Data Science & ML Agent Skills

PySCF Quantum Chemistry

Performs ab initio quantum chemistry calculations and molecular simulations using the PySCF framework.

SymPy Symbolic Mathematics

Performs complex symbolic mathematical operations including calculus, equation solving, and algebraic manipulation using the SymPy library.

NetworkX Graph Analysis

Analyzes and manipulates complex network structures and graph algorithms using Python's leading network science library.

SciPy Scientific Computing

Performs advanced scientific and technical computing tasks including numerical integration, optimization, and statistical analysis.

Plotly Interactive Visualization

Generates interactive, web-based charts and complex data dashboards using Python's high-level Plotly library.

PennyLane Quantum Machine Learning

Integrates differentiable quantum computing circuits into classical machine learning workflows for hybrid model development.

NumPy Scientific Computing

Optimizes numerical computing tasks in Python using high-performance array operations and vectorized mathematical functions.

Statsmodels Statistical Analysis

Performs advanced statistical modeling, hypothesis testing, and rigorous data inference using R-style formulas.

Seaborn Statistical Visualization

Generates sophisticated, publication-quality statistical graphics and exploratory data visualizations using the Python Seaborn library.

SunPy Solar Physics Analysis

Enables advanced solar data processing, coordinate transformations, and multi-instrument analysis using the SunPy ecosystem.

Matplotlib Data Visualization

Generates publication-quality 2D plots, scientific visualizations, and complex multi-panel figures using industry-standard Python patterns.

Photutils Astronomical Photometry

Detects astronomical sources and performs high-precision photometry on digital images using the Astropy ecosystem.

Matplotlib Pro Viz & Animation

Generates high-performance animations, publication-quality scientific figures, and interactive data visualization tools using advanced Matplotlib techniques.

PyTorch Model Deployment & Optimization

Deploys and optimizes PyTorch models for production environments, edge devices, and high-performance C++ applications.

Astropy for Astronomy & Astrophysics

Simplifies astronomical data analysis and physical calculations using standardized units, coordinates, and cosmological models.

Polars High-Performance Data Processing

Accelerates data manipulation and analysis using the blazingly fast Polars DataFrame library for Python and Rust.

Scalable Parallel Computing with Dask

Scales Python's data science stack to multi-core systems and distributed clusters using lazy evaluation and task scheduling.

Molecular Dynamics Analysis (MDAnalysis)

Analyzes molecular dynamics trajectories and structural data using the MDAnalysis Python library for biophysical research.

Qiskit Quantum Computing

Facilitates the design, simulation, and execution of quantum circuits and algorithms using IBM's Qiskit framework.

RDKit Cheminformatics & Drug Discovery

Provides specialized tools for molecular manipulation, chemical property calculation, and machine learning in drug discovery workflows.

Qiskit Hardware & Pulse Control

Optimizes and executes quantum circuits on physical IBM Quantum hardware using advanced error mitigation and pulse-level control.

Pydicom Medical Imaging

Provides specialized tools for reading, modifying, and writing DICOM medical imaging data within Python environments.

Scikit-Learn Machine Learning

Implements industry-standard machine learning workflows in Python for predictive data analysis including classification, regression, and clustering.

Lifelines Survival Analysis

Performs advanced survival analysis and time-to-event modeling using the lifelines library for medical, clinical, and epidemiological research.

Scikit-Image Assistant

Simplifies scientific image processing and analysis using Python-based algorithms and NumPy-compatible workflows.

tqdm Progress Monitoring

Implements intelligent, low-overhead progress bars for Python loops, data processing, and machine learning workflows.

NumPy Low-Level Optimization

Optimizes NumPy performance through advanced memory management, stride manipulation, and zero-copy operations.

Atomic Simulation Environment (ASE)

Builds, manipulates, and analyzes atomistic simulations using a universal Python interface for molecular dynamics and quantum chemistry codes.

Scikit-Learn Model Explainability

Provides specialized guidance and code patterns for interpreting machine learning models using scikit-learn, SHAP, and advanced diagnostic tools.

Lattice Spatial-Collective

Implements a decentralized context-sharing protocol for multi-agent systems using cryptographic sharding and Byzantine fault tolerance.