Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add agentlas-ai/agentlas-desktop --skill cellxgene-censusgit clone --depth 1 https://github.com/agentlas-ai/agentlas-desktopWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/agentlas-ai/agentlas-desktop/cellxgene-census)<a href="https://agentmods.dev/skills/agentlas-ai/agentlas-desktop/cellxgene-census"><img src="https://agentmods.dev/badge/skills/agentlas-ai/agentlas-desktop/cellxgene-census/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/agentlas-ai/agentlas-desktop/cellxgene-census"><img src="https://agentmods.dev/badge/skills/agentlas-ai/agentlas-desktop/cellxgene-census.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00097 | $0.02728 |
| Opus 5.5 | $0.00039 | $0.01091 |
| Sonnet 5.5 | $0.00019 | $0.00546 |
| Haiku 4.5 | $0.00010 | $0.00273 |
Grade A, and why
cellxgene-census scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
92% identical to cellxgene-census — 96 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 301 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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:
uv pip install "cellxgene-census==1.17.*"
For spatial workflows:
uv pip install "cellxgene-census[spatial]==1.17.*" "spatialdata[extra]>=0.2.5"
For PyTorch model training, use TileDB-SOMA-ML. The old cellxgene_census.experimental.ml loaders are deprecated:
uv pip install "cellxgene-census==1.17.*" tiledbsoma-ml
Core Workflow Patterns
Eight patterns, each with code, are in references/core_workflow_patterns.md:
- Opening the Census — always pin
census_versionso an analysis stays reproducible. - Exploring Census information — available datasets, cell counts, and summary tables.
- Querying expression data — small to medium scale into an
AnnData. - Large-scale queries — out-of-core processing when the slice will not fit in memory.
- Machine learning with PyTorch — the Census data loaders.
- Spatial Census data — accessing spatial assays.
- Integration with Scanpy — handing a Census slice to a standard Scanpy workflow.
- Multi-dataset integration — combining datasets and handling batch effects.
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 301 lines · 97 tokens per session scan A 3e7cee2dc141
cellxgene-census is a skill published in the GitHub repository agentlas-ai/agentlas-desktop (6 stars, last pushed today), licensed Apache-2.0. It adds 97 tokens to every session and 2,728 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 92% identical to cellxgene-census, differing in 96 lines, and is treated as a copy.
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