Is Bob Safe?
Bob — Nerq Trust Score 72.5/100 (B grade). Score based on 2 independent trust signals. Last analyzed: 2026-03-20
Bob is a Python package with a Nerq Trust Score of 72.5/100 (B), based on 3 independent data dimensions. Last analyzed: 2026-03-20 Security: 90/100. Popularity: 45/100. Data sourced from PyPI registry, GitHub repository, NVD, OSV.dev, and OpenSSF Scorecard. Last updated: 2026-03-20. Machine-readable data (JSON).
Is Bob safe?
Trust Score Breakdown — Bob has a Nerq Trust Score of 72.5/100 (B). Measured across 2 independent trust signals (as of 2026-03-20).
What is Bob's trust score?
Bob has a Nerq Trust Score of 72.5/100, earning a B grade. This score is based on 2 independently measured dimensions including security, maintenance, and community adoption.
What are the key security findings for Bob?
Bob's strongest signal is security at 90/100. No known vulnerabilities have been detected.
What is Bob and who maintains it?
| Author | Unknown |
| Category | Python Packages |
| Source | N/A |
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Safety Guide: Bob
What is Bob?
Bob is a Python package — Bob is a free signal-processing and machine learning toolbox originally developed by the Biometrics group at Idiap Research Institute, in Switzerland..
How to Verify Safety
Run pip audit or safety check. Review on PyPI for download stats.
You can also check the trust score via API: GET /v1/preflight?target=bob
Key Safety Concerns for Python package
When evaluating any Python package, watch for: dependency vulnerabilities, malicious uploads, maintenance status.
Measured Signals
Bob has a Nerq Trust Score of 72/100 (B). This score is a composite of automated measurements of security, maintenance, community, and quality signals.
Key Takeaways
- Bob has a Trust Score of 72/100 (B).
- The score is a measured composite — it is not a suitability judgment. Evaluate the individual signals against your own requirements.
- Query the current measured values via the Nerq API.
Frequently Asked Questions
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Disclaimer: Nerq trust scores are automated measurements based on publicly available signals. They are not endorsements, verdicts, or guarantees of suitability. Always evaluate the signals against your own requirements.