/notebooklm-import

name: notebooklm-import
description: Import NotebookLM notebooks into your Obsidian vault as linked knowledge graphs. Sources become wikilink-able files, Q&A answers get citations resolved to [[wikilinks]] with passage-level deep links. Use when user says "notebooklm import", "import notebook", "notebooklm sources", or wants to import NotebookLM data into vault files.

NotebookLM - Vault Knowledge Graph

Turn NotebookLM notebooks into persistent, linked vault knowledge. Sources become files you can `[[wikilink]]`, Q&A answers get `[N]` citations resolved to those wikilinks, dashboards tie it all together.

Prerequisites

1. Install notebooklm-py

pip install "notebooklm-py[browser]"
playwright install chromium

2. Authenticate

notebooklm login

Opens a browser window - log in with your Google account. Cookies are saved to `~/.notebooklm/storage_state.json`.

Re-run `notebooklm login` when cookies expire (you'll see auth errors).

3. Obsidian Plugins

• **Dataview** (required) - citation tables and dashboard queries use Dataview/DataviewJS

4. Vault Setup

Copy the templates from `templates/` into your vault:

• `templates/notebook-source.md` -> `Templates/Types/notebook-source.md`
• `templates/dashboard.md` -> use as reference when creating dashboards

Quick Start

# Check current notebook
notebooklm status

# Switch notebook
notebooklm use <notebook-id>

# List notebooks
notebooklm list

Workflow Routing

| User says | Workflow | |-----------|----------| | "import notebook", "notebooklm import", "import sources" | [workflows/import.md](workflows/import.md) | | "notebooklm ask", "ask notebook", "Q&A" | [workflows/ask.md](workflows/ask.md) |

Architecture

Your Vault
├── Notes/NotebookLM/{notebook-slug}/
│   ├── Sources/          # One .md per source (YouTube, PDF, web, etc.)
│   │   ├── Video Title.md
│   │   └── Article Title.md
│   └── QA/               # Q&A notes with resolved citations
│       └── 2026-02-23 Emerging Themes.md
└── Notes/Dashboards/
    └── My Research.md     # Dashboard with Dataview queries

• **Source files** have type `notebook-source` with frontmatter: `source_id`, `notebook_id`, `url`, `source_type`, topics as `[[wikilinks]]`
• **Q&A notes** have type `reference` with `[N]` citations resolved to `[[Source Title#Passage N]]` wikilinks
• **Dashboards** use Dataview queries to list sources and Q&A notes

Data Model

**notebook-source frontmatter:**

type: notebook-source
source_id: "uuid"        # NotebookLM source ID
notebook_id: "uuid"      # NotebookLM notebook ID
url: ""                  # Original URL
source_type: youtube     # youtube, pdf, web, text, gdocs, gslides
status: active
date: YYYY-MM-DD
topics:
  - "[[Topic Name]]"     # AI-extracted topics as wikilinks
related:
  - "[[Notes/Dashboards/Dashboard Name]]"

**Q&A reference frontmatter:**

type: reference
status: current
date: YYYY-MM-DD
source: "notebooklm:{notebook-slug}"
related:
  - "[[Notes/Dashboards/Dashboard Name]]"

Scripts

| Script | Purpose | |--------|---------| | `scripts/import_sources.py` | Import sources as vault files with AI-generated guides and topics | | `scripts/extract_passages.py` | Extract cited passages from Q&A into source files | | `scripts/resolve_citations.py` | Replace `[N]` markers with `[[wikilinks]]` in Q&A answers |

All scripts use `Path.cwd()` as vault root - run them from your vault directory.