Skill UI

Python package for handling annotated data matrices, storing experimental measurements with observation and variable metadata. Use for single-cell genomics workflows, .h5ad/zarr file I/O, concatenation, and scverse ecosystem integration.

Core Python library for astronomy and astrophysics workflows, covering units, coordinates, FITS I/O, tables, time systems, WCS, and cosmology. Use when implementing or debugging astronomical data analysis code with Astropy.

This skill provides programmatic access to the CZ CELLxGENE Census, a comprehensive collection of versioned public single-cell and spatial transcriptomics data. It enables efficient querying of population-scale cell metadata, gene expression slices, and embeddings without downloading entire datasets. Suitable for analyzing human, mouse, and other organism data across tissues and diseases. Integrates with Scanpy, AnnData, and TileDB-SOMA for large-scale analysis and machine learning workflows.

Scale pandas and NumPy workflows beyond RAM using Dask for parallel and distributed computing. Ideal for processing large multi-file datasets, out-of-core DataFrames, big arrays, and unstructured data, from laptops to clusters.

Pythonic RDKit wrapper for molecular cheminformatics: parse and standardize SMILES, compute descriptors and fingerprints, cluster compounds, generate 3D conformers, and run parallel drug-discovery workflows.

DeepTools is a comprehensive Python command-line toolkit for processing and analyzing high-throughput sequencing data. It supports quality control, normalization, sample comparison, and generating publication-quality visualizations for ChIP-seq, RNA-seq, and ATAC-seq experiments. Key features include converting BAM to bigWig, assessing sequencing depth, creating heatmaps around genomic features, and performing correlation analysis. Ideal for bioinformatics workflows requiring robust genomic data processing and visualization.

Build and operate reproducible genomics workloads on DNAnexus using dx CLI, dxpy, apps/applets, native workflows, and Nextflow. Supports data transfers, app development, execution monitoring, and project automation for genomic data analysis pipelines.

Geniml validates genomic interval workflows, BED files, and model compatibility for ML/statistical analysis. Plan Region2Vec/scEmbed runs, inspect tokenizers, and assess consensus universes locally without network requests. Requires Python 3.10+ and uv for reproducible bioinformatics ML pipelines.

This comprehensive geospatial science skill covers remote sensing, GIS, spatial analysis, and machine learning for Earth observation. It supports satellite imagery processing, vector and raster data operations, spatial statistics, and point cloud processing. Users can utilize cloud-native workflows and 8 programming languages with 500+ code examples. Ideal for terrain analysis, hydrological modeling, and marine spatial analysis tasks.

Guidance and local audit tooling for Python workflows using GeoPandas GeoSeries, GeoDataFrame, spatial operations, and vector data I/O, covering reproducible environments, CRS and antimeridian rules, correctness gates, and privacy safeguards.

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 or audit reproducible research workflows with NeuroKit2 for physiological time-series: preprocessing, event/interval analysis, multimodal alignment, variability and complexity on ECG, RSP and EDA signals.