ml-example vs create-sss — Trust Score Comparison

Name: ml-example vs create-sss Trust Comparison
Creator: Nerq

Side-by-side trust comparison of ml-example and create-sss. Scores based on security, compliance, maintenance, popularity, and ecosystem signals.

ml-example scores 0.0/100 (D) while create-sss scores 56.0/100 (C) on the Nerq Trust Score. create-sss leads by 56.0 points. ml-example is a uncategorized agent with 0 stars. create-sss is a uncategorized agent with 0 stars.

ml-example

0.0

Categoryuncategorized

Stars0

Sourcepypi_full

Compliance92

create-sss

56.0

Categoryuncategorized

Stars0

Sourcenpm

Security90

Maintenance58

Documentation65

Detailed Metric Comparison

Metric	ml-example	create-sss
Trust Score	0.0/100	56.0/100
Grade	D	C
Stars	0	0
Category	uncategorized	uncategorized
Security	N/A	90
Compliance	92	N/A
Maintenance	N/A	58
Documentation	N/A	65
EU AI Act Risk	N/A	N/A
Verified	No	No

Verdict

create-sss leads with a trust score of 56.0/100 compared to ml-example's 0.0/100 (a 56.0-point difference). Both agents should be evaluated based on your specific requirements.

Detailed Analysis

Security

Security scores measure dependency vulnerabilities, CVE exposure, and security practices. ml-example scores N/A and create-sss scores 90 on this dimension.

Maintenance & Activity

Activity scores reflect how actively each project is maintained. ml-example: N/A, create-sss: 58.

Documentation

Documentation quality is evaluated based on README, API docs, and example coverage. ml-example: N/A, create-sss: 65.

Community & Adoption

ml-example has 0 GitHub stars while create-sss has 0. Both tools have comparable community sizes, suggesting similar levels of ecosystem support and third-party resources.

When to Choose Each Tool

Choose ml-example if you need:

Consider if it better fits your specific use case

Choose create-sss if you need:

Higher overall trust score — more reliable for production use
Stronger security profile with fewer known vulnerabilities
More actively maintained with faster release cadence
Better documentation for faster onboarding

Switching from ml-example to create-sss (or vice versa)

When migrating between ml-example and create-sss, consider these factors:

API Compatibility: ml-example (uncategorized) and create-sss (uncategorized) share similar interfaces since they are in the same category.
Security Review: Run a security audit after migration. Check the ml-example safety report and create-sss safety report for known issues.
Testing: Ensure your test suite covers all integration points before switching in production.
Community Support: ml-example has 0 stars and create-sss has 0. Larger communities typically mean better Stack Overflow answers and migration guides.

ml-example Safety Report create-sss Safety Report ml-example Alternatives create-sss Alternatives

Frequently Asked Questions

Which is safer, ml-example or create-sss?

Based on Nerq's independent trust assessment, ml-example has a trust score of 0.0/100 (D) while create-sss scores 56.0/100 (C). The 56.0-point difference suggests create-sss has a stronger trust profile. Trust scores are based on security, compliance, maintenance, documentation, and community adoption.

How do ml-example and create-sss compare on security?

ml-example has a security score of N/A/100 and create-sss scores 90/100. There is a notable difference in their security assessments. ml-example's compliance score is 92/100 (EU risk: N/A), while create-sss's is N/A/100 (EU risk: N/A).

Should I use ml-example or create-sss?

The choice depends on your requirements. ml-example (uncategorized, 0 stars) and create-sss (uncategorized, 0 stars) serve similar use cases. On trust, ml-example scores 0.0/100 and create-sss scores 56.0/100. Review the full KYA reports for each agent before making a decision. Consider factors like integration requirements, documentation quality (N/A vs 65), and maintenance activity (N/A vs 58).

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Last updated: 2026-07-30 | 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.