Notebooklm Py vs Vercel Skills
Both are Grade A and 1.8 points apart, so choose on what you weigh most. Notebooklm Py leads on supply-chain integrity and transparency; Vercel Skills leads on maintenance and adoption.
Unofficial Python API and agentic skill for Google Gemini Notebook. Full programmatic access to Gemini Notebook's features—including capabilities the web UI doesn't expose—via Python, CLI, and AI agents like Claude Code, Codex, and OpenClaw.
Choose Notebooklm Py if supply-chain integrity and transparency matter most.
- +0.8Transparency: OSSF Scorecard 5.6 against 4.7
- +0.7Safety / Integrity: OSSF Scorecard 5.6 against 4.7
The open agent skills tool - npx skills
Choose Vercel Skills if maintenance and adoption matter most.
- +3.7Adoption: 5.0M weekly downloads against 27.4k
- +1.6Maintenance: last push today, against 2d ago
Where they differ
1 dimension identical: Identity 18.0 · Full evidence table
An independent, evidence-based trust comparison of Notebooklm Py and Vercel Skills, two Agent Skills projects in the HVTracker registry. Scores come from public, checkable signals — supply-chain provenance, OSSF Scorecard, maintenance, and adoption — not popularity.
Full evidence
| Signal | Notebooklm Pyteng-lin/notebooklm-py | Vercel Skillsvercel-labs/skills |
|---|---|---|
| HVTrust score | 89.0 | 90.8 |
| Evidence grade | A | A |
| Coverage grade | B | B |
| Overall rank | #6 | #2 |
| Rank in Agent Skills | #6 | #2 |
| GitHub stars | 20.2k | 38.8k |
| Last updated | 2d ago | today |
| Build provenance | Yes | Yes |
| OSSF Scorecard | 5.6 / 10 | 4.7 / 10 |
| License | MIT | MIT |
| Downloads | 27k/wk | 5.0M/wk |
| Trust dimensions (points earned) | ||
| Safety / integrity / 25 | 19.1 | 18.4 |
| Identity & provenance / 18 | 18.0 | 18.0 |
| Transparency / 17 | 13.3 | 12.5 |
| Maintenance / 20 | 18.1 | 19.7 |
| Adoption / 20 | 16.2 | 19.9 |
| Runtime capability surface (full matrix) | ||
| MCP server | Implemented | — |
| External providers | — | — |
| Requires API keys | No | No |
| Plugin surface | — | — |
| Provenance drift | Match | Match |
How to read this: HVTrust (0–100) weighs supply-chain signals (provenance, OSSF Scorecard, signed commits, open license) alongside real-world adoption, scaled by an evidence-confidence factor. Grade bands: A ≥ 80, B ≥ 65, C ≥ 50, D < 50. Signals refresh daily. Full methodology v4.4 →