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.
npx skills add K-Dense-AI/scientific-agent-skills --skill cellxgene-census --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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 - I
- Overview
- When to Use This Skill
- Installation and Setup
- Core Workflow Patterns
- Key Concepts and Best Practices
- Always Filter for Primary Data
- Specify Census Version for Reproducibility
- Estimate Query Size Before Loading
- Use tissuegeneral for Broader Groupings
- Select Only Needed Columns
- Check Dataset Presence for Gene-Specific Queries
- Two-Step Workflow: Explore Then Query
- Available Metadata Fields
- Cell Metadata (obs)
uv pip install "cellxgene-census==1.17.*" uv pip install "cellxgene-census[spatial]==1.17.*" "spatialdata[extra]>=0.2.5" uv pip install "cellxgene-census==1.17.*" tiledbsoma-ml
What does the cellxgene-census skill do?
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.
How do I install it?
Run `npx skills add K-Dense-AI/scientific-agent-skills --skill cellxgene-census --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From K-Dense-AI/scientific-agent-skills, a repository with 32,619 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.