Is Boolean.Py Safe?
Boolean.Py — Nerq Trust Score 72.5/100 (B grade). Score based on 2 independent trust signals. Last analyzed: 2026-07-21
Boolean.Py is a Python package with a Nerq Trust Score of 72.5/100 (B), based on 3 independent data dimensions. Last analyzed: 2026-07-21 Security: 90/100. Popularity: 90/100. Data sourced from PyPI registry, GitHub repository, NVD, OSV.dev, and OpenSSF Scorecard. Last updated: 2026-07-21. Machine-readable data (JSON).
Is Boolean.Py safe?
Trust Score Breakdown — Boolean.Py has a Nerq Trust Score of 72.5/100 (B). Measured across 2 independent trust signals (as of 2026-07-21).
What is Boolean.Py's trust score?
Boolean.Py has a Nerq Trust Score of 72.5/100, 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 Boolean.Py?
Boolean.Py's strongest signal is security at 90/100. No known vulnerabilities have been detected.
What is Boolean.Py and who maintains it?
| Author | Sebastian Kraemer |
| Category | Python Packages |
| Source | N/A |
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Safety Guide: Boolean.Py
What is Boolean.Py?
Boolean.Py is a Python package — Define boolean algebras, create and parse boolean expressions and create custom boolean DSL..
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=boolean.py
Key Safety Concerns for Python package
When evaluating any Python package, watch for: dependency vulnerabilities, malicious uploads, maintenance status.
Measured Signals
Boolean.Py has a Nerq Trust Score of 72/100 (B). This score is a composite of automated measurements of security, maintenance, community, and quality signals.
Key Takeaways
- Boolean.Py has a Trust Score of 72/100 (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.
Detailed Score Analysis
| Dimension | Score |
|---|---|
| Security | 90/100 |
| Maintenance | 73/100 |
| Popularity | 90/100 |
| Quality | 65/100 |
| Community | 35/100 |
Based on 5 dimensions. Data from PyPI registry, GitHub repository, NVD, OSV.dev, and OpenSSF Scorecard.
What data does Boolean.Py collect?
Boolean.Py is a Python package maintained by Sebastian Kraemer. It receives approximately 9,026,088 weekly downloads. Licensed under BSD-2-Clause.
As a development package, Boolean.Py does not directly collect end-user personal data. However, applications built with it may collect data depending on implementation. Privacy score: 80/100.
Review the package's dependencies for potential supply chain risks. Run your package manager's audit command regularly.
Full analysis: Boolean.Py Privacy Report · Privacy review
Is Boolean.Py secure?
Security score: 90/100. Boolean.Py has 0 known vulnerabilities (CVEs) in the National Vulnerability Database. This is a clean record.
Licensed under BSD-2-Clause, allowing code inspection. Open-source packages allow independent security review of the source code.
Run your package manager's audit command (`npm audit`, `pip audit`, `cargo audit`) to check for known vulnerabilities in your dependency tree.
Full analysis: Boolean.Py Security Report
How we calculated this score
Boolean.Py's trust score of 72.5/100 (B) is computed from PyPI registry, GitHub repository, NVD, OSV.dev, and OpenSSF Scorecard. The score reflects 5 independent dimensions: security (90/100), maintenance (73/100), popularity (90/100), quality (65/100), community (35/100). Each dimension is weighted equally to produce the composite trust score.
Nerq analyzes over 7.5 million entities across 26 registries using the same methodology, enabling direct cross-entity comparison. Scores are updated continuously as new data becomes available.
Signals last measured on July 21, 2026. Data version: 1.0.
Full methodology documentation · Machine-readable data (JSON 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.