Data & Analytics

Use when cleaning clinical trial data, preparing data for FDA/EMA submission, standardizing SDTM datasets, handling missing values in clinical studies, detecting outliers in lab results, or converting raw CRF data to CDISC format. Cleans and standardizes clinical trial data for regulatory compliance with audit trails.

Use when cleaning clinical trial data, preparing data for FDA/EMA submission, standardizing SDTM datasets, handling missing values in clinical studies, detecting outliers in lab results, or converting raw CRF data to CDISC format. Cleans and standardizes clinical trial data for regulatory compliance with audit trails.

AI-optimized web search, image search and content extraction via BytePlus InfoQuest API. Use this skill when you need to gather concise and up-to-date information from the web, find images, or extract clean content from specific URLs.

Extract document structure, text, tables, and figures from documents using Azure Content Understanding prebuilt-layout analyzer. Converts PDF, images, Office docs into Markdown and structured JSON. Use when asked to extract text from documents, convert PDF/images to markdown, perform OCR, analyze document layout, or extract tables from files. Requires AZURE_CU_ENDPOINT and AZURE_CU_API_KEY environment variables.

Automated data analysis tool - Input CSV/Excel data and automatically generate comprehensive analysis reports. Includes data cleaning, statistical description, visualization, correlation analysis, and regression analysis. Ideal for rapid data analysis needs.

You are a data scientist with expertise in statistical analysis, machine learning, data visualization, and experimental design. Use when: statistical analysis and hypothesis testing, machine learning model development and evaluation, data visualization and storytelling, experimental design and a/b testing, feature engineering and selection.

Guides users through the process of preparing datasets and fine-tuning local Large Language Models (LLMs) using techniques like LoRA and QLoRA.

Analyzes time-series data for patterns, trends, seasonality, and anomalies, with forecasting using statistical and machine learning methods.

Guides ML experiment logging, versioning, and reproducibility using tools like MLflow, Weights & Biases, and DVC for systematic model development.

Transforms raw data into compelling visual narratives using Python or R, focusing on clarity, insight, and aesthetic presentation.

Expert data analyst skilled in data exploration, statistical analysis, visualization, SQL queries, and deriving actionable insights from datasets. Use when analyzing data, creating reports, or building data pipelines.

Upscale and enhance image resolution using AI. Use when the user requests "Upscale image", "Enhance resolution", "Make image bigger", "Increase quality", or similar upscaling tasks.

Content experimentation and A/B testing guidance covering experiment design, hypotheses, metrics, sample size, statistical foundations, CMS-managed variants, and common analysis pitfalls. Use this skill when planning experiments, setting up variants, choosing success metrics, interpreting statistical results, or building experimentation workflows in a CMS or frontend stack.

Comprehensive geospatial science skill covering remote sensing, GIS, spatial analysis, machine learning for earth observation, and 30+ scientific domains. Supports satellite imagery processing (Sentinel, Landsat, MODIS, SAR, hyperspectral), vector and raster data operations, spatial statistics, point cloud processing, network analysis, and 7 programming languages (Python, R, Julia, JavaScript, C++, Java, Go) with 500+ code examples. Use for remote sensing workflows, GIS analysis, spatial ML, ...

Organizes your scattered files into logical folders by project, type, and purpose

Use when analyzing business processes, gathering requirements from stakeholders, or identifying process improvement opportunities to drive operational efficiency and measurable business value.

A dashboard requirements document specifies what questions a dashboard should answer, what metrics it displays, and how data should be visualized. Clear requirements help data teams build dashboards that actually inform decisions rather than just displaying numbers.

A dashboard requirements document specifies what questions a dashboard should answer, what metrics it displays, and how data should be visualized. Clear requirements help data teams build dashboards that actually inform decisions rather than just displaying numbers.

Track ML experiments with automatic logging, visualize training in real-time, optimize hyperparameters with sweeps, and manage model registry with W&B - collaborative MLOps platform

Use this skill for processing and analyzing large tabular datasets (billions of rows) that exceed available RAM. Vaex excels at out-of-core DataFrame operations, lazy evaluation, fast aggregations, efficient visualization of big data, and machine learning on large datasets. Apply when users need to work with large CSV/HDF5/Arrow/Parquet files, perform fast statistics on massive datasets, create visualizations of big data, or build ML pipelines that do not fit in memory.

Query the U.S. Treasury Fiscal Data API for federal financial data including national debt, government spending, revenue, interest rates, exchange rates, and savings bonds. Access 54 datasets and 182 data tables with no API key required. Use when working with U.S. federal fiscal data, national debt tracking (Debt to the Penny), Daily Treasury Statements, Monthly Treasury Statements, Treasury securities auctions, interest rates on Treasury securities, foreign exchange rates, savings bonds, or ...

UMAP dimensionality reduction. Fast nonlinear manifold learning for 2D/3D visualization, clustering preprocessing (HDBSCAN), supervised/parametric UMAP, for high-dimensional data.

Provides guidance for PyTorch-native agentic RL using torchforge, Meta's library separating infra from algorithms. Use when you want clean RL abstractions, easy algorithm experimentation, or scalable training with Monarch and TorchTitan.

Graph Neural Networks (PyG). Node/graph classification, link prediction, GCN, GAT, GraphSAGE, heterogeneous graphs, molecular property prediction, for geometric deep learning.