Skill UI

Transforms data into publication-ready figures with multi-panel layouts, precise annotations, colorblind-friendly palettes, and journal formatting using matplotlib, seaborn, and plotly plus export helpers for PDF/EPS/TIFF.

Toolkit for Python-based survival and time-to-event modeling with scikit-survival, supporting Cox models, ensemble learners, SVMs, and evaluation metrics for censored data.

Probabilistic deep-learning framework built on PyTorch for single-cell omics; handles batch correction, integration, multimodal and spatial data, differential expression, and transfer learning with unified APIs.

Seaborn is a powerful Python library for creating publication-quality statistical graphics. It is designed for dataset-oriented plotting, making it ideal for exploratory data analysis, multivariate analysis, and visualizing complex relationships, distributions, and comparisons with minimal code. It builds upon Matplotlib, offering high-level interfaces for functions like scatterplots, heatmaps, and pair plots.

SimPy is a powerful, process-based discrete-event simulation framework written in Python. It is designed for modeling complex systems where entities interact with shared resources and time-based events occur, such as manufacturing lines, service queues, network traffic, or logistics networks. Use this skill to analyze resource contention, simulate process optimization, conduct capacity planning, and model dynamic system behaviors.

Statsmodels is a comprehensive Python library dedicated to advanced statistical modeling and econometrics. It provides tools for rigorous statistical inference, including Ordinary Least Squares (OLS), Generalized Linear Models (GLM), mixed-effects models, and time series analysis (ARIMA). Use this skill for detailed diagnostics, coefficient estimation, hypothesis testing, and producing publication-ready statistical reports in fields like econometrics and biostatistics.

TileDB-VCF lets you ingest, store, query, and export large genomic VCF/BCF datasets using TileDB’s sparse arrays, supporting incremental sample growth, parallel regions queries, and cloud-local interoperability for population genomics workflows.

TorchDrug is a PyTorch-native toolbox for building graph neural networks tailored to molecules, proteins, and biomedical knowledge graphs, covering property prediction, generation, retrosynthesis, and knowledge graph reasoning with curated datasets and models.

Provides open REST access to the U.S. Treasury Fiscal Data API so analysts can pull national debt, spending, revenue, interest rates, exchange rates, auctions, and savings bond statistics without an API key; useful for financial dashboards, debt tracking, and research involving federal fiscal metrics.

Vaex is a high-performance Python library for lazy out-of-core DataFrames that process billions of rows without RAM. Use it to analyze massive CSV/HDF5/Arrow/Parquet tables, compute fast statistics, visualize big data, and build scikit-learn pipelines.

Zarr is a Python toolkit for chunked N-D array storage with compression, sharding, and native integrations with NumPy/Dask/Xarray, ideal for parallel I/O and scalable cloud pipelines.

A comprehensive toolkit designed to track and analyze content performance, engagement metrics, and Return on Investment (ROI) across major social media platforms. By utilizing specialized Apify Actors, this skill extracts deep insights from Instagram, Facebook, YouTube, TikTok, and more, helping users identify high-performing content, monitor growth, and measure marketing campaign effectiveness.