Skill comparison
Use this as a shortlist, then open the skill detail page before adopting.
Decision summary
Strongest overall
Pytorch
Use this as a leading candidate, then validate the README and install path in your own agent stack.
Fastest prototype
Pytorch
Best first install candidate based on install readiness and adoption.
Freshest repo
Pytorch
Most recent maintenance signal among this shortlist.
| Signal | T81 558 Deep Learning T81-558: Keras - Applications of Deep Neural Networks @Washington University in St. Louis | Pytorch Tensors and Dynamic neural networks in Python with strong GPU acceleration |
|---|---|---|
| Quality | 94/100 Excellent | 100/100 Excellent |
| Decision verdict | 100/100 Production-ready Use this as a leading candidate, then validate the README and install path in your own agent stack. | 100/100 Production-ready Use this as a leading candidate, then validate the README and install path in your own agent stack. |
| Adoption | 5.7K stars Verified outcomes are shown on each skill page | 101K stars Verified outcomes are shown on each skill page |
| Freshness | Apr 25, 2026 | Jun 16, 2026 |
| Use-case fit |
| Workflow fit |
| Platform hints | Jupyter Notebook, Machine Learning, Claude Code | Python, Machine Learning, Claude Code |
| Warnings | No OpenAgentSkill engagement data yet | No OpenAgentSkill engagement data yet |
| Best for | Browser automation workflows · Claude Code teams · teams that value GitHub adoption signals | Browser automation workflows · Claude Code teams · teams that value GitHub adoption signals |
| Not ideal for | teams that need a vendor-supported SLA · high-compliance environments without internal security review | teams that need a vendor-supported SLA · high-compliance environments without internal security review |
| OpenAgentSkill engagement | 0 views 0 install copies | 0 views 0 install copies |
| Install | $ npx skills add jeffheaton/t81_558_deep_learning | $ npx skills add pytorch/pytorch |