panda-video-automation-notebooklm

[ENTRY POINT] Full NotebookLM pipeline — research → generate video → download → watermark → trim → generate meta files → cover → prepare upload → publish. Single entry point for all end-to-end video tasks.

Cross-platform video upload (login only when explicitly requested) — Bilibili, Douyin, Kuaishou, Weixin Video, YouTube

Research a topic with NotebookLM — create notebook, run deep research, auto-import sources, and summarize. Checks notebooklm installation, login, and skill installation first.

Create a NotebookLM notebook for a topic and generate a video overview in Simplified Chinese. Delegates installation and login checks to notebooklm-research skill.

Overview of the Panda Video Automation pipeline — from NotebookLM research to video upload. Run this skill to see the full workflow.

Download a NotebookLM video artifact to /input/video.mp4 and create a title.json with its title.

Generate minimalist academic-style cover images via Python/Pillow — black (#000000) solid background, centered bold white title (120px), thin subtitle (76px) below. ONLY text on black — no graphics, icons, dates, or logos. TRIGGER: activates when user mentions "simple cover", "minimalist cover", "academic cover", or "cover" without further qualifiers. For "detailed cover", "complex cover", "report-style cover", or "detailed cover"/"complex cover", do NOT use this skill — instead generate a detailed infographic with --style professional or --style scientific.

Prepare a downloaded NotebookLM video for upload: create the title JSON file and move the video to the correct path expected by the Bilibili/Douyin upload e2e scripts.

List all NotebookLM notebooks and show their artifact status (video, infographic, etc.) in a summary table.

Check all prerequisites (Python, Node.js, notebooklm-py, PVA) before running any workflow.