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 ZhanlinCui/Agent-Skills-Hunter --skill notebooklmgit clone --depth 1 https://github.com/ZhanlinCui/Agent-Skills-HunterWrote 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/zhanlincui/agent-skills-hunter/notebooklm)<a href="https://agentmods.dev/skills/zhanlincui/agent-skills-hunter/notebooklm"><img src="https://agentmods.dev/badge/skills/zhanlincui/agent-skills-hunter/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/zhanlincui/agent-skills-hunter/notebooklm"><img src="https://agentmods.dev/badge/skills/zhanlincui/agent-skills-hunter/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 | $0.00049 | $0.02112 |
| Opus 5.5 | $0.00020 | $0.00845 |
| Sonnet 5.5 | $0.00010 | $0.00422 |
| Haiku 4.5 | $0.00005 | $0.00211 |
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 2mo 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.
This is a copy
100% identical to notebooklm — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 270 lines — stays where its author put it; the contents beside it link to each section on GitHub.
NotebookLM Research Assistant Skill
Interact with Google NotebookLM to query documentation with Gemini's source-grounded answers. Each question opens a fresh browser session, retrieves the answer exclusively from your uploaded documents, and closes.
When to Use This Skill
Trigger when user:
- Mentions NotebookLM explicitly
- Shares NotebookLM URL (
https://notebooklm.google.com/notebook/...) - Asks to query their notebooks/documentation
- Wants to add documentation to NotebookLM library
- Uses phrases like "ask my NotebookLM", "check my docs", "query my notebook"
⚠️ CRITICAL: Add Command - Smart Discovery
When user wants to add a notebook without providing details:
SMART ADD (Recommended): Query the notebook first to discover its content:
# Step 1: Query the notebook about its content
python scripts/run.py ask_question.py --question "What is the content of this notebook? What topics are covered? Provide a complete overview briefly and concisely" --notebook-url "[URL]"
# Step 2: Use the discovered information to add it
python scripts/run.py notebook_manager.py add --url "[URL]" --name "[Based on content]" --description "[Based on content]" --topics "[Based on content]"
MANUAL ADD: If user provides all details:
--url- The NotebookLM URL--name- A descriptive name--description- What the notebook contains (REQUIRED!)--topics- Comma-separated topics (REQUIRED!)
NEVER guess or use generic descriptions! If details missing, use Smart Add to discover them.
Critical: Always Use run.py Wrapper
NEVER call scripts directly. ALWAYS use python scripts/run.py [script]:
# ✅ CORRECT - Always use run.py:
python scripts/run.py auth_manager.py status
python scripts/run.py notebook_manager.py list
python scripts/run.py ask_question.py --question "..."
# ❌ WRONG - Never call directly:
python scripts/auth_manager.py status # Fails without venv!
The run.py wrapper automatically:
- Creates
.venvif needed - Installs all dependencies
- Activates environment
- Executes script properly
What ships with it
14 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.
- references/api_reference.md 7.5 KB
- references/troubleshooting.md 8.8 KB
- references/usage_patterns.md 9.3 KB
- requirements.txt 325 B
- scripts/__init__.py 2.6 KB runs code
- scripts/ask_question.py 8.0 KB runs code
- scripts/auth_manager.py 11 KB runs code
- scripts/browser_session.py 8.5 KB runs code
- scripts/browser_utils.py 3.4 KB runs code
- scripts/cleanup_manager.py 9.5 KB runs code
- scripts/config.py 1.2 KB runs code
- scripts/notebook_manager.py 14 KB runs code
- scripts/run.py 2.9 KB runs code
- scripts/setup_environment.py 7.0 KB runs code
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.
- 2mo ago First seen · 270 lines · 49 tokens per session scan A 8fcfe1aa0452
notebooklm is a skill published in the GitHub repository ZhanlinCui/Agent-Skills-Hunter (193 stars, last pushed 7mo ago), licensed MIT. It adds 49 tokens to every session and 2,112 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to notebooklm, differing in 0 lines, and is treated as a copy.
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