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
Sparse To Dense is the strongest overall pick here because it has a 38/100 readiness score and fits Sports analytics.
Strongest overall
Sparse To Dense
Do a manual repository review before adding this to an agent workflow.
Fastest prototype
Sparse To Dense
Best first install candidate based on install readiness and adoption.
Freshest repo
Sparse To Dense
Most recent maintenance signal among this shortlist.
| Signal | Sparse To Dense ICRA 2018 "Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single Image" (Torch Implementation) |
|---|---|
| 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 | 444 stars 0 installs |
| Freshness | Jul 21, 2018 |
| Use-case fit | |
| Workflow fit | |
| Platform hints | Lua, Computer Vision, Claude Code |
| Warnings | Repository looks stale · No OpenAgentSkill engagement data yet |
| Best for | Sports analytics 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 fangchangma/sparse-to-dense |