Borrowing it
Nothing to install: this file belongs to claude-world/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/claude-world/notebooklm-skill/main/AGENTS.mdgit clone --depth 1 https://github.com/claude-world/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/claude-world/notebooklm-skill/agents-md)<a href="https://agentmods.dev/instructions/claude-world/notebooklm-skill/agents-md"><img src="https://agentmods.dev/badge/instructions/claude-world/notebooklm-skill/agents-md/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/instructions/claude-world/notebooklm-skill/agents-md"><img src="https://agentmods.dev/badge/instructions/claude-world/notebooklm-skill/agents-md.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 | $0.00901 | $0.00901 |
| Opus 5.5 | $0.00360 | $0.00360 |
| Sonnet 5.5 | $0.00180 | $0.00180 |
| Haiku 4.5 | $0.00090 | $0.00090 |
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 3d 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 — 106 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 0.8.x and FastMCP 4 (MCP Python SDK 2.x) — pure async Python 3.10+.
Authentication
NotebookLM uses browser-based Google login (no API keys needed):
notebooklm-auth setup # One-time browser auth
notebooklm-auth verify # Read-only session verification
Sessions are profile-aware and stored under ~/.notebooklm/profiles/ by default.
CLI Commands
Five global commands are available after installation:
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 --type audio --notebook "Research" --lang en
notebooklm-skill download --type audio --notebook "Research" --output podcast.m4a
notebooklm-skill delete --notebook "Research" --yes
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
Authentication and Skill install
notebooklm-auth --profile work setup
notebooklm-install-skill --scope project
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.
- 3d ago Changed · +5 lines · +78 tokens per session 2bad8ffe1e31
- 1mo ago First seen · 101 lines · 823 tokens per session scan A 8427cb952fd4
notebooklm-skill AGENTS.md is an instructions file published in the GitHub repository claude-world/notebooklm-skill (481 stars, last pushed 4d ago), licensed MIT. It adds 901 tokens to every session, about $0.0036 per session on Opus 5.5. 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.
Other instructions, from other repositories
vscode buildNext.instructions.md
Working notes and architecture documentation for the new esbuild-based build system in build/next. Use when making changes to the new build pipeline (transpile/bundle commands, NLS plugin, source-map handling, resource copying, or self-hosting watch tasks).
next.js AGENTS.md
AGENTS.md instructions for vercel/next.js, covering next.js development guide, codebase structure, monorepo overview, core package: packages/next and other important packages.
vscode oss-third-party-notices.instructions.md
Instructions for microsoft/vscode, covering vs code oss third-party-notices pipeline, architecture, pipeline flow in ci, cache eligibility and rollout and applying the notice (cutover).
langchain AGENTS.md
AGENTS.md instructions for langchain-ai/langchain, covering global development guidelines for the langchain monorepo, corridor security analysis, project architecture and context, monorepo structure and development tools & commands.
vscode copilot-instructions.md
Copilot instructions for microsoft/vscode, covering vs code copilot instructions, project overview, root folders, core architecture (src/ folder) and built-in extensions (extensions/ folder).
deepseek-harness AGENTS.md
AGENTS.md instructions for deepseek-ai/deepseek-harness, covering agents.md, pre-stable apis and released session data, repository layout, commands and host sandbox failures.