Skill comparison
Compare agent skills before installing.
Comparing 4 skills
Use this as a shortlist, then open the skill detail page before adopting.
Decision summary
Google Research is the strongest overall pick here because it has a 100/100 readiness score and fits RAG and knowledge.
Strongest overall
Google Research
Use this as a leading candidate, then validate the README and install path in your own agent stack.
Fastest prototype
Google Research
Best first install candidate based on install readiness and adoption.
Freshest repo
Google Research
Most recent maintenance signal among this shortlist.
| Signal | EconML ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x. | CLIP CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image | Google Research Google Research | Mlops Zoomcamp Free MLOps course from DataTalks.Club |
|---|---|---|---|---|
| Quality | 97/100 Excellent | 100/100 Excellent | 100/100 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. | 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 | 4.7K stars Verified outcomes are shown on each skill page | 34K stars Verified outcomes are shown on each skill page | 38K stars Verified outcomes are shown on each skill page | 15K stars Verified outcomes are shown on each skill page |
| Freshness | Jun 15, 2026 | Mar 25, 2026 | Jun 16, 2026 | Jun 10, 2026 |
| Use-case fit | ||||
| Workflow fit | ||||
| Platform hints | Jupyter Notebook, Machine Learning, Claude Code | Jupyter Notebook, Machine Learning, Claude Code | Jupyter Notebook, Machine Learning, Claude Code | Jupyter Notebook, Machine Learning, Claude Code |
| Warnings | No OpenAgentSkill engagement data yet | No OpenAgentSkill engagement data yet | No OpenAgentSkill engagement data yet | No OpenAgentSkill engagement data yet |
| Best for | RAG and knowledge workflows · Claude Code teams · teams that value GitHub adoption signals | Coding agents workflows · Claude Code teams · teams that value GitHub adoption signals | RAG and knowledge workflows · Claude Code teams · teams that value GitHub adoption signals | Coding agents 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 | 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 | 0 views 0 install copies | 0 views 0 install copies |
| Install | $ npx skills add py-why/EconML | $ npx skills add openai/CLIP | $ npx skills add google-research/google-research | $ npx skills add DataTalksClub/mlops-zoomcamp |