Is Catlearn Safe?
Catlearn — Nerq Trust Score 56.5/100 (C grade). Based on analysis of 2 trust dimensions, it is has notable safety concerns. Last updated: 2026-08-01.
Use Catlearn with some caution. Catlearn is a Python package with a Nerq Trust Score of 56.5/100 (C), based on 3 independent data dimensions. Below the recommended threshold of 70. Security: 90/100. Popularity: 0/100. Data sourced from PyPI registry, GitHub repository, NVD, OSV.dev, and OpenSSF Scorecard. Last updated: 2026-03-20. Machine-readable data (JSON).
Is Catlearn safe?
CAUTION — Catlearn has a Nerq Trust Score of 56.5/100 (C). It has moderate trust signals but shows some areas of concern that warrant attention. Suitable for development use — review security and maintenance signals before production deployment.
What is Catlearn's trust score?
Catlearn has a Nerq Trust Score of 56.5/100, earning a C grade. This score is based on 2 independently measured dimensions including security, maintenance, and community adoption.
What are the key security findings for Catlearn?
Catlearn's strongest signal is security at 90/100. No known vulnerabilities have been detected. It has not yet reached the Nerq Verified threshold of 70+.
What is Catlearn and who maintains it?
| Author | Paul C. Jennings |
| Category | Python Packages |
| Source | N/A |
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Safety Guide: Catlearn
What is Catlearn?
Catlearn is a Python package — Machine Learning using atomic-scale calculations..
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=CatLearn
Key Safety Concerns for Python package
When evaluating any Python package, watch for: dependency vulnerabilities, malicious uploads, maintenance status.
Trust Assessment
Catlearn has a Nerq Trust Score of 56/100 (C) and has not yet reached Nerq trust threshold (70+). This score is based on automated analysis of security, maintenance, community, and quality signals.
Key Takeaways
- Catlearn has a Trust Score of 56/100 (C).
- Review carefully before use — below trust threshold.
- Always verify independently using the Nerq API.
Frequently Asked Questions
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Disclaimer: Nerq trust scores are automated assessments based on publicly available signals. They are not endorsements or guarantees. Always conduct your own due diligence.