Borrowing it
Nothing to install: this file belongs to jakubs2623/notebooklm-skill. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/jakubs2623/notebooklm-skill/main/AGENTS.mdgit clone --depth 1 https://github.com/jakubs2623/notebooklm-skillWrote 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/instructions/jakubs2623/notebooklm-skill/agents-md)<a href="https://agentmods.dev/instructions/jakubs2623/notebooklm-skill/agents-md"><img src="https://agentmods.dev/badge/instructions/jakubs2623/notebooklm-skill/agents-md.svg" alt="Measured on agentmods" 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 | $0.00805 | $0.00805 |
| Opus 5 | $0.00402 | $0.00402 |
| Sonnet 5 | $0.00161 | $0.00161 |
| Haiku 4.5 | $0.00081 | $0.00081 |
Grade A, and why
notebooklm-skill AGENTS.md 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 6d 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 — 95 lines — stays where its author put it; the contents beside it link to each section on GitHub.
notebooklm-skill
NotebookLM research automation — CLI, MCP server, and Claude Code Skill.
Overview
This project bridges Google NotebookLM's research capabilities with AI content generation. Feed it URLs, PDFs, or trending topics — it creates NotebookLM notebooks, runs deep research, and produces structured output: articles, social posts, podcasts, videos, slides, and more.
Built on notebooklm-py v0.3.4 — pure async Python.
Authentication
NotebookLM uses browser-based Google login (no API keys needed):
python3 -m notebooklm login # One-time browser auth
python scripts/auth_helper.py verify # Verify session
Session stored at ~/.notebooklm/storage_state.json. Lasts weeks.
CLI Commands
Three global commands are available after pip install .:
notebooklm-skill — Core Operations
notebooklm-skill create --title "Research" --sources https://example.com
notebooklm-skill list
notebooklm-skill ask --notebook "Research" --query "Key findings?"
notebooklm-skill generate audio --notebook "Research" --language en
notebooklm-skill download audio --notebook "Research" --output podcast.m4a
notebooklm-skill delete --notebook "Research"
notebooklm-pipeline — Workflow Orchestration
notebooklm-pipeline research-to-article --sources url1 url2 --title "Topic"
notebooklm-pipeline research-to-social --sources url1 --platform threads
notebooklm-pipeline trend-to-content --geo TW --count 5 --platform threads
notebooklm-pipeline batch-digest --rss https://example.com/feed.xml
notebooklm-pipeline generate-all --sources url1 --title "Research" --output-dir ./output
notebooklm-mcp — MCP Server
notebooklm-mcp # stdio mode (Claude Code, Cursor)
notebooklm-mcp --http # HTTP mode on port 8765
MCP Tools (13)
| Tool | Description |
|---|---|
nlm_create_notebook |
Create notebook with sources |
nlm_list |
List all notebooks |
nlm_delete |
Delete a notebook |
nlm_add_source |
Add source to existing notebook |
nlm_ask |
Ask question (returns answer + citations) |
nlm_summarize |
Get notebook summary |
nlm_generate |
Generate artifact (9 types, infographic excluded) |
nlm_download |
Download generated artifact |
nlm_list_sources |
List sources in notebook |
nlm_list_artifacts |
List generated artifacts |
nlm_research |
Deep web research |
nlm_research_pipeline |
Full research pipeline |
nlm_trend_research |
Trend-to-research pipeline |
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.
- 6d ago First seen · 95 lines · 805 tokens per session scan A fa99db0730f2
notebooklm-skill AGENTS.md is an instructions file published in the GitHub repository jakubs2623/notebooklm-skill (7 stars, last pushed yesterday), licensed MIT. It adds 805 tokens to every session, about $0.0040 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.
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