Skill UI

GeoPandas extends pandas for geospatial vector data, enabling reading/writing shapefiles, GeoJSON, GeoPackage and PostGIS, performing CRS management, spatial joins, overlays, buffering, area/distance calculations, and producing static or interactive choropleth maps.

PathML is a Python toolkit for computational pathology workflows, loading 160+ slide formats, preprocessing H&E images, constructing spatial graphs, training HoVer-Net/HACTNet models, and analyzing multiplex imaging for ML-ready pathology 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.

GeoMaster is a comprehensive geospatial science skill that guides remote sensing, GIS, spatial analysis, and machine learning workflows for Earth observation and planetary data projects, covering satellite imagery, vector/raster math, point clouds, terrain, hydrology, and cloud-native data stacks.

Programmatically query the CZ CELLxGENE Census for versioned single-cell and spatial transcriptomics data, including population-scale cell metadata, gene expression slices, summary counts, source H5AD URIs, embeddings, and reference atlas comparisons.

Provides geospatial research workflows for remote sensing, vector and raster GIS, spatial statistics, terrain and network analysis, and Earth-observation machine learning across Python, R, Julia, and other languages.

Guidance and local audit tools for Python workflows using GeoPandas GeoSeries, GeoDataFrame, spatial operations, CRS handling, and vector-data I/O. Includes safety gates for provenance, geometry validity, and privacy-aware geodata sharing.

LaminDB is an open-source lineage-native lakehouse designed for biological datasets and models. It enables queryable, traceable, and validated data management across various storage formats like S3 and Postgres. Users can track computational workflows, curate data with ontologies via Bionty, and ensure FAIR compliance. Ideal for managing scRNA-seq, spatial transcriptomics, and integrating ML pipelines while maintaining reproducibility and collaboration.

Build local, research-only computational pathology workflows with PathML: load and tile whole-slide images, run preprocessing and QC pipelines, manage h5path data, quantify multiplex images, construct spatial graphs, and plan bounded model inference.

A comprehensive guide outlining rigorous data analysis and reporting standards for geographic manuscripts. It covers spatial statistics, remote sensing accuracy, and qualitative interpretation, emphasizing spatial honesty, quantifying uncertainty, and ensuring results are robust across different scales and units.

A comprehensive guide for strengthening the methodological design of advanced geographical manuscripts. It covers various paradigms including spatial/quantitative GIScience, remote sensing, qualitative human geography, and mixed methods. Key advice areas include rigorously addressing spatial dependence (MAUP), validating measurements, justifying case selection, and ensuring holistic integration of diverse data sources.

A comprehensive set of guidelines for generating maps, figures, and tables for academic journals. This guide emphasizes cartographic standards, statistical rigor, and maximizing the visual argument, ensuring that all exhibits are reproducible, legible in print, and adhere to strict word count limitations. It covers everything from projection choice and data normalization to model plot interpretation and handling spatial uncertainty.