notebooklm-studio

name	notebooklm-studio
description	Import sources (URLs, YouTube, files, text) into Google NotebookLM and generate artifacts: podcasts, videos, reports, quizzes, flashcards, mind maps, slide decks, infographics, data tables. Use when users want to study from web content, create learning materials from URLs or documents, generate quizzes from articles, or produce study aids.
description_zh	NotebookLM 学习工作室：导入多种来源，生成播客、测验、抽认卡、思维导图等学习产物
description_en	NotebookLM Studio: import sources and generate podcasts, quizzes, flashcards, mind maps and more
version	2.1.3
homepage	https://clawhub.ai/skills/notebooklm-studio
allowed-tools	Read,Write,Bash

NotebookLM Studio

Import sources into NotebookLM, generate user-selected artifacts via CLI, download results locally.

Inputs

Collect from user message (ask only for missing fields):

• Sources: URLs, YouTube links, text notes, or file attachments (PDF, Word, audio, image, Google Drive link)
• Artifacts: User selects from 9 types (no default — always ask):
  • audio (podcast), video, report, quiz, flashcards, mind-map, slide-deck, infographic, data-table
• Language (optional, default: zh_Hant): applied via notebooklm language set
• Artifact options: format, style, length, difficulty, etc.
• Custom instructions (optional): passed as description to generate commands

Workflow

Steps are sequential gates — do NOT skip or combine steps.

0. Auth precheck — Verify the session is valid:

   notebooklm auth check --test --json
   

1. Parse input & configure artifacts —

   • 1a. Select artifacts
   • 1b. Discuss options (ASK / OFFER / SILENT priority levels)

2. Derive slug — Generate a short kebab-case slug for the notebook name and output directory.

3. Create notebook —

   notebooklm create "<slug> <YYYYMMDD>"
   notebooklm use <notebook_id>
   mkdir -p ./output/<slug>
   

4. Set language — notebooklm language set <confirmed_language>

5. Add sources — For each source: notebooklm source add "<url_or_filepath>"

6. Generate artifacts — Two-tier strategy:

   • Tier 1 (Immediate): mind-map, report, quiz, flashcards, data-table, infographic — use --wait
   • Tier 2 (Deferred): slide-deck, video, audio — use --json, capture task_id for polling

7. Download Tier 1 — Each artifact into ./output/<slug>/

8. Report + Deliver Tier 1 — Present completed artifacts to user

9. Poll + Deliver Tier 2 — Wait for deferred artifacts, download and deliver as each completes

Artifact Types

Type	Tier	Typical Time
mind-map	1	Instant
report	1	1-2 min
quiz	1	1-2 min
flashcards	1	1-2 min
data-table	1	1-2 min
infographic	1	2-5 min
slide-deck	2	5-15 min
video	2	10-30 min
audio (podcast)	2	10-30 min

Requirements

• notebooklm CLI (notebooklm-py)
• ffmpeg (for audio compression)
• playwright (for browser automation)

Error Handling

• Auth errors: Caught by step 0 precheck. Re-login if expired.
• Tier 1 failure: Retry up to 2 times, then include failure note in delivery.
• Tier 2 failure: Notify user per-artifact. Tier 1 already delivered.
• Timeout recovery: Never re-generate on timeout. Re-check status, re-wait if still processing.

Delivery Template

1. Selection rationale (≤3 bullets)
2. Artifact list with paths/status
3. Key takeaways (3-5 bullets)
4. Failures + fallback note (if any)
5. One discussion question