Is Pynitefea Safe?
Pynitefea — Nerq Trust Score 66.0/100 (B- grade). Score based on 2 independent trust signals. Last analyzed: 2026-09-01
Pynitefea is a Python package with a Nerq Trust Score of 66.0/100 (B-), based on 3 independent data dimensions. Last analyzed: 2026-09-01 Security: 90/100. Popularity: 60/100. Data sourced from PyPI registry, GitHub repository, NVD, OSV.dev, and OpenSSF Scorecard. Last updated: 2026-09-01. Machine-readable data (JSON).
Is Pynitefea safe?
Trust Score Breakdown — Pynitefea has a Nerq Trust Score of 66.0/100 (B-). Measured across 2 independent trust signals (as of 2026-09-01).
What is Pynitefea's trust score?
Pynitefea has a Nerq Trust Score of 66.0/100, 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 Pynitefea?
Pynitefea's strongest signal is security at 90/100. No known vulnerabilities have been detected.
What is Pynitefea and who maintains it?
| Author | D. Craig Brinck, PE, SE |
| Category | Python Packages |
| Source | N/A |
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Safety Guide: Pynitefea
What is Pynitefea?
Pynitefea is a Python package — A simple elastic 3D structural finite element library for Python..
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=pynitefea
Key Safety Concerns for Python package
When evaluating any Python package, watch for: dependency vulnerabilities, malicious uploads, maintenance status.
Measured Signals
Pynitefea has a Nerq Trust Score of 66/100 (B-). This score is a composite of automated measurements of security, maintenance, community, and quality signals.
Key Takeaways
- Pynitefea has a Trust Score of 66/100 (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.