alterlab-cellxgene

alterlab-cellxgene is a skill for Claude Code from AlterLab-IEU/AlterLab-Academic-Skills. It costs 101 tokens per session (1,043 once invoked), scanned A, original, MIT.

A programmatic interface to the CZ CELLxGENE Census, a versioned public atlas of standardized single-cell gene-expression data from humans and mice. You can select cells by attributes such as tissue, disease, or cell type and return the data as AnnData.

In plain words
What is it for?
Use it to query expression data and metadata, explore cell populations, calculate statistics across many datasets, train models, and combine reference data with Scanpy or other single-cell tools.
Why use it?
It avoids downloading and organizing a large reference atlas by hand. It provides a consistent way to compare datasets or obtain reference cells for analysis and machine-learning work.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter.

Part of the alterlab-bioinformatics plugin — 38 skills shipped together

Good fit Use it to query expression data and metadata, explore cell populations, calculate statistics across many datasets, train models, and combine reference data with Scanpy or other single-cell tools.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/alterlab-ieu/alterlab-academic-skills/alterlab-cellxgene
Install

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.

Any agent
npx skills add AlterLab-IEU/AlterLab-Academic-Skills --skill alterlab-cellxgene
Clone the repo
git clone --depth 1 https://github.com/AlterLab-IEU/AlterLab-Academic-Skills

Made for: Claude Code.

Or install alterlab-bioinformatics, the plugin that ships this one along with the rest of its 38 skills.

Wrote 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.

agentmods badge for alterlab-cellxgene

README.md
[![agentmods](https://agentmods.dev/badge/skills/alterlab-ieu/alterlab-academic-skills/alterlab-cellxgene/github.svg)](https://agentmods.dev/skills/alterlab-ieu/alterlab-academic-skills/alterlab-cellxgene)
Your own site
<a href="https://agentmods.dev/skills/alterlab-ieu/alterlab-academic-skills/alterlab-cellxgene"><img src="https://agentmods.dev/badge/skills/alterlab-ieu/alterlab-academic-skills/alterlab-cellxgene/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.

agentmods 80×15 button for alterlab-cellxgene

Your own site · 80×15
<a href="https://agentmods.dev/skills/alterlab-ieu/alterlab-academic-skills/alterlab-cellxgene"><img src="https://agentmods.dev/badge/skills/alterlab-ieu/alterlab-academic-skills/alterlab-cellxgene.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 101 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,043 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00101 $0.01043
Opus 5 $0.00051 $0.00522
Sonnet 5 $0.00020 $0.00209
Haiku 4.5 $0.00010 $0.00104

Measured 11d ago against content hash a118c237f0b8, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

alterlab-cellxgene 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 11d 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.

skills/bioinformatics/alterlab-cellxgene/SKILL.md · 74 lines

How it starts

The opening of the file, as written. The whole thing — 74 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, versioned access to standardized single-cell genomics data from CZ CELLxGENE Discover. It contains 61+ million cells (human and mouse) with standardized metadata (cell types, tissues, diseases, donors), raw gene expression matrices, pre-calculated embeddings, and integration with PyTorch, scanpy, and other analysis tools.

When to Use This Skill

Use this skill 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

For analyzing your own dataset (not the reference atlas), use scanpy or scvi-tools instead.

Installation

uv pip install cellxgene-census
# For PyTorch ML workflows (loaders moved out of cellxgene-census):
uv pip install tiledbsoma-ml

Core Workflow

  1. Open the Census with a context manager; pin census_version for reproducibility.
  2. Explore metadata first (get_obs / datasets summary) to understand what's available — always filter is_primary_data == True to avoid duplicate cells.
  3. Estimate query size before loading expression. < 100k cells → get_anndata() (in-memory); larger → axis_query() out-of-core iteration.
  4. Query expression with obs_value_filter (cells) and var_value_filter (genes); select only the obs_column_names you need.
  5. Downstream: hand the returned AnnData to scanpy, or stream batches into a PyTorch dataloader for ML.

Minimal skeleton:

import cellxgene_census

with cellxgene_census.open_soma(census_version="2023-07-25") as census:
    adata = cellxgene_census.get_anndata(
        census=census,
        organism="Homo sapiens",
        obs_value_filter="cell_type == 'B cell' and tissue_general == 'lung' and is_primary_data == True",
    )

Read the full file on GitHub · 74 lines

Files

What ships with it

6 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.

Changes

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.

  1. 11d ago First seen · 74 lines · 101 tokens per session scan A a118c237f0b8

Subscribe to this mod's changes

alterlab-cellxgene is a skill published in the GitHub repository AlterLab-IEU/AlterLab-Academic-Skills (66 stars, last pushed 6d ago), licensed MIT. It adds 101 tokens to every session and 1,043 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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