Is Scitex Dev Safe?
Scitex Dev — Nerq Trust Score 66.0/100 (B- grade). Score based on 2 independent trust signals. Last analyzed: 2026-07-22
Scitex Dev is a Python package with a Nerq Trust Score of 66.0/100 (B-), based on 3 independent data dimensions. Last analyzed: 2026-07-22 Security: 90/100. Popularity: 60/100. Data sourced from PyPI registry, GitHub repository, NVD, OSV.dev, and OpenSSF Scorecard. Last updated: 2026-07-22. Machine-readable data (JSON).
Is Scitex Dev safe?
Trust Score Breakdown — Scitex Dev has a Nerq Trust Score of 66.0/100 (B-). Measured across 2 independent trust signals (as of 2026-07-22).
What is Scitex Dev's trust score?
Scitex Dev 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 Scitex Dev?
Scitex Dev's strongest signal is security at 90/100. No known vulnerabilities have been detected.
What is Scitex Dev and who maintains it?
| Author | Unknown |
| Category | Python Packages |
| Source | N/A |
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Safety Guide: Scitex Dev
What is Scitex Dev?
Scitex Dev is a Python package — Shared developer utilities for the SciTeX ecosystem.
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=scitex-dev
Key Safety Concerns for Python package
When evaluating any Python package, watch for: dependency vulnerabilities, malicious uploads, maintenance status.
Measured Signals
Scitex Dev 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
- Scitex Dev 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.