Is Chinstrap Safe?
Chinstrap — Nerq Trust Score 63.0/100 (C+ grade). Score based on 2 independent trust signals. Last analyzed: 2026-03-20
Chinstrap is a Python package with a Nerq Trust Score of 63.0/100 (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 Chinstrap safe?
Trust Score Breakdown — Chinstrap has a Nerq Trust Score of 63.0/100 (C+). Measured across 2 independent trust signals (as of 2026-03-20).
What is Chinstrap's trust score?
Chinstrap has a Nerq Trust Score of 63.0/100, 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 Chinstrap?
Chinstrap's strongest signal is security at 90/100. No known vulnerabilities have been detected.
What is Chinstrap and who maintains it?
| Author | ant4g0nist |
| Category | Python Packages |
| Source | N/A |
Chinstrap Across Platforms
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Safety Guide: Chinstrap
What is Chinstrap?
Chinstrap is a Python package — A swiss-army-knife for Tezos Smart Contract developers.
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=chinstrap
Key Safety Concerns for Python package
When evaluating any Python package, watch for: dependency vulnerabilities, malicious uploads, maintenance status.
Measured Signals
Chinstrap has a Nerq Trust Score of 63/100 (C+). This score is a composite of automated measurements of security, maintenance, community, and quality signals.
Key Takeaways
- Chinstrap has a Trust Score of 63/100 (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.