Is Astralbot Safe?
Astralbot — Nerq Trust Score 56.5/100 (C grade). Score based on 2 independent trust signals. Last analyzed: 2026-03-20
Astralbot is a Python package with a Nerq Trust Score of 56.5/100 (C), based on 3 independent data dimensions. Last analyzed: 2026-03-20 Security: 90/100. Popularity: 15/100. Data sourced from PyPI registry, GitHub repository, NVD, OSV.dev, and OpenSSF Scorecard. Last updated: 2026-03-20. Machine-readable data (JSON).
Is Astralbot safe?
Trust Score Breakdown — Astralbot has a Nerq Trust Score of 56.5/100 (C). Measured across 2 independent trust signals (as of 2026-03-20).
What is Astralbot's trust score?
Astralbot has a Nerq Trust Score of 56.5/100, 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 Astralbot?
Astralbot's strongest signal is security at 90/100. No known vulnerabilities have been detected.
What is Astralbot and who maintains it?
| Author | Nicolas Gargaud <[email protected]> |
| Category | Python Packages |
| Source | N/A |
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Safety Guide: Astralbot
What is Astralbot?
Astralbot is a Python package — Library to interact with an astralBot instance..
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=astralbot
Key Safety Concerns for Python package
When evaluating any Python package, watch for: dependency vulnerabilities, malicious uploads, maintenance status.
Measured Signals
Astralbot has a Nerq Trust Score of 56/100 (C). This score is a composite of automated measurements of security, maintenance, community, and quality signals.
Key Takeaways
- Astralbot has a Trust Score of 56/100 (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.