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
Catboost is the strongest overall pick here because it has a 100/100 readiness score and fits Workflow automation.
Strongest overall
Catboost
Use this as a leading candidate, then validate the README and install path in your own agent stack.
Fastest prototype
Catboost
Best first install candidate based on install readiness and adoption.
Freshest repo
H2o 3
Most recent maintenance signal among this shortlist.
| Signal | Tennis Crystal Ball Ultimate Tennis Statistics and Tennis Crystal Ball - Tennis Big Data Analysis and Prediction | Catboost A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU. | H2o 3 H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc. | Feast The Open Source Feature Store for AI/ML |
|---|---|---|---|---|
| Quality | 51/100 Needs review | 100/100 Excellent | 100/100 Excellent | 100/100 Excellent |
| Decision verdict | 41/100 Needs manual review Do a manual repository review before adding this to an agent workflow. | 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 | 288 stars 0 installs | 9.0K stars 0 installs | 7.5K stars 0 installs | 7.1K stars 0 installs |
| Freshness | Feb 22, 2022 | Jun 12, 2026 | Jun 16, 2026 | Jun 16, 2026 |
| Use-case fit | ||||
| Stack fit | ||||
| Platform hints | Java, Machine Learning, Claude Code | C++, Machine Learning, Claude Code | Jupyter Notebook, Machine Learning, Claude Code | Python, Machine Learning, Claude Code |
| Warnings | Repository looks stale · No OpenAgentSkill engagement data yet | No OpenAgentSkill engagement data yet | No OpenAgentSkill engagement data yet | No OpenAgentSkill engagement data yet |
| Best for | Sports analytics workflows · Claude Code teams · builders willing to evaluate younger projects | Workflow automation workflows · Claude Code teams · teams that value GitHub adoption signals | RAG and knowledge workflows · Claude Code teams · teams that value GitHub adoption signals | GitHub automation workflows · Claude Code teams · teams that value GitHub adoption signals |
| Not ideal for | teams that require actively maintained dependencies · production agents without a repository 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 mcekovic/tennis-crystal-ball | $ npx skills add catboost/catboost | $ npx skills add h2oai/h2o-3 | $ npx skills add feast-dev/feast |