Skill comparison
Compare agent skills before installing.
Put high-signal skills side by side and inspect quality, adoption, freshness, install readiness, use-case fit, and warnings in one place.
Comparing 1 skill
Use this as a shortlist, then open the skill detail page before adopting.
Decision summary
GCC SFCN is the strongest overall pick here because it has a 38/100 readiness score and fits Coding agents.
Strongest overall
GCC SFCN
Do a manual repository review before adding this to an agent workflow.
Fastest prototype
GCC SFCN
Best first install candidate based on install readiness and adoption.
Freshest repo
GCC SFCN
Most recent maintenance signal among this shortlist.
| Signal | GCC SFCN This is the official code of spatial FCN in the paper Learning from Synthetic Data for Crowd Counting in the Wild [CVPR2019]. |
|---|---|
| Quality | 48/100 Needs review |
| Decision verdict | 38/100 Needs manual review Do a manual repository review before adding this to an agent workflow. |
| Adoption | 162 stars 0 installs |
| Freshness | Sep 4, 2019 |
| Use-case fit | |
| Stack fit | |
| Platform hints | Python, Computer Vision, Claude Code |
| Warnings | Repository looks stale · No OpenAgentSkill engagement data yet |
| Best for | Coding agents workflows · Claude Code teams · builders willing to evaluate younger projects |
| Not ideal for | teams that require actively maintained dependencies · production agents without a repository review |
| OpenAgentSkill engagement | 0 views 0 install copies |
| Install | $ npx skills add gjy3035/GCC-SFCN |