Skill UI

A comprehensive, end-to-end workflow for building product features guided by rigorous data insights. This skill guides users through the entire lifecycle, encompassing exploratory data analysis, forming measurable business hypotheses, designing statistical A/B tests, architecting data pipelines, and implementing robust backend/frontend instrumentation. It ensures that every feature iteration is validated by data and measured for impact.

This skill models the expertise of a senior data scientist, covering the entire data science lifecycle. It provides advanced capabilities in statistical modeling, machine learning (deep learning, XGBoost), predictive analytics, and business intelligence. From raw data exploration (EDA) to deploying production-ready models and creating actionable dashboards, it covers technical depth across Python, R, SQL, and cloud platforms, while applying domain knowledge in finance, marketing, and operations.

BioServices provides Python wrappers for 40+ bioinformatics services, allowing cross-database queries, identifier mapping, pathway exploration, and sequence or compound research within consistent workflows.

Automates detection and deep exploratory analysis across 200+ scientific data formats, assessing quality, extracting metadata, and recommending visualizations while generating markdown reports that guide downstream workflows and highlight structure and distributions.

Plotly is a Python graphing library for interactive, publication-quality visuals that bring hover, zoom, animation, and dashboard-ready exports to exploratory analysis and presentations.

QuTiP is a Python toolbox for simulating open and closed quantum systems, offering solvers for master equations, trajectory methods, visualization, and analysis tailored to quantum research, education, and exploratory physics studies.

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.

A comprehensive skill for managing the entire lifecycle of datasets on the Hugging Face Hub. It allows users to initialize, configure, and stream updates for datasets of various types (Chat, Text Classification, Tabular). Crucially, it extends data handling capabilities by enabling powerful SQL-based querying and transformation directly on Hub datasets using the DuckDB engine, facilitating advanced data exploration and pipeline creation.

Plotly is a powerful Python graphing library designed to create highly interactive, publication-quality visualizations. It supports over 40 chart types, including scatter plots, heatmaps, candlestick charts, and geographical maps. Ideal for building sophisticated dashboards, conducting deep exploratory data analysis, and creating rich, web-embeddable presentations with features like zooming, panning, and hover tooltips.

This skill performs comprehensive Exploratory Data Analysis (EDA) on specialized scientific datasets. It supports over 200 file formats across diverse domains, including chemistry (PDB), bioinformatics (FASTA, BAM), microscopy (TIFF), and spectroscopy. The system automatically detects the file type, assesses data quality and integrity, and generates detailed, format-specific markdown reports, providing deep insights and recommending downstream analyses.

Seaborn is a high-level Python data visualization library built on Matplotlib, designed for creating aesthetically pleasing and statistically rich graphs. It excels in exploring multivariate relationships, visualizing distributions (like box and violin plots), and handling complex dataset-oriented statistical graphics with minimal code. Ideal for data exploration and generating publication-quality figures.

A comprehensive tutor designed to build robust statistical thinking. It guides users through the entire data analysis lifecycle, from exploratory data visualization and descriptive statistics to complex hypothesis testing and regression modeling. The focus is on understanding the 'why' and 'how' of statistical logic using real datasets and practical problems, rather than just memorizing formulas. It is tool-agnostic, supporting concepts learned in Python, R, SPSS, or Stata.