Load any expert's entire YouTube catalog into a searchable knowledge base. Query it with citations traced to exact transcript passages.
395 Huberman Lab episodes. One command to load. Every answer cited to the exact episode and passage.
But this is not a Huberman tool. It is a general-purpose system for turning any expert's content into a queryable, cited knowledge base — from the terminal.
YouTube Channel NotebookLM Your Terminal
+-----------------+ +----------------------+ +------------------------+
| 300+ videos | --> | Indexed & searchable | --> | Cited answers |
| Any expert | | Google's AI handles | | Traced to exact |
| Any domain | | transcript parsing | | transcript passages |
+-----------------+ +----------------------+ +------------------------+
SCRAPE LOAD QUERY
(no API key) (parallel upload) (structured citations)
Three capabilities:
Bulk load -- Scrape an entire YouTube channel and load 300+ videos into NotebookLM. One command. No API keys. No manual entry.
Cited answers -- Ask any question. Every recommendation traces to the exact episode and transcript passage. Verifiable, not hallucinated.
Deep expert plans -- Generate comprehensive protocols, playbooks, and guides synthesized across hundreds of episodes. See the example below.
# NotebookLM CLI (for queries)
uv tool install notebooklm-mcp-cli
# NotebookLM Python client (for bulk loading)
pip install "notebooklm-py[browser]"
playwright install chromium
# Authenticate (opens browser for Google login)
nlm auth login
notebooklm login# Scrape all videos from a channel (no API key needed)
python3 .claude/skills/notebooklm/scripts/load_channel.py scrape \
--channel "https://www.youtube.com/@hubermanlab" \
--output experts/huberman/videos.json
# Create a NotebookLM notebook
notebooklm create "Andrew Huberman - Health"# Load 300 videos in parallel (~75 seconds)
python3 .claude/skills/notebooklm/scripts/load_channel.py load \
--videos experts/huberman/videos.json \
--notebook <notebook-id> \
--count 300 \
--concurrency 20nlm notebook query <notebook-id> \
"What does Huberman recommend for sustaining deep focus for 4+ hours?" --jsonEvery answer comes back with [N] citations pointing to the exact source and passage.
This is a real output from querying 395 Huberman Lab episodes loaded into NotebookLM. The system synthesized a structured 30-day plan with phase-by-phase protocols, supplement stacks, exercise programming, and emergency craving tools -- all cited to specific episodes.
Phase 1 (Days 1-10): Withdrawal & Foundation
Execute 6 daily anchors: morning sunlight (5-30 min within 60 min of waking), cold exposure (1-3 min, gives sustained 250% dopamine increase for hours), exercise (<75 min), NSDR/Yoga Nidra (restores dopamine reserves by up to 65%), sleep protocol (lights out by 10-11 PM), no caffeine before 90 min after waking.
Phase 2 (Days 11-20): Rewiring
Layer on gratitude protocol, journaling (15-20 min continuous writing about stressful experiences -- proven to improve immune function), procedural memory visualization. Introduce focus supplements intermittently: L-Tyrosine 500mg, Alpha-GPC 300mg, every 3rd or 4th session only.
Phase 3 (Days 21-30): Consolidation
Intermittent reward training -- randomly skip caffeine, work out without music, attach dopamine to effort not outcome. Meditation 5-13 min daily to train prefrontal cortex. Careful re-introduction of one eliminated behavior on Day 28.
Full plan: experts/huberman/30-day-dopamine-reset-plan.md
That file includes daily schedules, supplement dosing tables, weekly exercise splits, emergency craving protocols, and weekly check-in templates -- all derived from the expert's own research-backed recommendations across 395 episodes.
The same three commands work for any YouTube channel.
python3 .claude/skills/notebooklm/scripts/load_channel.py scrape \
--channel "https://www.youtube.com/@LennysPodcast" \
--output experts/lenny/videos.json
notebooklm create "Lenny's Podcast - Product"
python3 .claude/skills/notebooklm/scripts/load_channel.py load \
--videos experts/lenny/videos.json \
--notebook <notebook-id> \
--count 200 --concurrency 20Then query: "What are the most effective user onboarding frameworks discussed across all episodes?"
| Expert | Channel | Domain | Use Case |
|---|---|---|---|
| Alex Hormozi | @AlexHormozi | Business | Offer creation, pricing, lead generation |
| Lex Fridman | @lexfridman | AI / Science | Research landscape, expert perspectives |
| Ali Abdaal | @aliabdaal | Productivity | Systems, tools, time management |
| My First Million | @MyFirstMillionPod | Startups | Business ideas, market analysis |
| Naval Ravikant | @naval | Wealth / Philosophy | Mental models, decision frameworks |
| Y Combinator | @ycombinator | Startups | Fundraising, product-market fit |
Any channel with substantial content becomes a queryable expert knowledge base.
+-------------------+
| YouTube |
| InnerTube API |
+--------+----------+
|
scrape (no API key,
pure Python stdlib)
|
v
+-------------------+
| videos.json |
| [{id, title, |
| url, ...}] |
+--------+----------+
|
load (async, 20
concurrent requests)
|
v
+-------------------+
| Google |
| NotebookLM |
| |
| Indexes videos, |
| parses trans- |
| cripts, builds |
| knowledge graph |
+--------+----------+
|
query via nlm CLI
(cited responses)
|
v
+-------------------+
| Cited Answers |
| [1] -> Episode |
| [2] -> Passage |
| [3] -> Timestamp |
+-------------------+
YouTube scraping uses the InnerTube browse API directly. No
yt-dlp, no API keys, no external dependencies. Pure Python stdlib with continuation token pagination.Parallel loading via
notebooklm-pyasync client. 20 concurrent requests loads 200 videos in ~75 seconds. For channels with 300+ videos, the system automatically splits across multiple notebooks (NotebookLM has a 300-source limit per notebook).Citation resolution replaces
[N]markers with clickable wikilinks that jump to the exact cited passage in source transcripts. Anchor IDs are MD5-hashed for stability. ~96% resolution rate.Claude Code integration via the
.claude/skills/notebooklm/skill. Workflows handle the full pipeline: scrape, load, query, resolve citations.
HealthwithHubber/
.claude/
skills/
notebooklm/
SKILL.md # Skill definition
scripts/
load_channel.py # Scrape YouTube + bulk-load into NotebookLM
resolve_citations.py # Replace [N] with linked wikilinks
import_sources.py # Import sources as local files
extract_passages.py # Extract cited passages from Q&A
backfill_fulltext.py # Fetch full transcripts
workflows/
youtube-channel.md # Channel loading workflow
ask.md # Q&A with citations workflow
import.md # Source import workflow
auth.md # Authentication workflow
experts/
experts.json # Registry of all loaded experts
huberman/
config.json # Expert metadata + notebook IDs
videos.json # 395 scraped videos
30-day-dopamine-reset-plan.md # Example deep plan output
queries/ # Saved Q&A outputs
CLAUDE.md # Project documentation
README.md # This file
| Requirement | Install | Purpose |
|---|---|---|
| Python 3.10+ | -- | Script runtime |
nlm CLI | uv tool install notebooklm-mcp-cli | Query notebooks, list sources |
notebooklm-py | pip install "notebooklm-py[browser]" | Create notebooks, bulk-load videos |
| Playwright | playwright install chromium | Browser auth for notebooklm-py |
| Google account | -- | NotebookLM access (free) |
No YouTube API key required. Scraping uses YouTube's InnerTube API directly.
No paid APIs for loading. NotebookLM is free. The nlm CLI and notebooklm-py are open-source tools that interface with it.
- 300 sources per notebook. Channels with more videos are automatically split across multiple notebooks.
- Processing time. After upload, NotebookLM indexes each video server-side. Allow a few minutes before querying.
- Some videos may fail if they are private, deleted, or region-locked. Errors are logged to
/tmp/channel-load-errors.json.
MIT