Skill audit report
Efficient Segmentation Networks audit report.
Lightweight models for real-time semantic segmentationon PyTorch (include SQNet, LinkNet, SegNet, UNet, ENet, ERFNet, EDANet, ESPNet, ESPNetv2, LEDNet, ESNet, FSSNet, CGNet, DABNet, Fast-SCNN, ContextNet, FPENet, etc.)
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
PASS86
1.0K GitHub stars
Recent maintenance
FAIL38
2y 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 xiaoyufenfei/Efficient-Segmentation-Networks
Repository evidence
PASS86
https://github.com/xiaoyufenfei/Efficient-Segmentation-Networks
Review status
PASS88
AI review data available
Checks
Install and adoption review
Install path
92
npx skills add xiaoyufenfei/Efficient-Segmentation-Networks
Repository
88
https://github.com/xiaoyufenfei/Efficient-Segmentation-Networks
License
86
MIT
Maintenance
38
2y 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
1.0K GitHub stars
Warnings
- Repository appears stale
- Repository looks stale
- Quality score needs review
- Recent maintenance: 2y 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.
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