Community superstitions - unverified observations from pattern development. Use when encountering undocumented edge cases or framework quirks not in official docs. Verified knowledge should be upstreamed to labs docs.

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Industry-standard gradient boosting libraries for tabular data and structured datasets. XGBoost and LightGBM excel at classification and regression tasks on tables, CSVs, and databases. Use when working with tabular machine learning, gradient boosting trees, Kaggle competitions, feature importance analysis, hyperparameter tuning, or when you need state-of-the-art performance on structured data.

N-dimensional labeled arrays and datasets in Python. Built on top of NumPy and Dask. It introduces labels in the form of dimensions, coordinates, and attributes on top of raw NumPy-like arrays, making data analysis in physical sciences more intuitive and less error-prone. Use for working with multi-dimensional scientific data, NetCDF/GRIB/Zarr files, climate/weather/oceanographic datasets, remote sensing, geospatial imaging, large out-of-memory datasets with Dask, and labeled array operations.

State-of-the-art Machine Learning for PyTorch, TensorFlow, and JAX. Provides thousands of pretrained models to perform tasks on different modalities such as text, vision, and audio. The industry standard for Large Language Models (LLMs) and foundation models in science.

A fast, extensible progress bar for Python and CLI. Instantly makes your loops show a smart progress meter with ETA, iterations per second, and customizable statistics. Minimal overhead. Use for monitoring long-running loops, simulations, data processing, ML training, file downloads, I/O operations, command-line tools, pandas operations, parallel tasks, and nested progress bars.

Comprehensive deep learning framework for building, training, and deploying neural networks. TensorFlow provides tf.keras high-level API for model construction, tf.data for efficient data pipelines, and tf.function for graph-mode optimization. Use when working with: neural network training and inference, image classification/detection/segmentation, NLP/text processing with embeddings or transformers, time series forecasting, generative models (VAE, GAN), transfer learning with pretrained models, custom training loops with GradientTape, GPU/TPU accelerated computation, or any deep learning task.

Comprehensive guide for SymPy - Python library for symbolic mathematics. Use for symbolic expressions, calculus (derivatives, integrals, limits, series), equation solving (algebraic, differential, systems), linear algebra, simplification, matrix operations, special functions, code generation, and mathematical proofs. Essential for analytical mathematics and computer algebra.

The community-developed free and open-source software package for solar physics. Provides tools for data search and download, coordinate transformations specific to solar physics, and powerful image processing through the Map object. Use when working with solar data, solar images (EUV, magnetograms, white light), solar coordinates (Helioprojective, Heliographic), Fido data search, solar time series, differential rotation, limb fitting, or multi-instrument solar analysis (AIA, HMI, GOES).

Advanced statistical modeling and hypothesis testing. Complementary to SciPy's stats module, it provides classes and functions for the estimation of many different statistical models, as well as for conducting statistical tests and statistical data exploration. Use for linear regression, GLM, time series analysis, ANOVA, survival analysis, causal inference, and statistical hypothesis testing. Load when working with OLS, WLS, logistic regression, Poisson regression, ARIMA, SARIMAX, statistical diagnostics, p-values, confidence intervals, or R-style statistical analysis.

Natural Language Processing for text analysis, corpus linguistics, and production NLP pipelines. spaCy provides fast production-grade tokenization, POS tagging, NER, dependency parsing, and custom model training. NLTK provides classical corpus linguistics, linguistic analysis, VADER sentiment, collocation analysis, and access to standard linguistic corpora. Use when: processing and analyzing text data, extracting named entities (people, orgs, locations, dates), dependency parsing and syntactic analysis, building text classification pipelines, performing corpus-level linguistic analysis (frequency, collocations, readability), sentiment analysis, lemmatization and stemming, working with multilingual text, training custom NER or text classifiers, or any task requiring structured understanding of natural language beyond simple string operations.

Time series machine learning layer (Tier 1): integration of **sktime** and **tsfresh** for building production-grade pipelines that transform raw time series into tabular feature representations suitable for classical machine-learning models. *sktime* provides a unified, sklearn-compatible interface for time-series data types, transformations, and pipelines, while *tsfresh* enables large-scale automated extraction of statistical, spectral, and autocorrelation features, with optional statistically grounded feature relevance selection (FRESH).

Advanced sub-skill for scikit-learn focused on model interpretability, feature importance, and diagnostic tools. Covers global and local explanations using built-in inspection tools and SHAP/LIME integrations.

Professional sub-skill for scikit-learn focused on robust pipeline architecture, custom estimator development, advanced feature engineering, and rigorous model validation. Covers Target Encoding, Nested Cross-Validation, and Production Deployment.

A process-based discrete-event simulation framework. Use for modeling queuing systems, supply chains, manufacturing processes, network simulation, project management, and any system where events occur at specific points in time. Load when working with discrete event simulation, process modeling, resource allocation, virtual time, simpy.Environment, simpy.Resource, or event-driven simulation.

Manipulation and analysis of planar geometric objects. Based on the widely deployed GEOS library. Provides data structures for points, curves, and surfaces, and standardized algorithms for geometric operations. Use for 2D geometry operations, spatial relationships, set-theoretic operations (intersection, union, difference), point-in-polygon queries, geometric calculations (area, distance, centroid), buffering, simplifying geometries, linear referencing, and cleaning invalid geometries. Essential for GIS operations, spatial analysis, and geometric computations.

A Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics. Great for exploring relationships between variables and visualizing distributions. Use for statistical data visualization, exploratory data analysis (EDA), relationship plots, distribution plots, categorical comparisons, regression visualization, heatmaps, cluster maps, and creating publication-quality statistical graphics from Pandas DataFrames.

Comprehensive guide for SciPy - the fundamental library for scientific and technical computing in Python. Use for integration, optimization, interpolation, linear algebra, signal processing, statistics, ODEs, Fourier transforms, and advanced scientific algorithms. Built on NumPy and essential for research and engineering.

Video processing library for scientists. Provides easy access to video files using FFmpeg, motion estimation algorithms, and video quality metrics. Built on NumPy and designed for high-performance research in computer vision and image sequence analysis. Use when working with video files, motion estimation, video quality assessment (VQA), FFmpeg, temporal image data, video codecs, YUV data, or scientific video recordings.

The industry standard library for machine learning in Python. Provides simple and efficient tools for predictive data analysis, covering classification, regression, clustering, dimensionality reduction, model selection, and preprocessing.

A collection of algorithms for image processing in Python. Built on NumPy, SciPy, and Cython. It focuses on scientific image analysis including segmentation, geometric transformations, color space manipulation, analysis, and filtering.

Library for bioinformatics and community ecology statistics. Provides data structures and algorithms for sequences, alignments, phylogenetics, and diversity analysis. Essential for microbiome research and ecological data science. Use for alpha/beta diversity metrics, ordination (PCoA), phylogenetic trees, sequence manipulation (DNA/RNA/Protein), distance matrices, PERMANOVA, and community ecology analysis.

Scalable toolkit for analyzing single-cell gene expression data. Built on top of Anndata, focusing on clustering, trajectory inference, and visualization.

Open-source cheminformatics and machine learning toolkit for drug discovery, molecular manipulation, and chemical property calculation. RDKit handles SMILES, molecular fingerprints, substructure searching, 3D conformer generation, pharmacophore modeling, and QSAR. Use when working with chemical structures, drug-like properties, molecular similarity, virtual screening, or computational chemistry workflows.