No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by proyecto26 · Agent Tool · ★ 63
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is notebooklm-ai-plugin safe to install? View the security audit →
NotebookLM AI Plugin Bring the full Google NotebookLM experience to Claude Code. Chat with your notebook AI, generate 9 types of artifacts, manage sources (URLs, YouTube, files), run fast/deep web research, and manage notes — just ask Claude. Quick Start Installation Option 1: CLI Install (Recommended) Use npx skills to install skills directly: This automatically installs to your directory. Option 2: Claude Code Plugin Install via Claude Code's built-in plugin system: Option 3: Clone and Copy Clone the repo and copy the skills folder: Option 4: Git Submodule Add as a
| Stars | 63 |
| Forks | 16 |
| Language | TypeScript |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 75.6713869344236/100 |
| Last Updated | 2026-04-06 |
| Created | 2026-03-17 |
| Platforms | browser, claude-code, cli, node |
| Est. Tokens | ~12k |
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notebooklm-ai-plugin is AI Agent plugin for Google NotebookLM (Claude, OpenClaw, etc) — generate slide decks, audio overviews, videos, mind maps, flashcards, quizzes, infographics, reports, and data tables from your notebook. It is categorized as a Agent Tool with 63 GitHub stars.
notebooklm-ai-plugin is primarily written in TypeScript. It covers topics such as ai, audio-overview, automation.
You can find installation instructions and usage details in the notebooklm-ai-plugin GitHub repository at github.com/proyecto26/notebooklm-ai-plugin. The project has 63 stars and 16 forks, indicating an active community.
notebooklm-ai-plugin is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to notebooklm-ai-plugin on Agent Skills Hub include sudocode, frago, claude-emporium. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: