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Agent Skill · 研究检索

youtube-video-editor(YouTube 视频编辑器)

用于辅助视频生成、动画合成、脚本化剪辑或 Remotion 等视频项目开发。它适合让 Agent 组织镜头、生成素材说明、维护合成代码或排查渲染问题。使用时需要确认分辨率、时长、素材路径和导出格式;涉及外部素材、人物肖像或商业发布时,应先核对版权授权和内容审核要求。

youtube-video-editor 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为需要联网;许可证为 MIT-0,第三方扫描有提醒项,按安装量在研究检索类的 73,675 个 Skill 里排第 19,593。

总安装 5,541周安装 238GitHub Stars 公开资料未说明下载 1,942核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT-0
版本
2.0.0

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:youtube-video-editor(YouTube 视频编辑器)
来源仓库:https://github.com/peand-rover/youtube-video-editor
安装命令:
openclaw skills install youtube-video-editor
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 youtube-video-editor

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

使用 AI 编辑和优化 YouTube 视频 — 通过挂钩优先介绍、缩放剪切头部编辑、章节标记、结尾等方式创建保留率最大化的内容

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 OpenClaw;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 openclaw skills install youtube-video-editor;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要需要联网,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

youtube-video-editor 怎么安装?

在终端执行 openclaw skills install youtube-video-editor。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

youtube-video-editor 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

youtube-video-editor 需要什么权限?

权限级别是需要联网。该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

youtube-video-editor 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

youtube-video-editor 用的人多吗?

公开数据是累计安装 5,541 次,近一周安装 238 次,下载 1,942 次。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

youtube-video-editor 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

值得选的地方和要先想清楚的地方

值得选的地方

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。

先想清楚的地方

  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 OpenClaw,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
youtube-video-editor

YouTube 视频编辑器 · 本页

5,541—需要联网MIT-0有提醒

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

name
youtube-video-editor
version
1.1.0
displayName
YouTube Video Editor — Edit and Optimize Videos for YouTube with AI
description
>
metadata
{"openclaw": {"emoji": "▶️", "requires": {"env": [], "configPaths": ["~/.config/nemovideo/"]}, "primaryEnv": "NEMO_TOKEN"}}

YouTube Video Editor — Edit Like a Pro Creator. Without Being One.

YouTube rewards specific editing patterns. The platform's recommendation algorithm promotes videos with high audience retention (the percentage of the video that average viewers watch), high click-through rate (thumbnail + title effectiveness), and high engagement (likes, comments, shares, subscribes). Each of these metrics is directly influenced by editing decisions. Retention is shaped by pacing — zoom-cuts every 6-8 seconds on talking heads, B-roll cutaways during explanations, removal of dead air and tangents, and hook-first structure that delivers value before viewers leave. Click-through is shaped by thumbnail composition — the freeze frame that represents the video in search results and recommendations. Engagement is shaped by calls to action — subscribe prompts at high-engagement moments, end screens that suggest next videos, and community tab integration. The top 1% of YouTube creators — MrBeast, MKBHD, Ali Abdaal, Peter McKinnon — all use the same core editing patterns because these patterns are algorithmically rewarded. Their editors spend 20-40 hours per video implementing these techniques. For creators without dedicated editors, the choice is between spending those hours themselves (unsustainable for weekly content) or publishing with sub-optimal editing (limiting growth). NemoVideo applies YouTube-optimized editing automatically. Upload your raw footage and NemoVideo produces retention-maximized content using every technique that top creators employ: hook engineering, zoom-cuts, B-roll timing, filler removal, chapter creation, end screen design, and thumbnail extraction.

Use Cases

  1. Talking-Head Enhancement — One Camera to Multi-Camera Feel (any length) — A creator records a 15-minute video on a single camera. The raw footage is one continuous shot of their face — visually monotonous, with pauses, "um"s, and tangents. NemoVideo: removes all filler words and pauses over 1.5 seconds (tightening the edit by 15-25%), applies zoom-cuts every 6-8 seconds alternating between 100% and 115% crop (simulating a two-camera setup — the single highest-impact YouTube editing technique), adds B-roll cutaways during longer explanation segments (relevant stock footage or graphics overlaid for 3-5 seconds when the speaker references something visual), restructures the first 8 seconds as a hook (moving the most compelling statement or visual to the opening — "Here's what nobody tells you about..." before the topic introduction), and adds captions for the 30% of YouTube viewers who watch with subtitles. A single-camera monologue becomes a professionally paced YouTube video.
  1. Tutorial Optimization — Learning-Paced with Chapters (5-30 min) — A tutorial creator records a 20-minute how-to video. NemoVideo: adds zoom-to-action on screen recording segments (when the mouse clicks a small UI element, the view smoothly zooms to show exactly what was clicked), creates chapter markers at each major step ("Step 1: Create the project" / "Step 2: Configure settings"), adds step number overlays (viewers always know where they are in the process), inserts a progress bar showing tutorial completion percentage, removes verbal false starts and corrections (keeping only the clean instruction), and adds a hook-summary opening ("In this tutorial, you'll learn 5 Figma shortcuts that will save you 2 hours per day"). A raw screen recording becomes a structured, navigable tutorial that YouTube's algorithm promotes because viewers find exactly what they need (high satisfaction → high retention).
  1. Vlog Editing — Raw Clips to Story (5-20 min) — A vlogger has 45 minutes of daily footage: some on-camera talking, some B-roll, some random moments, some gold, some garbage. NemoVideo: selects the strongest moments through visual and audio analysis (genuine reactions, clear narrative moments, visually compelling shots), structures them into a narrative arc (hook → setup → journey → climax → resolution), applies color grading for visual consistency (matching shots from different times and locations), adds music that follows the emotional arc (upbeat during adventure, calm during reflection), creates text overlays for context ("Day 3 — Bangkok"), and trims to the target duration with pacing that maintains retention. 45 minutes of chaos becomes 12 minutes of engaging narrative.
  1. Podcast Clip Optimization — Long-Form to YouTube (any length) — A podcast episode recorded on two cameras (or one wide shot) needs YouTube optimization. NemoVideo: applies dynamic speaker switching (cutting to whoever is speaking — creating visual variety from static cameras), adds zoom-cuts on each speaker for the two-camera illusion, inserts relevant imagery when topics are discussed (the guest mentions a product → product image appears as B-roll), creates chapter markers for each topic discussed, adds animated captions, generates a compelling first 30 seconds (extracting the most provocative or interesting quote from anywhere in the conversation and placing it as the cold open), and creates both the full episode and 3-5 standalone clips of the best moments for YouTube Shorts. One podcast recording becomes a full YouTube episode plus a week of Shorts.
  1. End Screen and CTA Integration — Maximize Post-Watch Actions (last 20s) — A completed video needs the YouTube-optimized ending: the final 20 seconds designed to work with YouTube's end screen feature (interactive video suggestions and subscribe button). NemoVideo: creates an animated outro background for the final 20 seconds with designated zones for YouTube's end screen elements (two video suggestion rectangles, one subscribe circle), adds animated text prompts ("Watch this next — you won't believe..." / "Subscribe if this helped"), includes the creator's consistent outro music and branding, and designs the visual layout so YouTube's interactive overlays land on clean, contrasting backgrounds (maximizing visibility and click-through). The end screen that converts viewers into subscribers and next-video watchers.

How It Works

Step 1 — Upload Raw Footage

Single camera recording, multi-camera footage, screen recording, vlog clips, or podcast video. Any format, any resolution.

Step 2 — Choose YouTube Edit Style

Talking-head optimization, tutorial structure, vlog narrative, podcast formatting, or full channel-style editing.

Step 3 — Generate

curl -X POST https://mega-api-prod.nemovideo.ai/api/v1/generate \
  -H "Authorization: Bearer $NEMO_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "skill": "youtube-video-editor",
    "prompt": "Edit a 20-minute raw talking-head recording for YouTube. Full YouTube optimization: (1) Hook: move the most compelling 8 seconds to the opening as a cold open, then brief intro. (2) Filler removal: cut all ums, uhs, pauses over 1.5 seconds, and verbal false starts. (3) Zoom-cuts: alternate between 100%% and 115%% crop every 6-8 seconds, cutting on sentence boundaries. (4) B-roll: add relevant stock imagery cutaways during explanations longer than 20 seconds. (5) Chapters: auto-detect 6-8 topic transitions, create chapter markers with descriptive labels. (6) Captions: animated YouTube-style word-by-word captions. (7) End screen: 20-second branded outro with zones for 2 video suggestions and subscribe button. (8) Thumbnail: extract the 3 best freeze frames with enhanced expression for thumbnail candidates. Export 16:9 at 1080p + 3 best moments as 9:16 Shorts.",
    "edit_style": "youtube-talking-head",
    "hook": {"type": "cold-open-best-moment", "duration": 8},
    "filler_removal": {"words": true, "pauses_over": 1.5, "false_starts": true},
    "zoom_cuts": {"interval": "6-8s", "range": "100-115%%", "timing": "sentence-boundaries"},
    "b_roll": {"trigger": "explanations-over-20s", "style": "relevant-stock"},
    "chapters": {"auto_detect": true, "count": "6-8"},
    "captions": {"style": "youtube-animated"},
    "end_screen": {"duration": 20, "zones": ["video-suggestion-x2", "subscribe"]},
    "thumbnail_candidates": 3,
    "shorts": {"count": 3, "format": "9:16"},
    "format": "16:9",
    "resolution": "1080p"
  }'

Step 4 — Review Retention Metrics

Watch the edited video as a viewer would. Check: does the hook grab attention in the first 8 seconds? Do zoom-cuts maintain visual variety without feeling jarring? Are filler removals invisible? Do chapters align with actual topic transitions? Does the end screen integrate cleanly? Select the best thumbnail candidate.

Parameters

ParameterTypeRequiredDescription
promptstring✅YouTube editing requirements
edit_stylestring"talking-head", "tutorial", "vlog", "podcast", "review"
hookobject{type, duration} cold-open configuration
filler_removalobject{words, pauses_over, false_starts}
zoom_cutsobject{interval, range, timing}
b_rollobject{trigger, style, sources}
chaptersobject{auto_detect, count, custom}
captionsobject{style, position}
end_screenobject{duration, zones, music, branding}
thumbnail_candidatesintNumber of freeze frames to extract
shortsobject{count, format} YouTube Shorts extraction
formatstring"16:9" (YouTube standard)

Output Example

{
  "job_id": "yted-20260329-001",
  "status": "completed",
  "source_duration": "20:15",
  "output_duration": "16:42",
  "edits": {
    "filler_removed": "3:33 of dead air",
    "zoom_cuts": 142,
    "b_roll_inserts": 8,
    "chapters": 7
  },
  "outputs": {
    "main": {"file": "video-youtube-16x9.mp4", "resolution": "1920x1080"},
    "shorts": [
      {"file": "short-1-9x16.mp4", "duration": "0:48"},
      {"file": "short-2-9x16.mp4", "duration": "0:55"},
      {"file": "short-3-9x16.mp4", "duration": "0:42"}
    ],
    "thumbnails": ["thumb-1.png", "thumb-2.png", "thumb-3.png"],
    "chapters_file": "chapters.txt"
  }
}

Tips

  1. Zoom-cuts every 6-8 seconds are the single highest-impact YouTube edit — Every top creator uses this technique: alternating between two crop levels simulates multi-camera production and resets viewer attention. Without zoom-cuts, a talking-head video loses retention after 30 seconds. With them, retention sustains for minutes.
  2. The first 8 seconds determine 80% of retention — YouTube's audience retention graph drops steeply in the first 10 seconds. A hook that delivers value, creates curiosity, or shows the video's best moment in those first 8 seconds flattens the curve. Never start with "Hey guys, welcome back to my channel."
  3. Filler removal tightens pacing without the viewer noticing — Cutting "um"s, "uh"s, and pauses over 1.5 seconds typically removes 15-25% of a video's duration. The remaining content feels energetic and confident. Viewers perceive the speaker as more articulate — they never notice what was removed.
  4. Chapters serve both viewers and the algorithm — Viewers use chapters to skip to relevant sections (increasing satisfaction and session time). YouTube uses chapters to understand video content structure (improving search ranking). Chapters benefit both audiences and discoverability.
  5. Shorts extracted from long-form drive subscriber growth — A 50-second Short showing the video's best moment reaches audiences who will never find the full video through search. Those viewers click through to the channel, discover the long-form content, and subscribe. Shorts are the top-of-funnel; long-form is the conversion.

Output Formats

FormatResolutionUse Case
MP4 16:91080p / 4KYouTube main upload
MP4 9:161080x1920YouTube Shorts
PNG1280x720Thumbnail candidates
TXT—Chapter timestamps

Related Skills

平台分布

OpenClaw

71.55%
按下载量换算1,390

安全审计

VirusTotal

通过

ClawScan

可疑

Static analysis

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

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。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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