youtube-digest

youtube-digest is a skill for Claude Code from team-attention/plugins-for-claude-natives. It costs 89 tokens per session (947 once invoked), scanned A, original, MIT.

A workflow for turning a YouTube video into a summary, insights, translated transcript, and comprehension quiz. YouTube is a video-sharing website.

In plain words
What is it for?
Use it to process a YouTube URL, correct transcript names using web research, create a structured reading document, and produce a nine-question quiz.
Why use it?
It saves the effort of watching, transcribing, translating, and checking understanding manually.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin. Also seen: names the AskUserQuestion tool; mentions Claude Code.

Part of the youtube-digest plugin — 1 skill shipped together

Good fit Use it to process a YouTube URL, correct transcript names using web research, create a structured reading document, and produce a nine-question quiz.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/team-attention/plugins-for-claude-natives/youtube-digest
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add team-attention/plugins-for-claude-natives --skill youtube-digest
Clone the repo
git clone --depth 1 https://github.com/team-attention/plugins-for-claude-natives

Made for: Claude Code.

Or install youtube-digest, the plugin that ships this one along with the rest of its 1 skill.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for youtube-digest

README.md
[![agentmods](https://agentmods.dev/badge/skills/team-attention/plugins-for-claude-natives/youtube-digest/github.svg)](https://agentmods.dev/skills/team-attention/plugins-for-claude-natives/youtube-digest)
Your own site
<a href="https://agentmods.dev/skills/team-attention/plugins-for-claude-natives/youtube-digest"><img src="https://agentmods.dev/badge/skills/team-attention/plugins-for-claude-natives/youtube-digest/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for youtube-digest

Your own site · 80×15
<a href="https://agentmods.dev/skills/team-attention/plugins-for-claude-natives/youtube-digest"><img src="https://agentmods.dev/badge/skills/team-attention/plugins-for-claude-natives/youtube-digest.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 89 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 947 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector warn 7 Sept 2026
SkillSpector: 1 finding, up to high

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

  • high YARA Match · line 114
    YARA rule matched a known malware signature (reverse shell, backdoor, ransomware, C2 framework, or info stealer).
    Fix: Remove the malware payload or compromised file entirely. Investigate how it entered the skill and audit all other artifacts for additional indicators of compromise.
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00089 $0.00947
Opus 5 $0.00044 $0.00474
Sonnet 5 $0.00018 $0.00189
Haiku 4.5 $0.00009 $0.00095

Measured 11d ago against content hash 0cd74f11b7af, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

youtube-digest scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.

The scan reads SKILL.md. This mod also ships 2 executable files (scripts/extract_metadata.sh, scripts/extract_transcript.sh), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

plugins/youtube-digest/skills/youtube-digest/SKILL.md · 122 lines

What it actually says

YouTube Digest

YouTube 영상 분석 → 요약/인사이트/번역 문서 생성 → 퀴즈 테스트.

워크플로우

1. 메타데이터 수집

scripts/extract_metadata.sh "<URL>"

추출: title, description, channel, upload_date, duration, tags

2. Transcript 추출

scripts/extract_transcript.sh "<URL>" [output_dir]

우선순위: 수동 자막(ko→en) > 자동 생성 자막(ko→en)

3. 맥락 파악 (WebSearch)

웹 검색으로 고유명사 정확한 표기 수집:

  • "{영상 제목}" {채널명} summary
  • "{발표자명}" {주제 키워드}

4. Transcript 교정

자동 자막의 고유명사 오인식을 웹 검색 결과로 대체:

  • Kora → Cora, cloud code → Claude Code, every → Every.to

5. 문서 생성

---
title: {영상 제목}
url: {YouTube URL}
channel: {채널명}
date: {업로드 날짜}
duration: {영상 길이}
processed_at: {처리 일시}
---

# {영상 제목}

## 요약
{3-5문장 요약 + 주요 포인트 3개}

## 인사이트
### 핵심 아이디어
### 적용 가능한 점

## 전체 스크립트 (한글 번역)
[00:00] ...

6. 파일 저장

위치: research/readings/youtube/{YYYY-MM-DD}-{sanitized-title}.md

7. 학습 퀴즈

3단계 × 3문제 = 총 9문제. AskUserQuestion으로 각 단계 3문제 동시 출제.

단계 난이도 출제 기준
1 기본 핵심 인사이트, 주요 개념
2 중급 인사이트 + 세부 내용 연결
3 심화 세부 내용, 적용/분석

문제 유형 상세: references/quiz-patterns.md

결과 처리

틀린 문제에 대해 정답과 해설 제공 후, 문서 끝에 퀴즈 결과 추가:

## 퀴즈 결과

총점: 7/9 (78%) | 1단계 3/3 ✅ | 2단계 2/3 | 3단계 2/3

### 오답 노트

**Q5**: {질문}
- 선택: B → 정답: C
- {1-2문장 해설}

8. 후속 선택

퀴즈 완료 후 AskUserQuestion:

  • 한 번 더 퀴즈: 다른 문제로 재테스트
  • Deep Research: 웹 심층 조사 (references/deep-research.md 참조)
  • 종료: 마무리

참고사항

자막 언어 우선순위

  1. 한국어 수동 → 2. 영어 수동 → 3. 한국어 자동 → 4. 영어 자동

불완전한 자막 처리

  • 고유명사 오인식: 4단계에서 일괄 대체
  • 이해 불가 부분: [불명확] 표시

yt-dlp 옵션

  • --list-subs: 자막 목록 확인
  • --cookies-from-browser chrome: 로그인 필요 시

리소스

  • scripts/extract_metadata.sh - 메타데이터 추출
  • scripts/extract_transcript.sh - 자막 추출
  • references/quiz-patterns.md - 퀴즈 문제 유형 상세
  • references/deep-research.md - Deep Research 워크플로우
Files

What ships with it

4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 11d ago First seen · 122 lines · 89 tokens per session scan A 0cd74f11b7af

Subscribe to this mod's changes

youtube-digest is a skill published in the GitHub repository team-attention/plugins-for-claude-natives (824 stars, last pushed 4mo ago), licensed MIT. It adds 89 tokens to every session and 947 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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