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

1. Load sources from terminal. You can't just tell NotebookLM to add a YouTube channel. This skill does it. One command. 300 episodes.
2. Cited answers traced to exact transcript lines. Every recommendation links back to the exact episode and passage. Verifiable.
3. Expert-informed interviews. Claude queries NotebookLM with YOUR goal. Generates questions informed by the expert's research on your specific topic.
4. Experiments in Obsidian. Protocol becomes experiments in your daily note. Morning routine skill asks every day: how is this going?
5. 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-cli

Gives you the

nlm

2. Install notebooklm-py (for notebook creation and channel loading)

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

3. Authenticate

# nlm CLI auth (for queries and source listing)
nlm auth login

# notebooklm-py auth (for notebook creation and loading)
notebooklm login

Both open a browser window for Google login.

nlm

notebooklm-py

~/.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> --json

Workflow Routing

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 20

3. Ask expert-informed questions

nlm notebook query <notebook-id> \
  "What does Huberman recommend for sustaining deep focus for 4+ hours daily?" --json

Each answer comes with

[N]

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

/lenny

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 experiments

Scripts

scripts/load_channel.py

scripts/resolve_citations.py

[N]

[[Source#^anchor|[N]]]

scripts/import_sources.py

scripts/extract_passages.py

scripts/backfill_fulltext.py

All scripts use

Path.cwd()

Citation Resolution

The resolver turns

[N]

[[Source#^c-XXXXXXXX|[N]]]

• 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