Skill UI

Provides a Python client for querying the Data Commons knowledge graph so teams can resolve entities, fetch statistical observations, and explore public demographic, economic, and health datasets with optional Pandas support.

Query and download the NCI Imaging Data Commons public cancer imaging datasets via idc-index, filter by metadata, fetch DICOM files, visualize series, and respect licensing details for AI training or research.

Conduct rigorous, multi-database literature reviews with systematic workflows, verified citations in styles like APA or Nature, and AI-enhanced schematics for publication-ready markdown and PDF outputs in biomedical and technical domains.

Direct REST/API access to PubMed for constructing complex Boolean or MeSH queries, filtering by publication attributes, and automating literature retrieval or citation management via NCBI’s E-utilities.

Scanpy is a scalable Python toolkit for single-cell RNA-seq analyses, covering QC, normalization, dimensionality reduction, clustering, marker discovery, annotation, and publication-ready visualization across h5ad/10X/CSV datasets.

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.

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.

Build a production-ready, AI-powered data collection agent for any public source (job boards, prices, news, etc.). This agent automatically scrapes data, enriches it using free LLMs (like Gemini Flash), and stores the results in databases such as Notion, Sheets, or Supabase. It runs on a free schedule using GitHub Actions and learns from user feedback, making it ideal for continuous data monitoring and tracking.

This skill automates the generation of publication-quality figures and comparison tables for academic papers. It converts structured experimental data (e.g., JSON, CSV) into various plots, including line graphs, bar charts, scatter plots, and heatmaps, ensuring consistent styling suitable for LaTeX submissions. It handles the core data visualization needs for ML and research reports, greatly streamlining the process of creating professional results sections.

This skill provides institutional-depth equity analysis by combining public SEC EDGAR filings and real-time market data. It can generate structured, verdict-style memos, score stocks across four key pillars (Momentum, Stability, Financial Health, Upside), and perform side-by-side comparisons of two tickers, enabling data-driven investment decision support without requiring proprietary terminal access.

A comprehensive tool for generating publication-quality figures essential for Machine Learning and AI academic papers. It supports two distinct workflows: generating complex system architecture and workflow diagrams using AI (Gemini), and creating precise, data-driven visualizations (line charts, heatmaps, etc.) using standard libraries like matplotlib and seaborn. Use it when preparing any figure for a conference submission.

Provides REST API access to PubMed for building advanced Boolean/MeSH queries, filtering by publication details, and programmatically fetching citations through E-utilities, making it ideal for systematic reviews and automated literature workflows.