plotly

Provides XGBoost and LightGBM gradient boosting best practices, part of a collection of 72 agent skills for scientific computing and the Python data science ecosystem.

Scientific data visualization reference for matplotlib covering 2D plotting, heatmaps, contours, vector fields, multi-panel figures, and LaTeX-formatted publication-quality output from NumPy arrays and Pandas DataFrames.

Part of a collection of 72 agent skills for scientific computing, providing OpenCV computer vision capabilities that automatically enhance AI coding assistants with deep domain knowledge.

Provides expert guidance on Pyomo, a Python framework for formulating and solving mathematical optimization models. Covers linear programming, mixed-integer programming, non-linear programming, solver integration (IPOPT, SCIP, Gurobi, CPLEX, GLPK), and applications in energy systems, supply chain, and process engineering.

Google OR-Tools optimization skill covering vehicle routing, scheduling, bin packing, linear/integer programming, constraint programming, and resource allocation using the CP-SAT solver.

A skill for using Seaborn, the Python statistical data visualization library built on Matplotlib. It covers relationship plots, distribution analysis, categorical comparisons, regression visualization, heatmaps, cluster maps, and faceted grids for creating publication-quality graphics from Pandas DataFrames.

Part of a collection of 72 agent skills for scientific computing that provides expert-level guidance for scikit-learn and the scientific Python ecosystem. Skills auto-load based on your code context, offering best practices, common patterns, and performance optimization for libraries like NumPy, PyTorch, and SciPy.

A collection of 72 agent skills for scientific computing and data analysis, providing expert-level guidance across the scientific Python ecosystem including NumPy, PyTorch, scikit-learn, and other libraries through automatic topic detection.

A collection of 72 agent skills for scientific computing that auto-load domain knowledge for NumPy, SciPy, PyTorch, scikit-learn, and the broader scientific Python ecosystem, covering fields from bioinformatics to geospatial analysis.

Part of a comprehensive collection of 72 agent skills for scientific computing, providing expert-level guidance across the scientific Python ecosystem including NumPy, PyTorch, pandas, and domains like bioinformatics, geospatial analysis, and machine learning.