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 AZIO-126/azio-vault-os --skill notebooklm-notebookgit clone --depth 1 https://github.com/AZIO-126/azio-vault-osWrote 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/azio-126/azio-vault-os/notebooklm-notebook)<a href="https://agentmods.dev/skills/azio-126/azio-vault-os/notebooklm-notebook"><img src="https://agentmods.dev/badge/skills/azio-126/azio-vault-os/notebooklm-notebook/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/azio-126/azio-vault-os/notebooklm-notebook"><img src="https://agentmods.dev/badge/skills/azio-126/azio-vault-os/notebooklm-notebook.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 | $0.00091 | $0.02067 |
| Opus 5 | $0.00046 | $0.01033 |
| Sonnet 5 | $0.00018 | $0.00413 |
| Haiku 4.5 | $0.00009 | $0.00207 |
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 — 283 lines — stays where its author put it; the contents beside it link to each section on GitHub.
NotebookLM Skill
Full CLI control of Google NotebookLM — research, synthesis, and content generation from your vault.
Command path: Use
notebooklm(Python 3.13, fully working).
Setup & Auth
# Login (opens browser, press ENTER when done)
notebooklm login
# Check auth status
notebooklm auth check
notebooklm auth check --test # full network validation
# See current context (active notebook, paths)
notebooklm status
notebooklm status --paths
Notebook Management
notebooklm list # List all notebooks
notebooklm create "Topic Name" # Create new notebook
notebooklm use <id> # Select notebook (partial ID works)
notebooklm rename "New Title" # Rename current notebook
notebooklm summary # Get AI summary
notebooklm metadata # Get notebook metadata
notebooklm delete <id> # Delete notebook
notebooklm clear # Clear current context
Sources
# Add any source (URL, file path, or text)
notebooklm source add "https://example.com/article"
notebooklm source add "./paper.pdf"
notebooklm source add "https://www.youtube.com/watch?v=..."
# AI-powered research discovery
notebooklm source add-research "your topic" --mode fast # quick web search
notebooklm source add-research "your topic" --mode deep # thorough research
notebooklm source add-research "topic" --mode deep --no-wait # non-blocking
# Manage sources
notebooklm source list
notebooklm source get <id>
notebooklm source fulltext <id> # get full indexed text
notebooklm source guide <id> # get source summary
notebooklm source rename <id> "Title"
notebooklm source refresh <id>
notebooklm source delete <id>
notebooklm source delete-by-title "Title"
notebooklm source wait <id> # wait for processing
Chat / Q&A
notebooklm ask "What are the key themes?"
notebooklm ask "Summarize the main arguments" --save-as-note
notebooklm ask "question" --save-as-note --note-title "My Summary"
notebooklm history # view chat history
notebooklm history --save # save chat to note
notebooklm history --clear
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 · 283 lines · 91 tokens per session scan A b347a021d25a
notebooklm is a skill published in the GitHub repository AZIO-126/azio-vault-os (2 stars, last pushed 5mo ago), licensed MIT. It adds 91 tokens to every session and 2,067 once invoked, about $0.0005 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 2026-08-31.
Other skills, from other repositories
VaultForge
Process learning materials into structured Obsidian atomic notes with roadmaps, MOCs, and double-links. Auto-generate study vaults from PDFs, Markdown, Word, PowerPoint, and more with AI-powered note-taking, knowledge linking, and deep research. Supports English and Chinese output. Use when the user wants to build a…
cognitive-compile
Use when source material must be transformed into a compact, evidence-aware model for learning, decisions, or an Obsidian concept note.
notebooklm
Drive NotebookLM over a programmatic CLI with no browser: turn a vault slice into an audio overview, study guide or mind map, and fold the artifacts back into the vault. Browser automation is the fallback if CLI auth breaks. Triggers: "/notebooklm", "make an audio digest of ", "podcast from the vault".
Research for Datacore
Automated research — source processing, podcast generation, and knowledge extraction.
wiki-ingest
Ingest supplied source material into an Obsidian vault with provenance and claim tracking: pasted text, files staged in the selected vault's inbox or .raw archive, or explicitly approved URLs. Use for a single source or bounded batch, not for saving an assistant answer. Triggers: ingest, ingest this file, ingest this…
autoresearch
Run a bounded, source-grounded research loop, draft a cited dossier, and optionally propose a separately reviewed canonical vault merge. Use when the user wants autonomous or deep research that may access the public web. Triggers: /autoresearch, autoresearch, research this topic, deep dive into, investigate, find…