Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add ArtemXTech/personal-os-skills --skill notebooklmgit clone --depth 1 https://github.com/ArtemXTech/personal-os-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/artemxtech/personal-os-skills/notebooklm)<a href="https://agentmods.dev/skills/artemxtech/personal-os-skills/notebooklm"><img src="https://agentmods.dev/badge/skills/artemxtech/personal-os-skills/notebooklm/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/artemxtech/personal-os-skills/notebooklm"><img src="https://agentmods.dev/badge/skills/artemxtech/personal-os-skills/notebooklm.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00074 | $0.01571 |
| Opus 5 | $0.00037 | $0.00785 |
| Sonnet 5 | $0.00015 | $0.00314 |
| Haiku 4.5 | $0.00007 | $0.00157 |
Grade A, and why
notebooklm scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 178 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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-cli
Gives 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 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 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> --json
What ships with it
9 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- scripts/backfill_fulltext.py 3.1 KB runs code
- scripts/extract_passages.py 4.8 KB runs code
- scripts/import_sources.py 4.3 KB runs code
- scripts/load_channel.py 8.5 KB runs code
- scripts/resolve_citations.py 16 KB runs code
- workflows/ask.md 2.9 KB
- workflows/auth.md 1.2 KB
- workflows/import.md 3.0 KB
- workflows/youtube-channel.md 4.7 KB
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 178 lines · 74 tokens per session scan A f9f3704f12fe
notebooklm is a skill published in the GitHub repository ArtemXTech/personal-os-skills (532 stars, last pushed 5mo ago), licensed MIT. It adds 74 tokens to every session and 1,571 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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