Is Auto Flow Safe?
Auto Flow — Nerq Trust Score 53.5/100 (C- grade). Score based on 2 independent trust signals. Last analyzed: 2026-03-20
Auto Flow is a Python package with a Nerq Trust Score of 53.5/100 (C-), based on 3 independent data dimensions. Last analyzed: 2026-03-20 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 Auto Flow safe?
Trust Score Breakdown — Auto Flow has a Nerq Trust Score of 53.5/100 (C-). Measured across 2 independent trust signals (as of 2026-03-20).
What is Auto Flow's trust score?
Auto Flow has a Nerq Trust Score of 53.5/100, 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 Auto Flow?
Auto Flow's strongest signal is security at 90/100. No known vulnerabilities have been detected.
What is Auto Flow and who maintains it?
| Author | qichun tang |
| Category | Python Packages |
| Source | N/A |
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Safety Guide: Auto Flow
What is Auto Flow?
Auto Flow is a Python package — AutoFlow: Automatic machine learning workflow modeling platform..
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=auto-flow
Key Safety Concerns for Python package
When evaluating any Python package, watch for: dependency vulnerabilities, malicious uploads, maintenance status.
Measured Signals
Auto Flow has a Nerq Trust Score of 54/100 (C-). This score is a composite of automated measurements of security, maintenance, community, and quality signals.
Key Takeaways
- Auto Flow has a Trust Score of 54/100 (C-).
- 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.