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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.