Skill audit report
Convolutional KANs audit report.
This project extends the idea of the innovative architecture of Kolmogorov-Arnold Networks (KAN) to the Convolutional Layers, changing the classic linear transformation of the convolution to learnable non linear activations in each pixel.
OpenAgentSkill Trust Score
Stars, maintenance, license, docs, dependency risk, and installability.
The Trust Score is OpenAgentSkill's adoption layer. It is designed to help an agent decide whether a skill is safe enough to shortlist before installation.
GitHub adoption
INFO76
921 GitHub stars
Recent maintenance
FAIL38
1y since push
License clarity
PASS86
MIT
README/SKILL.md completeness
PASS90
Metadata includes enough usage and workflow context
Dependency risk
PASS90
no major dependency risk hints in public metadata
Install availability
PASS92
npx skills add AntonioTepsich/Convolutional-KANs
Repository evidence
PASS86
https://github.com/AntonioTepsich/Convolutional-KANs
Review status
PASS88
AI review data available
Checks
Install and adoption review
Install path
92
npx skills add AntonioTepsich/Convolutional-KANs
Repository
88
https://github.com/AntonioTepsich/Convolutional-KANs
License
86
MIT
Maintenance
38
1y since push
AI review
88
Approved with no listed issues
README/SKILL.md completeness
90
Usable description available
Dependency risk
90
no major dependency risk hints in public metadata
Adoption
88
921 GitHub stars
Warnings
- Repository appears stale
- Repository looks stale
- Quality score needs review
- Recent maintenance: 1y since push
Method
This report combines public metadata, AI review output, repository freshness, install readiness, OpenAgentSkill events, quality scoring, trust checks, and the agent safety gate. It is not a full source-code security review.
Compare nearby options