Agent skill · research science · k-dense-ai
cellxgene-census
Query the CZ CELLxGENE Census programmatically for versioned public single-cell and spatial transcriptomics data. Use when you need population-scale cell metadata, gene expression slices, Census summary counts, source H5AD URIs/downloads, embeddings, spatial Census data, or reference atlas comparisons across organisms, tissues, diseases, assays, and cell types. For analyzing your own local single-cell data use scanpy, anndata, or scvi-tools.
Why this skill is useful
Provides specific commands and patterns for querying and analyzing large-scale single-cell transcriptomics data that the AI wouldn't reliably generate on its own.
What it needs
Requires cellxgene-census, spatialdata installed locally. About 6k tokens when loaded. Last updated 2026-08-03. 32,849 stars on the source repository.
What this skill does
CZ CELLxGENE Census Overview The CZ CELLxGENE Census provides programmatic access to a comprehensive, versioned collection of standardized single-cell and spatial transcriptomics data from CZ CELLxGENE Discover. This skill enables efficient querying and analysis of public Census releases without downloading whole datasets first. The Census includes: 217+ million total cells and 125+ million unique cells in the 2025-11-08 stable LTS release 1,845 datasets in the 2025-11-08 stable LTS release Human, mouse, marmoset, rhesus macaque, and chimpanzee data in the current schema Standardized metadata (cell types, tissues, diseases, donors) Raw gene expression matrices and source H5AD lookup/download helpers Pre-calculated summary counts, embeddings, and spatial data Integration with AnnData, Scanpy, TileDB-SOMA, TileDB-SOMA-ML, and other analysis tools When to Use This Skill This skill should be used when: Querying single-cell expression data by cell type, tissue, or disease Exploring available single-cell datasets and metadata Training machine learning models on single-cell data Performing large-scale cross-dataset analyses Integrating Census data with scanpy or other analysis frameworks Computing statistics across millions of cells Accessing pre-calculated embeddings or model predictions Installation and Setup Install the Census API: For spatial workflows: For PyTorch model training, use TileDB-SOMA-ML. The old cellxgenecensus.experimental.ml loaders are deprecated: Core Workflow Patterns Eight patterns, each with code, are in references/coreworkflowpatterns.md: 1. Opening the Census — always pin censusversion so an analysis stays reproducible. 2. Exploring Census information — available datasets, cell counts, and summary tables. 3. Querying expression data — small to medium scale into an AnnData. 4. Large-scale queries — out-of-core processing when the slice will not fit in memory. 5. Machine learning with PyTorch — the Census data loaders. 6. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills k-dense-ai/cellxgene-census