Is Scikit Image Safe?
Scikit Image — Nerq Trust Score 77.2/100 (B+ grade). Based on analysis of 2 trust dimensions, it is generally safe but has some concerns. Last updated: 2026-04-02.
Yes, Scikit Image is safe to use. Scikit Image is a Python package with a Nerq Trust Score of 77.2/100 (B+), based on 3 independent data dimensions. It is recommended for production use. Security: 90/100. Popularity: 90/100. Data sourced from PyPI registry, GitHub repository, NVD, OSV.dev, and OpenSSF Scorecard. Last updated: 2026-04-02. Machine-readable data (JSON).
Is Scikit Image safe?
YES — Scikit Image has a Nerq Trust Score of 77.2/100 (B+). It meets Nerq's trust threshold with strong signals across security, maintenance, and community adoption. Recommended for production use — review the full report below for specific considerations.
What is Scikit Image's trust score?
Scikit Image has a Nerq Trust Score of 77.2/100, 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 Scikit Image?
Scikit Image's strongest signal is security at 90/100. No known vulnerabilities have been detected. It meets the Nerq Verified threshold of 70+.
What is Scikit Image and who maintains it?
| Author | Unknown |
| Category | pypi |
| Source | N/A |
Scikit Image Across Platforms
Same developer/company in other registries:
Similar Pypi by Trust Score
Safety Guide: Scikit Image
What is Scikit Image?
Scikit Image is a Python package — Image processing in Python.
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=scikit-image
Key Safety Concerns for Python packages
When evaluating any Python package, watch for: dependency vulnerabilities, malicious uploads, maintenance status.
Trust Assessment
Scikit Image has a Nerq Trust Score of 77/100 (B+) and meets Nerq trust threshold. This score is based on automated analysis of security, maintenance, community, and quality signals.
Key Takeaways
- Scikit Image has a Trust Score of 77/100 (B+).
- Recommended for use — passes trust threshold.
- Always verify independently using the Nerq API.
Detailed Score Analysis
| Dimension | Score |
|---|---|
| Security | 90/100 |
| Privacy | 80/100 |
| Reliability | 90/100 |
| Transparency | 85/100 |
| Maintenance | 60/100 |
Based on 5 dimensions. Data from PyPI registry, GitHub repository, NVD, OSV.dev, and OpenSSF Scorecard.
What data does Scikit Image collect?
Scikit Image is a Python package maintained by Unknown. It receives approximately 5,686,320 weekly downloads. Licensed under Files: * Copyright: 2009-2022 the scikit-image team License: BSD-3-Clause .
As a development package, Scikit Image 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: Scikit Image Privacy Report · Privacy review
Is Scikit Image secure?
Security score: 90/100. Scikit Image has 0 known vulnerabilities (CVEs) in the National Vulnerability Database. This is a clean record.
Licensed under Files: * Copyright: 2009-2022 the scikit-image team License: BSD-3-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: Scikit Image Security Report
Scikit Image Across Platforms
Same developer/company in other registries:
How we calculated this score
Scikit Image's trust score of 77.2/100 (B+) is computed from PyPI registry, GitHub repository, NVD, OSV.dev, and OpenSSF Scorecard. The score reflects 5 independent dimensions: security (90/100), privacy (80/100), reliability (90/100), transparency (85/100), maintenance (60/100). 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.
This page was last reviewed on April 02, 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 assessments based on publicly available signals. They are not endorsements or guarantees. Always conduct your own due diligence.