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
Xgboost is the strongest overall pick here because it has a 100/100 readiness score and fits Sports analytics.
Strongest overall
Xgboost
Use this as a leading candidate, then validate the README and install path in your own agent stack.
Fastest prototype
Xgboost
Best first install candidate based on install readiness and adoption.
Freshest repo
Xgboost
Most recent maintenance signal among this shortlist.
| Signal | MachineLearningWithMe A repository contains more than 12 common statistical machine learning algorithm implementations. 常见10余种机器学习算法原理与实现及视频讲解。@月来客栈 出品 | 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. | Xgboost Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow | LightGBM A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks. |
|---|---|---|---|---|
| Quality | 77/100 Strong | 100/100 Excellent | 100/100 Excellent | 100/100 Excellent |
| Decision verdict | 76/100 Strong shortlist Shortlist this skill and compare it with close alternatives before production adoption. | 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 | 284 stars 0 installs | 7.5K stars 0 installs | 28K stars 0 installs | 18K stars 0 installs |
| Freshness | Jun 15, 2026 | Jun 16, 2026 | Jun 16, 2026 | Jun 9, 2026 |
| Use-case fit | ||||
| Stack fit | ||||
| Platform hints | Jupyter Notebook, Machine Learning, Claude Code | Jupyter Notebook, Machine Learning, Claude Code | C++, Machine Learning, Claude Code | C++, Machine Learning, Claude Code |
| Warnings | No OpenAgentSkill engagement data yet | No OpenAgentSkill engagement data yet | No OpenAgentSkill engagement data yet | No major risk signals from current metadata |
| Best for | GitHub automation workflows · Claude Code teams · builders willing to evaluate younger projects | RAG and knowledge workflows · Claude Code teams · teams that value GitHub adoption signals | Sports analytics workflows · Claude Code teams · teams that value GitHub adoption signals | Workflow 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 | 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 | 5 views 0 install copies |
| Install | $ npx skills add mlwithme/MachineLearningWithMe | $ npx skills add h2oai/h2o-3 | $ npx skills add dmlc/xgboost | $ npx skills add lightgbm-org/LightGBM |