name	youtube-render-pdf
description	Use when the user provides a YouTube URL and wants LaTeX course notes rendered as PDF. Triggers: YouTube link, '这个视频', '整理这节课', 'YouTube 笔记'.

YouTube Render PDF

Turn a YouTube video into a complete, compileable .tex note and a rendered PDF.

Goal

Produce a professional Chinese lecture note from a YouTube URL. The output must:

use the video's actual teaching content rather than subtitle transcription alone
place the video's original cover image on the front page
include all necessary high-value key frames as figures
end with a final synthesis section
be a complete .tex document compiled successfully to PDF

Shared Rules

Read ../video-render-common/writing-and-figures.md for all writing rules, teaching content rules, figure handling, figure time provenance, and visualization guidelines. These rules are mandatory.

Supplementary Material Discovery (Optional but Recommended)

Before downloading the video, search for official slides, lecture notes, or other supplementary materials:

Extract course code, speaker name, institution, year from video title/description
Search: video description links → course website → GitHub repos → speaker's website → web search
Look for BOTH slides AND official lecture notes/readings — lecture notes provide precise formulas, proofs, and context that subtitles often garble
If slides are a PDF, extract pages as images (magick -density 200 slides.pdf slides-images/slide-%03d.jpg) — prefer these over video frames
Google Slides / Google Docs: some courses host slides on Google Slides instead of PDF. Strategy:
- Try appending /export/pdf to the Google Slides URL to download as PDF (e.g., https://docs.google.com/presentation/d/XXXXX/export/pdf)
- If export fails, note the URL in the metadata and fall back to video frame extraction
When slides are available, use them as the primary figure source and make sure slide screenshots appear in the final PDF; still download video for supplementary frames
When official lecture notes are available, use them to verify formula accuracy and fill in details the speaker glossed over verbally
If no materials found, proceed with video-only mode

Source Acquisition

Inspect video metadata first (title, chapters, duration, thumbnails, subtitles)
Probe formats and choose the best working format for the task.
- For a one-off user request to download the original video, keep the highest usable original.
- For large podcast/interview batches, default to a low/medium-resolution work copy for frames/transcription and do not retain full-resolution originals unless requested.
Download original cover image (highest-res thumbnail), save locally for front page
Prefer manual subtitles over auto-generated; preserve timestamps
If YouTube requires authentication, use the repository helper:
```
tools/scripts/youtube_auth_check.sh "<URL>"
```
This validates cookies, browser impersonation, bgutil PO-token support, and EJS challenge solving.
Keep all required artifacts local (metadata, cover, subtitles/transcript, selected frames, final TeX/PDF). Avoid committing large media.

Delivery

the final .tex file
the downloaded cover image
any extracted or generated figure assets
the compiled PDF
evidence that the compiled PDF was visually inspected via rendered pages/contact sheet and corrected if needed

Rules

Follow ai-course-notes/CLAUDE.md conventions for LaTeX structure, boxes, and figures
Always compile PDF twice (xelatex two passes) to resolve references
After the final compile, follow the PDF Visual QA workflow in ../video-render-common/writing-and-figures.md; do not mark the note complete until rendered pages have been inspected and layout/rendering issues fixed
Prefer manual subtitles; fall back to auto-generated or youtube-transcript-api
For videos with no usable subtitles, use tools/scripts/transcribe_faster_whisper.py with CUDA as described in the shared rules
For fixed-camera interviews with no slides/demos/whiteboard, use the cover only and do not repeat speaker-head frames in the body; generate concept diagrams/tables instead
Skip non-teaching content (intros, sponsor segments, channel promos)
Use [H] float placement for all figures
Set \noteauthors to "基于公开课程资料整理" or "基于 [Speaker Name] 授课内容整理" — never "XX & Codex" or similar
Set \notedate to the video's publish date or course semester — never \today
Always fill \videourl with the URL the user provided
Keep \repourl{https://github.com/hqhq1025/ai-course-notes} unchanged from the template default
Do NOT use TikZ for any visualization — it causes compilation timeouts. Use tables or pre-generated images instead

Asset

assets/notes-template.tex: default LaTeX template to copy and fill

YouTube Render PDF

Turn a YouTube video into a complete, compileable .tex note and a rendered PDF.

Goal

Produce a professional Chinese lecture note from a YouTube URL. The output must:

use the video's actual teaching content rather than subtitle transcription alone

place the video's original cover image on the front page

include all necessary high-value key frames as figures

end with a final synthesis section

be a complete .tex document compiled successfully to PDF

Shared Rules

Supplementary Material Discovery (Optional but Recommended)

Before downloading the video, search for official slides, lecture notes, or other supplementary materials:

Extract course code, speaker name, institution, year from video title/description

Search: video description links → course website → GitHub repos → speaker's website → web search

Look for BOTH slides AND official lecture notes/readings — lecture notes provide precise formulas, proofs, and context that subtitles often garble

If slides are a PDF, extract pages as images (magick -density 200 slides.pdf slides-images/slide-%03d.jpg) — prefer these over video frames

Google Slides / Google Docs: some courses host slides on Google Slides instead of PDF. Strategy:

Try appending /export/pdf to the Google Slides URL to download as PDF (e.g., https://docs.google.com/presentation/d/XXXXX/export/pdf)
If export fails, note the URL in the metadata and fall back to video frame extraction

When slides are available, use them as the primary figure source and make sure slide screenshots appear in the final PDF; still download video for supplementary frames

When official lecture notes are available, use them to verify formula accuracy and fill in details the speaker glossed over verbally

If no materials found, proceed with video-only mode

Source Acquisition

Inspect video metadata first (title, chapters, duration, thumbnails, subtitles)

Probe formats and choose the best working format for the task.

For a one-off user request to download the original video, keep the highest usable original.
For large podcast/interview batches, default to a low/medium-resolution work copy for frames/transcription and do not retain full-resolution originals unless requested.

Download original cover image (highest-res thumbnail), save locally for front page

Prefer manual subtitles over auto-generated; preserve timestamps

If YouTube requires authentication, use the repository helper:

tools/scripts/youtube_auth_check.sh "<URL>"

This validates cookies, browser impersonation, bgutil PO-token support, and EJS challenge solving.

Keep all required artifacts local (metadata, cover, subtitles/transcript, selected frames, final TeX/PDF). Avoid committing large media.

Delivery

the final .tex file

the downloaded cover image

any extracted or generated figure assets

the compiled PDF

evidence that the compiled PDF was visually inspected via rendered pages/contact sheet and corrected if needed

Rules

Follow ai-course-notes/CLAUDE.md conventions for LaTeX structure, boxes, and figures

Always compile PDF twice (xelatex two passes) to resolve references

After the final compile, follow the PDF Visual QA workflow in ../video-render-common/writing-and-figures.md; do not mark the note complete until rendered pages have been inspected and layout/rendering issues fixed

Prefer manual subtitles; fall back to auto-generated or youtube-transcript-api

For videos with no usable subtitles, use tools/scripts/transcribe_faster_whisper.py with CUDA as described in the shared rules

For fixed-camera interviews with no slides/demos/whiteboard, use the cover only and do not repeat speaker-head frames in the body; generate concept diagrams/tables instead

Skip non-teaching content (intros, sponsor segments, channel promos)

Use [H] float placement for all figures

Set \noteauthors to "基于公开课程资料整理" or "基于 [Speaker Name] 授课内容整理" — never "XX & Codex" or similar

Set \notedate to the video's publish date or course semester — never \today

Always fill \videourl with the URL the user provided

Keep \repourl{https://github.com/hqhq1025/ai-course-notes} unchanged from the template default

Do NOT use TikZ for any visualization — it causes compilation timeouts. Use tables or pre-generated images instead

Asset

assets/notes-template.tex: default LaTeX template to copy and fill

youtube-render-pdf