tondevrel/scientific-agent-skills

62 resources in this repository

🎯62Skills

🎯Skills62

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.

xgboost-lightgbm

🎯matplotlib🎯Skill

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.

matplotlib

🎯opencv🎯Skill

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.

opencv

🎯plotly🎯Skill

A skill providing expert guidance on Plotly, a Python interactive visualization library ideal for web-based charts, 3D plots, geographic maps, financial charts, and production dashboards via Dash. It covers both the high-level Plotly Express API and the low-level Graph Objects API with practical code examples.

plotly

🎯pyomo🎯Skill

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.

pyomo

🎯ortools🎯Skill

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

ortools

🎯seaborn🎯Skill

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.

seaborn

🎯scikit-learn🎯Skill

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.

scikit-learn

🎯numpy🎯Skill

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.

numpy

🎯scipy🎯Skill

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.

scipy

🎯matplotlib-pro🎯Skill

A comprehensive collection of 72 Agent Skills for scientific computing that cover the entire scientific Python ecosystem including NumPy, PyTorch, scikit-learn, pandas, matplotlib, and specialized fields like quantum computing, bioinformatics, and geospatial analysis.

matplotlib-pro

🎯fastapi-streamlit🎯Skill

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.

fastapi-streamlit

🎯scikit-image🎯Skill

A collection of 72 agent skills for scientific computing, research workflows, and data analysis across the scientific Python ecosystem including NumPy, PyTorch, scikit-learn, and domain-specific libraries.

scikit-image

🎯xarray🎯Skill

Part of a 72-skill collection for scientific computing, this skill provides AI coding assistants with expert-level xarray knowledge for multi-dimensional labeled data analysis. Auto-loads based on semantic matching when working with scientific Python libraries.

xarray

🎯chempy🎯Skill

A collection of 72 agent skills for scientific computing, research workflows, and data analysis across the scientific Python ecosystem. Covers domains including numerical computing, physics, chemistry, astronomy, and bioinformatics with libraries like NumPy, SciPy, PyTorch, and scikit-learn.

chempy

🎯ase🎯Skill

A collection of 72 agent skills for scientific computing that auto-load based on conversation context, covering the full scientific Python ecosystem including NumPy, PyTorch, scikit-learn, pandas, SciPy, and more. Provides expert-level guidance for numerical computing, machine learning, data analysis, geospatial work, and optimization.

Part of a collection of 72 agent skills for scientific computing that automatically enhance AI coding assistants with deep domain knowledge across the scientific Python ecosystem, covering libraries like NumPy, PyTorch, and scikit-learn.

mne

🎯numba🎯Skill

Part of a collection of 72 agent skills for scientific computing that provides expert-level guidance on scientific Python libraries. Skills auto-load based on context and cover domains like numerical computing, physics, chemistry, and data analysis.

numba

🎯statsmodels🎯Skill

Part of the Scientific Agent Skills collection of 72 skills for scientific computing, providing AI assistants with expert-level knowledge of the statsmodels library for statistical modeling, hypothesis testing, and time series analysis in Python.

statsmodels

🎯mdanalysis🎯Skill

Part of Scientific Agent Skills, a collection of 72 agent skills for scientific computing, research workflows, and data analysis that auto-load based on context to provide expert-level guidance for scientific Python libraries.

mdanalysis

🎯pytorch-research🎯Skill

A collection of 72 agent skills for scientific computing, research workflows, and data analysis, providing expert-level guidance across the entire scientific Python ecosystem including NumPy, PyTorch, scikit-learn, and more. Skills auto-load based on semantic matching, giving AI assistants domain-specific best practices and optimization techniques.

pytorch-research

🎯duckdb🎯Skill

A collection of 72 agent skills for scientific computing, research workflows, and data analysis across the Python ecosystem, covering NumPy, PyTorch, scikit-learn, quantum computing, astronomy, and bioinformatics.

duckdb

🎯sklearn-advanced🎯Skill

A collection of 72 agent skills for scientific computing, research workflows, and data analysis across the scientific Python ecosystem. Skills auto-load based on context, providing expert-level guidance for libraries like NumPy, PyTorch, scikit-learn, and pandas.

sklearn-advanced

🎯pyscf🎯Skill

Part of a 72-skill collection for scientific computing that auto-loads domain expertise for AI coding assistants, covering numerical computing, quantum chemistry, molecular dynamics, astronomy, and the broader scientific Python ecosystem.

pyscf

🎯biopython🎯Skill

A collection of 72 agent skills for scientific computing that automatically enhance AI coding assistants with domain expertise across the scientific Python ecosystem. Covers numerical computing, machine learning, data visualization, bioinformatics, geospatial analysis, and optimization.

biopython

🎯jax-pde🎯Skill

Part of a 72-skill collection for scientific computing that auto-loads domain expertise into AI coding assistants. Covers numerical computing, physics, chemistry, astronomy, bioinformatics, and the full scientific Python ecosystem including NumPy, SciPy, JAX, and PyTorch.

jax-pde

🎯sklearn-explainability🎯Skill

A skill from a collection of 72 agent skills for scientific computing, providing auto-loading expert knowledge for NumPy, PyTorch, scikit-learn, and other scientific Python libraries to enhance AI coding assistants.

sklearn-explainability

🎯networkx🎯Skill

A collection of 72 agent skills for scientific computing, research workflows, and data analysis across the scientific Python ecosystem. Covers numerical computing, physics, chemistry, astronomy, bioinformatics, machine learning, and more, with auto-loading based on semantic matching of code and questions.

networkx

🎯geopandas🎯Skill

A collection of 72 agent skills for scientific computing, research workflows, and data analysis that automatically enhance AI coding assistants with deep domain knowledge across the scientific Python ecosystem including NumPy, PyTorch, scikit-learn, and pandas.

geopandas

🎯pandas-performance🎯Skill

A scientific computing agent skill that provides expert-level guidance for optimizing pandas performance, including best practices, common pitfalls, and real-world patterns, as part of a collection of 72 skills for the scientific Python ecosystem.

pandas-performance

🎯numpy-low-level🎯Skill

A collection of 72 agent skills for scientific computing and data analysis, covering the full scientific Python ecosystem including NumPy, PyTorch, SciPy, and scikit-learn. Skills auto-load based on context to provide expert-level guidance for research workflows.

numpy-low-level

🎯rdkit🎯Skill

Part of Scientific Agent Skills, a collection of 72 skills for scientific computing covering chemistry (RDKit), physics, astronomy, bioinformatics, and machine learning. Skills auto-load based on conversation context to provide expert-level guidance for the scientific Python ecosystem.

rdkit

🎯pytorch🎯Skill

A skill from the Scientific Agent Skills collection providing expert-level knowledge for PyTorch and the scientific Python ecosystem, auto-loading domain-specific guidance for deep learning, numerical computing, and research workflows.

pytorch

🎯scanpy🎯Skill

A comprehensive collection of 72 agent skills for scientific computing and data analysis, providing expert-level AI guidance across the scientific Python ecosystem including NumPy, PyTorch, scikit-learn, astronomy, bioinformatics, and quantum computing.

scanpy

🎯tqdm🎯Skill

A collection of 72 agent skills for scientific computing and data analysis, providing expert-level guidance for libraries like NumPy, PyTorch, scikit-learn, and pandas, plus specialized domains including astronomy, bioinformatics, geospatial analysis, and quantum computing.

tqdm

🎯pydicom🎯Skill

A collection of 72 agent skills for scientific computing, research workflows, and data analysis that automatically enhance AI coding assistants with expert-level guidance across the scientific Python ecosystem including NumPy, PyTorch, scikit-learn, and more.

pydicom

🎯openbabel🎯Skill

Part of a collection of 72 agent skills for scientific computing that provides expert-level guidance for chemical file format conversion and molecular data processing using the OpenBabel toolkit within AI coding assistants.

openbabel

🎯lifelines🎯Skill

A collection of 72 Agent Skills for scientific computing and research workflows, providing expert-level guidance across the scientific Python ecosystem including NumPy, PyTorch, scikit-learn, Pandas, and geospatial analysis libraries.

lifelines

🎯scikit-bio🎯Skill

Part of the Scientific Agent Skills collection of 72 skills, this provides expert-level guidance for scikit-bio and bioinformatics workflows, automatically loading domain-specific knowledge when the AI assistant detects relevant scientific computing topics.

scikit-bio

🎯pytorch-deployment🎯Skill

A skill from Scientific Agent Skills, a comprehensive collection of 72 agent skills for scientific computing, research workflows, and data analysis. Covers the scientific Python ecosystem including NumPy, PyTorch, scikit-learn, SciPy, and domain-specific tools for physics, chemistry, astronomy, and bioinformatics.

pytorch-deployment

🎯sympy🎯Skill

Part of a collection of 72 agent skills for scientific computing that provides expert-level guidance for symbolic mathematics, algebraic manipulation, and equation solving using SymPy within AI coding assistants.

sympy

🎯prody🎯Skill

A comprehensive collection of 72 agent skills for scientific computing, research workflows, and data analysis across the scientific Python ecosystem. Skills auto-load contextually when AI assistants detect relevant topics.

prody

🎯transformers🎯Skill

A collection of 72 agent skills for scientific computing, research workflows, and data analysis across the scientific Python ecosystem. Skills auto-load based on context to provide expert-level guidance for libraries like NumPy, PyTorch, and scikit-learn.

transformers

🎯jax🎯Skill

A Claude Code plugin marketplace offering a collection of full-featured development system plugins and skills for project management, code review, and automation workflows.

jax

🎯pysam🎯Skill

Part of a collection of 72 agent skills for scientific computing that enhance AI coding assistants with deep domain knowledge. Skills auto-load based on context and cover numerical computing, physics, chemistry, bioinformatics, astronomy, and machine learning across the scientific Python ecosystem.

pysam

🎯polars🎯Skill

Part of Scientific Agent Skills, a collection of 72 agent skills for scientific computing, research workflows, and data analysis across the scientific Python ecosystem. Skills auto-load based on semantic matching to provide expert-level guidance with best practices and domain-specific solutions.

polars

🎯pyproj🎯Skill

Engineering-discipline skills for AI coding agents that enforce best practices, preventing common pitfalls like shipping bandaids, hand-rolling existing library solutions, and accepting unsafe code patterns.

pyproj

🎯cobrapy🎯Skill

Part of Scientific Agent Skills, a collection of 72 agent skills for scientific computing that auto-load based on your code context. Covers numerical computing, machine learning, data analysis, bioinformatics, geospatial analysis, and mathematical optimization across the Python ecosystem.

cobrapy

🎯qiskit🎯Skill

Collection of 72 agent skills for scientific computing that auto-load based on conversation context. Covers the scientific Python ecosystem including NumPy, SciPy, PyTorch, quantum computing (Qiskit, PennyLane), astronomy (Astropy), and bioinformatics.

qiskit

🎯astropy🎯Skill

Part of a collection of 72 agent skills for scientific computing that auto-loads domain-specific expertise for libraries like NumPy, PyTorch, scikit-learn, and Astropy, providing best practices and performance optimization guidance for scientific Python workflows.

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