dsh-youtube-study-notes
将 YouTube 链接转换为学习笔记：提取视频自带字幕，可选使用视觉模型读取屏幕内容，然后询问你自己的笔记并将所有内容合并为一份文档。
插件会安装到这里；不确定时保持 web。
npx -y @deepseek-ai/dsh plugin --profile web add github:jagathsrujan/dsh-youtube-study-notes#a8fd6a98aa0fd19a14b763a1213c3ad8d3f5566bYoutube Study Notes 以 dsh-youtube-study-notes 发布，当前版本为 0.2.0。Plugin Hub 会校验它的 manifest，并保存精确安装来源，便于复现安装结果。
A DeepSeek Harness (dsh) plugin that turns a YouTube link into study notes — and then gets your notes involved.
Give it a link. It pulls the video's own captions, samples the frames so it can read what was on screen, stops and asks what you took from it, and merges all three into one document.
The middle step is the point. Every other video-to-notes tool produces the same summary for everyone. This one writes the notes you'd have written, if you'd been taking notes.
| Step | What happens |
|---|---|
| 1 | Captions — yt-dlp fetches the caption track YouTube already publishes. Human-written preferred, auto-generated as fallback. No ASR, no third-party transcript sites. |
| 2 | Outline — the timestamped transcript is read in full and outlined by chapter. |
| 3 | Visual pass (optional) — ffmpeg scene-change detection samples one frame per shot; a vision model reads the on-screen text. Catches slides, code, formulas and diagrams the narration never says out loud. |
| 4 | Ask — the agent stops and asks what you noticed, in your words. Type it or dictate it. |
| 5 | Merge — transcript + on-screen evidence + your observations, into one markdown file. |
| 6 | Save — notes.md, written beside the transcript so the evidence stays with it. |
| 7 | Clean up — the frame stills and any video file are removed; the transcript stays. |
The video is never kept: step 3 downloads it into a temporary directory that is deleted
when the script exits (--keep-video is the only way it survives). The frame JPEGs are
removed by step 7 once notes.md exists — they are derived from someone else's video and
are typically tens of megabytes, against a few kilobytes of transcript.
Kept, because it is the evidence behind every spoken claim and costs almost nothing:
transcript.md transcript.txt meta.json notes.md
cleanup.py refuses to act on a directory that does not carry one of those markers, so
it cannot be pointed at an unrelated folder. Pass --keep-frames to review the stills
yourself.
dsh plugin --profile <your-profile> add dsh-youtube-study-notes
Restart the profile afterwards — bundle changes apply at boot.
Then trigger it either way:
/youtube-study-notes https://youtu.be/VIDEO_ID
or just ask in plain language ("get me notes from this video: …"). A link plus a request for notes, a summary, or study material is enough.
Desktop clients: the desktop app manages its own profile. Use its Plugins → Add plugin flow rather than the CLI, and enable the bundle when the install finishes.
The plugin ships no runtime code — its substance is the Skill, which drives local tools.
dsh does not install these for you:
| Tool | Role | Install |
|---|---|---|
python3 | runs the bundled scripts (standard library only) | brew install python3 |
ffmpeg / ffprobe | audio/video probing, scene detection, frame extraction | brew install ffmpeg |
yt-dlp | caption listing and download | brew install yt-dlp |
The Skill detects missing tools, names them, and waits for your confirmation before installing anything.
Step 3 needs a model that accepts images, and many model entries do not — even ones
whose name says "vision". A model is only offered images if its configuration declares
input: [text, image]; entries otherwise default to ["text"], and the harness refuses
the image before attaching it with:
model "<provider>/<model>" does not declare image input
The Skill handles this: it tries read_image on a single frame first. If the session
model can see images, it reads the frames directly. If not, it delegates through the
workflow tool's per-agent provider / model override, and asks you once which
image-capable model to use.
To make a model explicitly image-capable, declare it in your profile's
cordis.patch.yml (the file your profile tells you to edit — not cordis.yml):
- id: llm-pi-ai
name: "@deepseek-ai/dsh-llm-pi-ai"
config:
providers:
your-provider:
# ...existing provider config...
models:
- id: "your-vision-model"
name: "Your Vision Model"
contextWindow: 200000
input:
- text
- image
A profile with live reload picks this up without a restart.
Only declare this if the endpoint actually serves images. Over-claiming admits an image the provider then rejects mid-turn, after the message is already durable. The declaration is not verified for you.
If you skip all of this, the plugin still works — it just produces transcript-only notes, and says so rather than pretending it saw the screen.
1 as l. Fine for prose, worth double-checking in code and formulas. The skill is
instructed to flag anything load-bearing that looks ambiguous.package.json declares dsh.bundle → cordis.patch.yml
cordis.patch.yml inserts one @deepseek-ai/dsh-skill-filesystem row
lib/index.js resolveSkillRoot() — resolves skills/ from the profile's own module identity
skills/youtube-study-notes/
SKILL.md the workflow
scripts/ yt_transcript.py, video_frames.py, cleanup.py
references/ notes template and merge rules
The bundle mounts its skills/ directory as an isolated provider
(includeDefaultRoots: false), so installing it contributes exactly one skill and
cannot shadow or reorder the ones you already have.
MIT — see LICENSE.