Advanced sub-skill for NumPy focused on internal memory management, stride manipulation, structured arrays, and interfacing with C/Cython. Covers zero-copy operations and SIMD vectorization principles.

Python package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks. Supports various graph types (Directed, Undirected, Multigraphs) and features a vast library of standard graph algorithms. Use for network analysis, graph theory, social network analysis, biological networks, infrastructure networks, path finding, centrality measures, community detection, graph algorithms, shortest paths, PageRank, connectivity analysis, and routing optimization.

Open-source Python package for exploring, visualizing, and analyzing human neurophysiological data including EEG, MEG, sEEG, and ECoG.

Comprehensive guide for MDAnalysis - the Python library for analyzing molecular dynamics trajectories. Use for trajectory loading, RMSD/RMSF calculations, distance/angle/dihedral analysis, atom selections, hydrogen bonds, solvent accessible surface area, protein structure analysis, membrane analysis, and integration with Biopython. Essential for MD simulation analysis.

The foundational library for creating static, animated, and interactive visualizations in Python. Highly customizable and the industry standard for publication-quality figures. Use for 2D plotting, scientific data visualization, heatmaps, contours, vector fields, multi-panel figures, LaTeX-formatted plots, custom visualization tools, and plotting from NumPy arrays or Pandas DataFrames.

Professional sub-skill for Matplotlib focused on high-performance animations, complex multi-figure layouts (GridSpec), interactive widgets, and publication-ready typography (LaTeX/PGF).

Complete survival analysis library in Python. Handles right-censored data, Kaplan-Meier curves, and Cox regression. Standard for clinical trial analysis and epidemiology.

Composable transformations of Python+NumPy programs. Differentiate, vectorize, JIT-compile to GPU/TPU. Built for high-performance machine learning research and complex scientific simulations. Use for automatic differentiation, GPU/TPU acceleration, higher-order derivatives, physics-informed machine learning, differentiable simulations, and automatic vectorization.

Advanced sub-skill for JAX focused on solving Partial Differential Equations (PDEs) and Differentiable Physics. Covers Finite Difference Methods (FDM), Neural Operators, and Physics-Informed Neural Networks (PINNs).

A Pythonic interface to the HDF5 binary data format. It allows you to store huge amounts of numerical data and easily manipulate that data from NumPy. Features a hierarchical structure similar to a file system. Use for storing datasets larger than RAM, organizing complex scientific data hierarchically, storing numerical arrays with high-speed random access, keeping metadata attached to data, sharing data between languages, and reading/writing large datasets in chunks.

Programmatic mesh generation and mesh I/O for computational physics and FEM simulation. gmsh generates 2D and 3D meshes from geometric primitives and CAD-style boolean operations (union, difference, intersection) via the OpenCASCADE kernel, with fine-grained control over element size, adaptive refinement around features, and physical group tagging for boundary conditions. meshio reads and writes meshes across 40+ formats (GeoTIFF, VTK, VTU, Gmsh .msh, HDF5, ExodusII, XDMF, NetCDF) and performs format conversion. Use when: generating meshes for FEM/FEA simulation (prerequisite for FEniCS, deal.II, Firedrake), creating 2D or 3D computational domains from geometric descriptions, performing CSG (Constructive Solid Geometry) to build complex shapes from primitives, controlling mesh density and adaptive refinement near boundaries or singularities, tagging boundaries and subdomains for boundary conditions in solvers, converting meshes between simulation formats, or inspecting and manipulating mesh data programmatically. This is the geometry layer that sits between "I have a shape" and "I can simulate physics on it.

Open source project to make working with geospatial data in python easier. Extends the datatypes used by pandas to allow spatial operations on geometric types. Built on top of Shapely, Fiona, and Pyproj. Use for reading and writing spatial formats (Shapefile, GeoJSON, GeoPackage, KML), performing spatial joins, coordinate system transformations (reprojecting), geometric analysis (buffers, centroids, convex hulls), thematic mapping (Choropleth maps), calculating spatial relationships (contains, overlaps, touches, within), working with OpenStreetMap data or satellite-derived vector data.

Dual skill for deploying scientific models. FastAPI provides a high-performance, asynchronous web framework for building APIs with automatic documentation. Streamlit enables rapid creation of interactive data applications and dashboards directly from Python scripts. Load when working with web APIs, model serving, REST endpoints, interactive dashboards, data visualization UIs, scientific app deployment, async web frameworks, Pydantic validation, uvicorn, or building production-ready scientific tools.

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An analytical in-process SQL database management system. Designed for fast analytical queries (OLAP). Highly interoperable with Python's data ecosystem (Pandas, NumPy, Arrow, Polars). Supports querying files (CSV, Parquet, JSON) directly without an ingestion step. Use for complex SQL queries on Pandas/Polars data, querying large Parquet/CSV files directly, joining data from different sources, analytical pipelines, local datasets too big for Excel, intermediate data storage and feature engineering for ML.

Causal inference framework for answering "does X cause Y?" beyond correlation. DoWhy (Microsoft Research) provides the identify-estimate-refute loop: define a causal graph (DAG), identify the causal effect using backdoor/frontdoor/instrumental variable criteria, estimate treatment effects with multiple estimators, and validate results with automated refutation tests. Use when: distinguishing causation from correlation, estimating treatment effects (ATE, ATT, CATE), designing and analyzing A/B tests with confounders, using instrumental variables, performing counterfactual reasoning ("what would have happened if..."), validating causal claims with sensitivity analysis, working with observational data where randomization is impossible, or any analysis where the question is "what is the CAUSAL effect of X on Y" rather than just "how do X and Y relate?

A flexible library for parallel computing in Python. It scales Python libraries like NumPy, pandas, and scikit-learn to multi-core systems or distributed clusters. Features lazy evaluation and task scheduling for data that exceeds RAM capacity. Use for out-of-core computing, parallel processing, distributed computing, large-scale data analysis, dask.array, dask.dataframe, dask.delayed, dask.bag, task scheduling, lazy evaluation, and scaling beyond memory limits.

Advanced sub-skill for Dask focused on distributed system performance, memory management, and task graph optimization. Covers cluster tuning, efficient serialization, data skew mitigation, and dashboard-driven debugging.

Constraints-Based Reconstruction and Analysis for Python. Used for modeling large-scale metabolic networks in microorganisms.

A Python package useful for chemistry (mainly physical/analytical/inorganic chemistry). Features include balancing chemical reactions, chemical kinetics (ODE integration), chemical equilibria, ionic strength calculations, and unit handling. Use when working with chemical equations, reaction balancing, kinetic modeling, equilibrium calculations, speciation, pH calculations, ionic strength, activity coefficients, or chemical formula parsing.

Comprehensive guide for Biopython - the premier Python library for computational biology and bioinformatics. Use for DNA/RNA/protein sequence analysis, file I/O (FASTA, FASTQ, GenBank, PDB), sequence alignment, BLAST searches, phylogenetic analysis, structure analysis, and NCBI database access.

The core library for Astronomy and Astrophysics in Python. Provides data structures for coordinates, time, units, FITS files, and cosmological models. Essential for observational data reduction and theoretical astrophysics. Use when working with astronomical coordinates (RA/Dec), physical units, FITS files, time scales, WCS, cosmology, or astronomical tables.

Atomic Simulation Environment - a set of tools for setting up, manipulating, running, visualizing, and analyzing atomistic simulations. Acts as a universal interface between Python and numerous quantum chemical and molecular dynamics codes. Use for building atomic structures, geometry optimization, molecular dynamics simulations, transition state searches (NEB), file format conversion (CIF, XYZ, POSCAR, PDB), electronic property calculations (DOS, band structures), and automating simulation workflows with DFT/MD codes like VASP, GPAW, Quantum ESPRESSO, LAMMPS.

Manages git worktrees for parallel feature development. This skill should be used when starting multiple features at once, or to list, switch between, and merge existing worktrees.