / 草 / 終わった / ヤバ / うわ

Optional outputs

  • Premiere Pro FCPXML — drop into File > Import > XML, builds a timeline
  • DaVinci Resolve script — places clips on tracks, creates render queue
  • Thumbnail brief JSON — peak_frame_sec + emotion_tags + title hints for image AI
  • SRT subtitles — Whisper-derived per-clip captions, editable in any NLE

Tips for best results

  • Provide stream subtitles if available (auto-captions are OK but Whisper transcripts are better)
  • Specify a reference clip channel — output style adapts to that channel's thinking
  • For VTuber clips: include character name + game name so context-specific phrases score correctly
  • Ask for "3-5 clips" first to validate, then re-run for full set

版本历史

共 1 个版本

  • v1.0.0 当前
    2026-05-08 01:47 安全 安全

安全检测

腾讯云安全 (Keen)

安全,无风险
查看报告

腾讯云安全 (Sanbu)

安全,无风险
查看报告

🔗 相关推荐

content-creation

Humanizer

biostartechnology
消除AI写作痕迹,使文本更自然真实。基于维基百科"AI写作特征"指南,识别并修正夸张象征、宣传用语、肤浅-ing分析、模糊归因、破折号滥用、三项排比、AI词汇、负面平行结构及冗长连接词等模式。
★ 909 📥 207,941
content-creation

humanizer-zh

liuxy951129-cpu
去除文本中的 AI 生成痕迹。适用于编辑或审阅文本,使其听起来更自然、更像人类书写。 基于维基百科的"AI 写作特征"综合指南。检测并修复以下模式:夸大的象征意义、 宣传性语言、以 -ing 结尾的肤浅分析、模糊的归因、破折号过度使用、三段
★ 63 📥 29,689
content-creation

Marketing Skills

jchopard69
访问 23 个营销模块,提供转化率优化(CRO)、SEO、文案撰写、分析、发布、广告和社交媒体的清单、框架及可直接使用的交付物。
★ 143 📥 30,991