Is Aliby Parser Safe?
Aliby Parser — Nerq Trust Score 53.2/100 (C- grade). Score based on 2 independent trust signals. Last analyzed: 2026-03-25
Aliby Parser is a Python package with a Nerq Trust Score of 53.2/100 (C-), based on 3 independent data dimensions. Last analyzed: 2026-03-25 Security: 90/100. Popularity: 0/100. Data sourced from PyPI registry, GitHub repository, NVD, OSV.dev, and OpenSSF Scorecard. Last updated: 2026-03-25. Machine-readable data (JSON).
Is Aliby Parser safe?
Trust Score Breakdown — Aliby Parser has a Nerq Trust Score of 53.2/100 (C-). Measured across 2 independent trust signals (as of 2026-03-25).
What is Aliby Parser's trust score?
Aliby Parser has a Nerq Trust Score of 53.2/100, 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 Aliby Parser?
Aliby Parser's strongest signal is security at 90/100. No known vulnerabilities have been detected.
What is Aliby Parser and who maintains it?
| Author | Julian Pietsch |
| Category | Python Packages |
| Source | N/A |
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Safety Guide: Aliby Parser
What is Aliby Parser?
Aliby Parser is a Python package — Log files parser for aliby pipeline..
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=aliby-parser
Key Safety Concerns for Python package
When evaluating any Python package, watch for: dependency vulnerabilities, malicious uploads, maintenance status.
Measured Signals
Aliby Parser has a Nerq Trust Score of 53/100 (C-). This score is a composite of automated measurements of security, maintenance, community, and quality signals.
Key Takeaways
- Aliby Parser has a Trust Score of 53/100 (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.