Is Cnstd Safe?
Cnstd — Nerq Trust Score 67.2/100 (B- grade). Score based on 2 independent trust signals. Last analyzed: 2026-07-22
Cnstd is a Python package with a Nerq Trust Score of 67.2/100 (B-), based on 3 independent data dimensions. Last analyzed: 2026-07-22 Security: 90/100. Popularity: 60/100. Data sourced from PyPI registry, GitHub repository, NVD, OSV.dev, and OpenSSF Scorecard. Last updated: 2026-07-22. Machine-readable data (JSON).
Is Cnstd safe?
Trust Score Breakdown — Cnstd has a Nerq Trust Score of 67.2/100 (B-). Measured across 2 independent trust signals (as of 2026-07-22).
What is Cnstd's trust score?
Cnstd has a Nerq Trust Score of 67.2/100, 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 Cnstd?
Cnstd's strongest signal is security at 90/100. No known vulnerabilities have been detected.
What is Cnstd and who maintains it?
| Author | breezedeus |
| Category | Python Packages |
| Source | N/A |
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Safety Guide: Cnstd
What is Cnstd?
Cnstd is a Python package — Python3 package for Chinese/English Scene Text Detection (STD), Mathematical Formula Detection (MFD), and Layout Analysis, with free pretrained models.
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=cnstd
Key Safety Concerns for Python package
When evaluating any Python package, watch for: dependency vulnerabilities, malicious uploads, maintenance status.
Measured Signals
Cnstd has a Nerq Trust Score of 67/100 (B-). This score is a composite of automated measurements of security, maintenance, community, and quality signals.
Key Takeaways
- Cnstd has a Trust Score of 67/100 (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.
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.