Scan timing: clone 4.19s · analysis 8.43s · 43.6 MB · GitHub preflight 481ms
https://github.com/teng-lin/notebooklm-py.git
· scanned 2026-05-21 12:05 UTC (2 weeks ago)
· 10 languages
675 findings (165 legacy + 510 scanner) 11/13 scanners ran 96th percentile · Python · large (100-500K LoC) Scanner says 88 (higher by 5)
Last scanned 2 weeks ago · v2 · 420 findings from 2 sources. Findings combine the legacy security pipeline AND the multi-layer engine (atlas, wiring, flows, ranked) AND verified AI agent contributions.
| Component | Sub-score | Weight | Contribution |
|---|---|---|---|
structure_score |
100.0 | 0.15 | 15.00 |
security_score |
100.0 | 0.25 | 25.00 |
testing_score |
100.0 | 0.20 | 20.00 |
documentation_score |
100.0 | 0.15 | 15.00 |
practices_score |
89.0 | 0.15 | 13.35 |
code_quality |
45.0 | 0.10 | 4.50 |
| Overall | 1.00 | 92.8 |
Top 10 actions, ranked by impact × ease. Severity drives impact; tag-based fix-clarity drives ease.
src/notebooklm/_auth/refresh.py:342.github/workflows/codeql.yml:24.github/workflows/codeql.yml:38.github/workflows/test.yml:30.github/workflows/test.yml:110.github/workflows/testpypi-publish.yml.github/workflows/claude.yml.github/workflows/publish.ymlClick "Find this gap" on any action above to jump to it on the Findings tab. Adjust the chip bar to filter by impact (severity), layer, or source.
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