NotebookLM - Expert Knowledge to Action
Turn any expert's content into a personalized protocol with experiments you actually run. Load 300 YouTube episodes into NotebookLM from terminal, run a cited interview about your goal, create experiments in your Obsidian daily note.
Video walkthrough: https://youtu.be/KRpZSvtMiTI
What This Does
- Load sources from terminal. You can't just tell NotebookLM to add a YouTube channel. This skill does it. One command. 300 episodes.
- Cited answers traced to exact transcript lines. Every recommendation links back to the exact episode and passage. Verifiable.
- Expert-informed interviews. Claude queries NotebookLM with YOUR goal. Generates questions informed by the expert's research on your specific topic.
- Experiments in Obsidian. Protocol becomes experiments in your daily note. Morning routine skill asks every day: how is this going?
- Any expert, any domain. Huberman for health. Lenny for product. Onboarding docs for a new job. Same pattern.
Prerequisites
1. Install nlm CLI
uv tool install notebooklm-mcp-cliGives you the nlm command. See notebooklm-mcp-cli for details.
2. Install notebooklm-py (for notebook creation and channel loading)
pip install "notebooklm-py[browser]"
playwright install chromium3. Authenticate
# nlm CLI auth (for queries and source listing)
nlm auth login
# notebooklm-py auth (for notebook creation and loading)
notebooklm loginBoth open a browser window for Google login. nlm saves to its own config, notebooklm-py saves cookies to ~/.notebooklm/storage_state.json.
4. Obsidian Plugins
- Dataview (required) - for dashboard queries and citation tables
Quick Start
# List your notebooks
nlm notebook list
# Ask a question with citations
nlm notebook query <notebook-id> "What does Huberman say about deep focus?" --json
# List sources
nlm source list <notebook-id> --jsonWorkflow Routing
| User says | Workflow |
|---|---|
| "load channel", "youtube channel", "bulk load videos" | workflows/youtube-channel.md |
| "notebooklm ask", "ask notebook", "Q&A" | workflows/ask.md |
| "import notebook", "import sources" | workflows/import.md |
| "notebooklm auth", "notebooklm login" | workflows/auth.md |
The Full Pipeline
This is the workflow shown in the video:
1. Pick your expert and goal
Goal: "I want to improve my health and focus"
Expert: Andrew Huberman (@hubaborhab on YouTube)2. Load their content
# Scrape channel videos
python3 scripts/load_channel.py scrape \
--channel "https://www.youtube.com/@hubaborhab" \
--output /tmp/huberman-videos.json
# Create notebook
notebooklm create "Andrew Huberman - Health"
# Load 200 most recent health-related episodes
notebooklm use <notebook-id>
python3 scripts/load_channel.py load \
--videos /tmp/huberman-videos.json \
--notebook <notebook-id> \
--count 200 \
--concurrency 203. Ask expert-informed questions
nlm notebook query <notebook-id> \
"What does Huberman recommend for sustaining deep focus for 4+ hours daily?" --jsonEach answer comes with [N] citations back to the exact source and passage.
4. Run a cited interview
Claude uses the notebook to generate interview questions specific to YOUR goal. You answer honestly. Claude builds a personalized protocol where each recommendation is tied to an exact episode.
5. Create experiments
The protocol becomes experiments in your Obsidian vault:
- Each experiment has a hypothesis, protocol, success criteria, and timeframe
- They appear in your daily note every morning
- Your morning routine skill asks: "How is this experiment going? Any observations?"
6. Turn it into a reusable skill
Package the workflow as a /huberman or /lenny skill. Same pattern, different expert.
Vault Structure
Your Vault/
├── Notes/NotebookLM/
│ ├── Huberman Health.md # type: notebook (index)
│ └── huberman-health/
│ ├── Sources/ # type: notebook-source (transcripts)
│ │ └── Episode Title.md
│ └── QA/ # type: nlm-query (cited answers)
│ └── 2026-04-05 Focus Protocol.md
├── Notes/Experiments/
│ └── Morning Sunlight Protocol.md # type: experiment
└── Notes/Dashboards/
└── Health.md # Dashboard with embedded experimentsScripts
| Script | Purpose | |
|---|---|---|
scripts/load_channel.py | Scrape YouTube channel + bulk-load into NotebookLM | |
scripts/resolve_citations.py | Replace [N] with `[[Source#^anchor\ | [N]]]` wikilinks |
scripts/import_sources.py | Import sources as vault files with metadata | |
scripts/extract_passages.py | Extract cited passages from Q&A into source files | |
scripts/backfill_fulltext.py | Fetch full transcripts for source files |
All scripts use Path.cwd() as vault root. Run them from your vault directory.
Citation Resolution
The resolver turns [N] markers in NotebookLM answers into clickable [[Source#^c-XXXXXXXX|[N]]] wikilinks. Click to jump to the exact cited passage in the source transcript.
- Anchor IDs are stable (MD5 of cited text)
- Idempotent: re-running same question skips existing anchors
- Cross-source citation remap: handles collapsed source_ids
- ~96% resolution rate across tested queries
Examples
- Health: 300 Huberman episodes -> personalized health protocol with sleep, supplements, exercise experiments
- Product: 200 Lenny's Podcast episodes -> product strategy playbook with cited frameworks
- New job: Onboarding docs + team wikis + architecture decisions -> ramp-up plan with daily experiments
- Business: Hormozi content -> offer audit with value equation scoring
License
MIT