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Turn YouTube talks into local study notes with slides, transcripts, editable annotations, and a markdown-backed viewer.
デフォルトでは、最初にソースを確認する Prompt が選択されています。直接コマンドに切り替えるか、ローカルコピーをダウンロードすることもできます。
インストールを決める前に、SKILL.md と SkillsMP に表示されている付属ファイルをお読みください。
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
直接コマンドでは確認用 Prompt が省略されます。実行前にソースを確認してください。
npx skills add https://github.com/canar1406/thaytrieu --skill youtube-notetakerコマンドは1行のまま表示されます。コピー前に横へスクロールして全体を確認してください。
ローカルで確認しますか？SkillsMP が現在取得できるファイルをダウンロードできます。
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SKILL.md を表示中
| name | youtube-notetaker |
| description | Turn YouTube talks into local study notes with slides, transcripts, editable annotations, and a markdown-backed viewer. |
| category | video |
| risk | safe |
| source | official |
| source_repo | dair-ai/dair-academy-plugins |
| source_type | official |
| date_added | 2026-06-19 |
| author | DAIR.AI |
| license | MIT |
| license_source | https://github.com/dair-ai/dair-academy-plugins/blob/main/README.md#license |
| tags | ["dair-academy","ai","workflow"] |
| tools | ["claude-code","codex-cli","cursor"] |
Use when this workflow matches the user request: >
Source: dair-ai/dair-academy-plugins (MIT).
Build a personal library of YouTube talks you study with. Each video becomes one plain markdown file: slide snapshots at their timestamps, a full timestamped transcript, and editable notes. A small bundled server renders the library as an interactive deep-dive in the browser. No database, no cloud service. Everything is files on disk you fully own.
The markdown library is the single source of truth. The artifact is a thin HTML shell that fetches from the server and writes notes back. Never hardcode video data into the HTML.
VIDEO_LIBRARY_DIR (default ~/video-deepdives/).
RtywqDFBYnQ.md).slides array.[HH:MM:SS] text lines._media/ holds slide images, namespaced per video as <youtube_id>-slide-NN.jpg
to avoid collisions between videos.scripts/serve.py, a single stdlib + PyYAML file. Start it with:
python3 scripts/serve.py --dir ~/video-deepdives --port 8000
/ and a small API the artifact talks to:
GET /api/video-deepdives (front page fetches this) lists every video.GET /api/video-deepdives/<id> returns one video {meta, body}.GET /api/video-deepdives/_media/<file> serves a slide image.PATCH /api/video-deepdives/<id> with {fields:{slides:[...]}} writes notes back./api/video-deepdives URL namespace is local to the bundled server.reference/artifact.html, served by at . A clean reference copy;
only rewrite it if the user wants a UI change. For new videos, leave it alone.serve.py/yt-dlp and ffmpeg on PATH (download + frame/scene extraction).Pillow (contact sheet) and PyYAML (markdown file + server).
pip install yt-dlp pillow pyyaml # ffmpeg via your package manager
All helper scripts are in scripts/. Work in a scratch dir (e.g. /tmp/ytnote-<id>/), then
copy final assets into the library. Set VIDEO_LIBRARY_DIR once per shell if you don't want the
default. Do not use em dashes (—) or arrows (→) in notes/titles.
scripts/setup.sh "<youtube_url_or_id>"
Prints the 11-char YTID, the scratch dir, the target library path, and whether YouTube
embedding is allowed (oembed 200) or blocked (oembed 401, e.g. some university talks).
If blocked, inline playback won't work but the artifact degrades gracefully to an "open at this
moment on YouTube" link, so proceed normally.
scripts/download.sh "<YTID>" /tmp/ytnote-<YTID>
Uses yt-dlp to grab the video (≤720p is plenty for slide frames) and the best available
subtitles (manual if present, else auto-captions) as .vtt. Also fetches title/uploader.
scripts/detect_slides.sh /tmp/ytnote-<YTID>/video.mp4 /tmp/ytnote-<YTID>
Runs ffmpeg scene detection (select='gt(scene,0.3)') and writes scene_times.txt (seconds).
0.3 is a good default; lower it (0.2) for subtle slide decks, raise it (0.4) for busy video.
python3 scripts/contact_sheet.py /tmp/ytnote-<YTID>/video.mp4 /tmp/ytnote-<YTID>/scene_times.txt /tmp/ytnote-<YTID>/contact.jpg
Read contact.jpg (labeled with index + timestamp). This is the human-judgment step: keep
frames that are real content slides; drop talking-head shots, transitions, duplicates, and
blurry mid-animation frames. Save the kept timestamps (seconds) to /tmp/ytnote-<YTID>/keep.txt,
one per line. Typical talk yields 15-25 slides.
python3 scripts/extract_slides.py <YTID> /tmp/ytnote-<YTID>/video.mp4 /tmp/ytnote-<YTID>/keep.txt > /tmp/ytnote-<YTID>/slides.json
Extracts each kept timestamp at 1280px wide, JPEG, and copies them into
$VIDEO_LIBRARY_DIR/_media/ as <YTID>-slide-01.jpg, -02.jpg, … (numbered in time order).
Progress goes to stderr; a clean slides.json scaffold prints to stdout, so redirect it to a
file as shown, then fill in title and note.
Tip: talks are often a slide + speaker-cam composite, and speakers flip back and forth, so the
same slide appears at several timestamps. Keep the cleanest instance of each, and re-anchor each
slide's t to where it is actually discussed in the transcript (better "play from here" UX).
python3 scripts/vtt_to_transcript.py /tmp/ytnote-<YTID>/*.vtt /tmp/ytnote-<YTID>/transcript.txt
Parses the VTT into clean, de-duplicated [HH:MM:SS] text lines (YouTube auto-captions repeat
rolling text; the script collapses it). This becomes the markdown body.
For each kept slide, write a 1-3 sentence note grounded in the transcript around that timestamp
(don't invent claims). Then assemble:
python3 scripts/write_library_item.py \
--id <YTID> \
--title "Talk title" \
--speaker "Name, Role, Org" \
--tags tag1,tag2,tag3 \
--slides /tmp/ytnote-<YTID>/slides.json \
--transcript /tmp/ytnote-<YTID>/transcript.txt
Writes $VIDEO_LIBRARY_DIR/<YTID>.md with correct frontmatter + body.
python3 scripts/serve.py --dir "$VIDEO_LIBRARY_DIR" --port 8000 &
scripts/verify.sh <YTID> # defaults to http://127.0.0.1:8000
verify.sh curls the collection list, the item, the first slide image, and the artifact,
asserting HTTP 200 and that the new id appears in the index. Then open
http://127.0.0.1:8000/#/<YTID> in a browser to confirm slides + transcript + notes render.
---
id: RtywqDFBYnQ
title: Memory and dreaming for self-learning agents
youtube_id: RtywqDFBYnQ
speaker: Mahesh, Product Manager, Platform team at Anthropic
source_url: https://www.youtube.com/watch?v=RtywqDFBYnQ
slide_count: 19
created: '2026-05-25'
tags: [anthropic, memory, agents]
slides:
- idx: 1
t: 55.7 # seconds (float ok), used for seeking
mmss: 00:55 # display label
title: Agent primitives have evolved
note: One to three sentences grounded in the transcript at this timestamp.
img: /api/video-deepdives/_media/RtywqDFBYnQ-slide-01.jpg
# ... more slides
---
## Transcript
[00:00:08] Hello, everyone...
[00:00:11] ...
Notes:
idx can be sparse/non-contiguous; the artifact sorts slides by t, so ordering is by
timestamp, not idx.img is always a /api/video-deepdives/_media/<file> URL (served by serve.py),
never base64.note is what the user edits in the UI; PATCH writes the whole slides array back.<YTID>-slide-NN.jpg. Never reuse bare
slide-NN.jpg for a new video..md file is directly inside --dir (not a
subfolder) and the filename is <YTID>.md.VIDEO_LIBRARY_DIR) controls where the library lives.serve.py, stdlib + PyYAML) renders everything and handles
note write-back. Drop it anywhere Python runs.