AtelierLabs vs scikit-image — Trust Score Comparison
Side-by-side trust comparison of AtelierLabs and scikit-image. Scores based on security, compliance, maintenance, popularity, and ecosystem signals.
atelier — Nerq Trust Score 67.2/100 (B-). scikit-image — Nerq Trust Score 77.2/100 (B+). scikit-image leads by 10.0 points.
Detailed Score Analysis
| Dimension | atelier | scikit-image |
|---|---|---|
| Security | 90/100 | 90/100 |
| Maintenance | 100/100 | 100/100 |
| Popularity | 30/100 | 90/100 |
| Quality | 50/100 | 55/100 |
| Community | 35/100 | 35/100 |
Five-dimension Nerq trust breakdown (registries: pypi / pypi). Scored equally weighted across security, maintenance, popularity, quality, community.
Detailed Metric Comparison
| Metric | AtelierLabs | scikit-image |
|---|---|---|
| Trust Score | 72.7/100 | 67.5/100 |
| Grade | B | B- |
| Stars | 0 | 6,459 |
| Category | coding | other |
| Security | 0 | 0 |
| Compliance | 100 | 100 |
| Maintenance | 1 | 0 |
| Documentation | 1 | 0 |
| EU AI Act Risk | minimal | N/A |
| Verified | Yes | No |
Verdict
AtelierLabs leads with a trust score of 72.7/100 compared to scikit-image's 67.5/100 (a 5.2-point difference). AtelierLabs scores higher on maintenance (1 vs 0). However, scikit-image has stronger community adoption (6,459 vs 0 stars). Both agents should be evaluated based on your specific requirements.
Detailed Analysis
Security
AtelierLabs leads on security with a score of 0/100 compared to scikit-image's 0/100. This score reflects dependency vulnerability analysis, known CVE exposure, and security best practices. A higher security score means fewer known vulnerabilities and better security hygiene in the codebase.
Maintenance & Activity
AtelierLabs demonstrates stronger maintenance activity (1/100 vs 0/100). This metric captures commit frequency, issue response times, and release cadence. Actively maintained tools receive faster security patches and are less likely to accumulate technical debt.
Documentation
AtelierLabs has better documentation (1/100 vs 0/100). Good documentation reduces onboarding time and helps teams adopt the tool safely. This score evaluates README completeness, API documentation, code examples, and tutorial availability.
Community & Adoption
AtelierLabs has 0 GitHub stars while scikit-image has 6,459. scikit-image has significantly broader community adoption, which typically means more Stack Overflow answers, more third-party tutorials, and faster ecosystem development.
When to Choose Each Tool
Choose AtelierLabs if you need:
- Higher overall trust score — more reliable for production use
- More actively maintained with faster release cadence
- Better documentation for faster onboarding
Choose scikit-image if you need:
- Larger community (6,459 vs 0 stars)
Switching from AtelierLabs to scikit-image (or vice versa)
When migrating between AtelierLabs and scikit-image, consider these factors:
- API Compatibility: AtelierLabs (coding) and scikit-image (other) serve different categories, so migration may require significant refactoring.
- Security Review: Run a security audit after migration. Check the AtelierLabs safety report and scikit-image safety report for known issues.
- Testing: Ensure your test suite covers all integration points before switching in production.
- Community Support: AtelierLabs has 0 stars and scikit-image has 6,459. Larger communities typically mean better Stack Overflow answers and migration guides.
Related Pages
Frequently Asked Questions
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Last updated: 2026-05-09 | Data refreshed weekly
Disclaimer: Nerq trust scores are automated assessments based on publicly available signals. They are not endorsements or guarantees. Always conduct your own due diligence.