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Agent Skill · 研究检索

youtube-catchup(YouTube 追赶)

youtube-catchup 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 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。

youtube-catchup 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为执行命令;许可证信息还不明确,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 65,745。

总安装 275周安装 11GitHub Stars 42下载 89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
unknown

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-catchup(YouTube 追赶)
来源仓库:https://github.com/taylorhuston/local-life-manager
仓库路径:skills/youtube-catchup
安装命令:
npx skills add https://github.com/taylorhuston/local-life-manager --skill youtube-catchup
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 npx skills 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。

skills.shnpx skills
npx skills add https://github.com/taylorhuston/local-life-manager --skill youtube-catchup

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。

用于查找、检索和筛选相关信息。适合根据关键词或任务场景快速定位候选结果。可结合来源仓库和 README 核验具体用法。安装命令:npx skills add https://github.com/taylorhuston/local-life-manager --skill youtube-catchup。

安装前建议确认权限范围和维护状态。

来源仓库内路径:skills/youtube-catchup

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 Codex、Claude、Cursor、Gemini CLI;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 npx skills add https://github.com/taylorhuston/local-life-manager --skill youtub…;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

    它需要执行命令,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-catchup 怎么安装?

在终端执行 npx skills add https://github.com/taylorhuston/local-life-manager --skill youtube-catchup。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

youtube-catchup 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。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-catchup 需要什么权限?

权限级别是执行命令。安装流程涉及命令执行,可能通过 npx skills add https://github.com/taylorhuston/local-life-manager --skill youtube-catchup 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

youtube-catchup 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

youtube-catchup 用的人多吗?

公开数据是累计安装 275 次,近一周安装 11 次,下载 89 次,GitHub 42 星。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-catchup 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

  • 同时适配 4 种工具,换客户端不用重新找替代品。

先想清楚的地方

  • 需要执行命令,装到常用环境前要先评估影响面。
  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 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 Codex,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

  • 不能接受 Skill 在你机器上执行命令的人。
  • 需要明确授权才能用到公司项目里的场景。
  • 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-catchup

YouTube 追赶 · 本页

27542执行命令未明确有提醒

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

Catch up on YouTube channels by auto-summarizing new videos.

Architecture

This skill uses a coordinator + subagent pattern:

  • Coordinator (Sonnet): Fetches video lists, filters new videos, spawns summarization agents, updates state
  • Summarizer (Haiku subagents): Each video gets its own agent with fresh context to ensure quality

Prerequisites

Requires yt-dlp:

pip install yt-dlp

Helper Script

Use scripts/youtube_helper.py for fetching data:

# Get NEW videos uploaded since a date (default operation)
python3 scripts/youtube_helper.py new @handle YYYY-MM-DD [processed_ids]

# Backfill: get all unprocessed videos regardless of date (explicit request only)
python3 scripts/youtube_helper.py backfill @handle [limit] [processed_ids]

# Fast channel list (no date info - rarely needed)
python3 scripts/youtube_helper.py channel @handle [limit]

# Get video metadata
python3 scripts/youtube_helper.py video VIDEO_ID

# Get transcript only
python3 scripts/youtube_helper.py transcript VIDEO_ID

# Get metadata + transcript (for summarization)
python3 scripts/youtube_helper.py full VIDEO_ID

Important: The new command checks upload dates to avoid backfilling old content. Use backfill only when explicitly requested.

Workflow

Phase 1: Discovery (Coordinator)

  1. Load configuration

- Read channel list from references/channels.json - Read processed videos list from references/state.json - Note the last_run date for filtering

  1. For each enabled channel:

- Run: python3 scripts/youtube_helper.py new @handle {last_run} {processed_ids} - This returns ONLY videos uploaded since last_run that aren't in processed list - Skip videos under 5 minutes (likely promotional/announcement fluff) - Collect list of new videos needing summarization

  1. Deduplicate against existing notes:

- Before processing, check if a note with that video URL already exists: grep -rl "youtube.com/watch?v={video_id}" "my-vault/07 Knowledge Base/Capture/Videos/" - If a note exists: skip that video (already processed, just missing from state) - This prevents duplicates even if state.json is out of sync

  1. Report findings to user:

- Show count of new videos per channel - Ask user how to proceed (all, select channels, limit count)

Backfill mode: If user explicitly requests backfilling old videos, use backfill command instead of new.

Phase 2: Summarization (Haiku Subagents)

  1. Spawn subagents for summarization:

- Use Task tool with model: haiku and subagent_type: general-purpose - Process 4-6 videos in parallel (multiple Task calls in single message) - Each subagent handles ONE video with fresh context - Wait for batch to complete before starting next batch

  1. Subagent prompt template:
Summarize this YouTube video and create a note.

VIDEO_ID: {video_id}
CHANNEL: {channel_name}
TAGS: {tags}
OUTPUT_PATH: {output_path}

IMPORTANT: Use the OUTPUT_PATH exactly as given with literal spaces (e.g., "my-vault/07 Knowledge Base/...").
Do NOT escape spaces with backslashes - this creates directories with literal backslash characters in the name.

STEPS:
1. Run: cd /home/taylor/my-life/.claude/skills/youtube-catchup && python3 scripts/youtube_helper.py full {video_id}
2. If NO_TRANSCRIPT_AVAILABLE, create note with that notice (see format below)
3. Otherwise, summarize the transcript into detailed bullet points
4. Identify any discoveries (tools, products, frameworks worth exploring)
5. Write the note file to the output path
6. Return: SUCCESS or FAILURE with brief reason

SUMMARY GUIDELINES:
- 5-15 bullet points depending on video length/density
- Each bullet should capture a complete idea with specific details
- Include names, numbers, tools, key arguments mentioned
- For tutorials: use step-by-step format with code snippets

NO TRANSCRIPT FORMAT:
## Summary

NO TRANSCRIPT AVAILABLE - REWATCH VIDEO TO CREATE PROPER SUMMARY

- Main topic: {title}

NOTE TEMPLATE:
---
class: Video
aliases:
tags: {tags}
lastReviewedDate:
reviewFrequency:
review:
created: {date}
modified: {date}
media: https://www.youtube.com/watch?v={video_id}
publishDate: {publish_date}
status: Summarized
duration: {duration}
cssclasses:
archived:
---
Related:

## Summary

- Main topic: [one sentence]
- [Key points with specific details...]

## Discoveries

- [[Product Name]] - brief context
- (or "None")

## Why Watch?

[One sentence on whether worth actually watching]

Phase 3: Completion (Coordinator)

  1. Collect results:

- Track which videos succeeded/failed - Report any failures to user

  1. Update state:

- Add successfully processed video IDs to references/state.json - Report summary of what was processed

Tagging

Use tags from the canonical list in my-vault/09 System/Tag Index.md.

Each channel in references/channels.json has a tags array specifying default tags. Format: tags: ["tag1", "tag2"]

Output Paths

Video notes: my-vault/07 Knowledge Base/Capture/Videos/{channel_folder}/{title}.md Discovery notes: my-vault/01 Inbox/{name}.md

CRITICAL - Path Handling:

  • NEVER escape spaces with backslashes in paths passed to subagents or the Write tool
  • Use paths exactly as shown: my-vault/07 Knowledge Base/... (with literal spaces)
  • The Write tool handles spaces correctly - backslash escaping creates literal \ characters in directory names
  • When using Bash commands, wrap paths in double quotes: "my-vault/07 Knowledge Base/..."

Channel Config

Edit references/channels.json:

{
  "channels": [
    {
      "name": "Channel Name",
      "handle": "@YouTubeHandle",
      "folder": "Folder Name",
      "tags": ["tag1", "tag2"],
      "priority": "high",
      "enabled": true
    }
  ]
}

Tutorial Videos - Special Handling

If the video is a tutorial, the subagent should use step-by-step format:

## Summary

Tutorial: [What you'll build/learn]

### Prerequisites
- Required software/tools
- Prior knowledge needed

### Steps

#### 1. [First major step]
- Specific action
- Code snippet if applicable
- Expected result

#### 2. [Continue for all steps...]

### Final Result
- What you should have
- How to verify it works

### Troubleshooting
- Common issues and solutions

Tutorial goals: Someone should be able to follow WITHOUT watching the video.

Discovery Notes

Subagents should identify products, services, frameworks, tools worth exploring.

Create notes for: New tools, interesting frameworks, notable projects Skip: Well-known things (Python, AWS, React), generic concepts

Discovery note format:

---
class: Note
tags: ["tag1"]
---
Related: [[Video Title]]

## What is it?
[One sentence description]

## Why look into it?
[Why it seemed interesting]

## Links
- [URL if mentioned]

Error Handling

  • If subagent fails, log the video ID and continue with others
  • If transcript unavailable, create note with NO TRANSCRIPT notice
  • Report all failures at the end for manual review

Example Coordinator Flow

1. Load channels.json, state.json (last_run: 2026-01-16)
2. Fetch NEW videos (uploaded since last_run):
   python3 youtube_helper.py new @t3dotgg 2026-01-16 id1,id2,...
   - Theo: 2 new videos
   - HealthyGamerGG: 1 new video
   - LangChain: 0 new videos
3. User confirms: "Process all"
4. Batch 1 - spawn 3 Haiku agents:
   - Task(video_1, theo, ...)
   - Task(video_2, theo, ...)
   - Task(video_3, healthygamer, ...)
5. Wait for batch completion
6. Update state.json with successful IDs
7. Update last_run to today's date
8. Report: "Processed 3 videos, 0 failures"

For explicit backfill:

User: "backfill Gamers Nexus videos"
1. Use: python3 youtube_helper.py backfill @GamersNexus 50 id1,id2,...
2. This returns ALL unprocessed videos regardless of upload date
3. Proceed with normal summarization workflow

平台分布

Codex

34.89%
按下载量换算31

Claude

32.38%
按下载量换算29

Cursor

19.87%
按下载量换算18

Gemini CLI

10.21%
按下载量换算9

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

可疑

权限和风险

执行命令

安装流程涉及命令执行,可能通过 npx skills add https://github.com/taylorhuston/local-life-manager --skill youtube-catchup 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

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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