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 teng-lin · Claude Skill · ★ 19.4k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is notebooklm-py safe to install? View the security audit →
notebooklm-py Unofficial Python API and agentic skill for Google NotebookLM. Full programmatic access to NotebookLM's features—including capabilities the web UI doesn't expose—via Python, CLI, and AI agents like Claude Code, Codex, and OpenClaw. Source & Development: ⚠️ Unofficial Library - Use at Your Own Risk This library uses undocumented Google APIs that can change without notice. - Not affiliated with Google - This is a community project - APIs may break - Google c
| Stars | 19,403 |
| Forks | 2,602 |
| Language | Python |
| Category | Claude Skill |
| License | MIT |
| Quality Score | 74.4698856231168/100 |
| Open Issues | 38 |
| Last Updated | 2026-09-20 |
| Created | 2026-01-07 |
| Platforms | browser, claude-code, cli, codex, gemini, python |
| Est. Tokens | ~15k |
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notebooklm-py is Unofficial Python API and agentic skill for Google Gemini Notebook. Full programmatic access to NotebookLM's features—including capabilities the web UI doesn't expose—via Python, CLI, and AI agents li. It is categorized as a Claude Skill with 19.4k GitHub stars.
notebooklm-py is primarily written in Python. It covers topics such as agentic-skill, claude-skills, gemini-notebook.
You can find installation instructions and usage details in the notebooklm-py GitHub repository at github.com/teng-lin/notebooklm-py. The project has 19.4k stars and 2602 forks, indicating an active community.
notebooklm-py is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to notebooklm-py on Agent Skills Hub include memU, awesome-agent-skills, baoyu-skills. 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: