Alternatives
Compare similar skills by workflow fit, trust score, quality, GitHub adoption, maintenance, and install readiness.
Current skill
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.
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
$ npx skills add dmlc/xgboostAn Open Source Machine Learning Framework for Everyone
$ npx skills add tensorflow/tensorflowH2O 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.
$ npx skills add h2oai/h2o-3A 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.
$ npx skills add catboost/catboostRay is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
$ npx skills add ray-project/ray⛔️ DEPRECATED – See https://github.com/ageron/handson-ml3 or handson-mlp instead.
$ npx skills add ageron/handson-mlA game theoretic approach to explain the output of any machine learning model.
$ npx skills add shap/shapA hyperparameter optimization framework
$ npx skills add optuna/optunaA Python library for anomaly detection across tabular, time series, graph, text, and image data. 60+ detectors, benchmark-backed ADEngine orchestration, and an agentic workflow for AI agents.
$ npx skills add yzhao062/pyodCross-platform, customizable ML solutions for live and streaming media.
$ npx skills add google-ai-edge/mediapipeA unified framework for machine learning with time series
$ npx skills add sktime/sktimePArallel Distributed Deep LEarning: Machine Learning Framework from Industrial Practice （『飞桨』核心框架，深度学习&机器学习高性能单机、分布式训练和跨平台部署）
$ npx skills add PaddlePaddle/PaddleONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
$ npx skills add microsoft/onnxruntimeMNN: A blazing-fast, lightweight inference engine battle-tested by Alibaba, powering high-performance on-device LLMs and Edge AI.
$ npx skills add alibaba/MNNA toolkit for making real world machine learning and data analysis applications in C++
$ npx skills add davisking/dlibTensor library for machine learning
$ npx skills add ggml-org/ggmlHow to choose
Use an alternative when it has a clearer install path, higher trust score, fresher maintenance, or better platform fit for your current agent stack. Keep LightGBM if it already passes your workflow test and repository review.
Next step
Open the compare page, test the install commands in a sandbox, and check each repository before using a skill in production.
Alternatives
Compare similar skills by workflow fit, trust score, quality, GitHub adoption, maintenance, and install readiness.
Current skill
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.
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
$ npx skills add dmlc/xgboostAn Open Source Machine Learning Framework for Everyone
$ npx skills add tensorflow/tensorflowH2O 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.
$ npx skills add h2oai/h2o-3A 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.
$ npx skills add catboost/catboostRay is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
$ npx skills add ray-project/ray⛔️ DEPRECATED – See https://github.com/ageron/handson-ml3 or handson-mlp instead.
$ npx skills add ageron/handson-mlA game theoretic approach to explain the output of any machine learning model.
$ npx skills add shap/shapA hyperparameter optimization framework
$ npx skills add optuna/optunaA Python library for anomaly detection across tabular, time series, graph, text, and image data. 60+ detectors, benchmark-backed ADEngine orchestration, and an agentic workflow for AI agents.
$ npx skills add yzhao062/pyodCross-platform, customizable ML solutions for live and streaming media.
$ npx skills add google-ai-edge/mediapipeA unified framework for machine learning with time series
$ npx skills add sktime/sktimePArallel Distributed Deep LEarning: Machine Learning Framework from Industrial Practice （『飞桨』核心框架，深度学习&机器学习高性能单机、分布式训练和跨平台部署）
$ npx skills add PaddlePaddle/PaddleONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
$ npx skills add microsoft/onnxruntimeMNN: A blazing-fast, lightweight inference engine battle-tested by Alibaba, powering high-performance on-device LLMs and Edge AI.
$ npx skills add alibaba/MNNA toolkit for making real world machine learning and data analysis applications in C++
$ npx skills add davisking/dlibTensor library for machine learning
$ npx skills add ggml-org/ggmlHow to choose
Use an alternative when it has a clearer install path, higher trust score, fresher maintenance, or better platform fit for your current agent stack. Keep LightGBM if it already passes your workflow test and repository review.
Next step
Open the compare page, test the install commands in a sandbox, and check each repository before using a skill in production.