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 AlterLab-IEU/AlterLab-Academic-Skills --skill alterlab-cellxgenegit clone --depth 1 https://github.com/AlterLab-IEU/AlterLab-Academic-SkillsWrote 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/alterlab-ieu/alterlab-academic-skills/alterlab-cellxgene)<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.
<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>- NVIDIA SkillSpector pass
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 | $0.00101 | $0.01043 |
| Opus 5 | $0.00051 | $0.00522 |
| Sonnet 5 | $0.00020 | $0.00209 |
| Haiku 4.5 | $0.00010 | $0.00104 |
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.
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
- Open the Census with a context manager; pin
census_versionfor reproducibility. - Explore metadata first (
get_obs/ datasets summary) to understand what's available — always filteris_primary_data == Trueto avoid duplicate cells. - Estimate query size before loading expression. < 100k cells →
get_anndata()(in-memory); larger →axis_query()out-of-core iteration. - Query expression with
obs_value_filter(cells) andvar_value_filter(genes); select only theobs_column_namesyou need. - 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",
)
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.
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.
- 11d ago First seen · 74 lines · 101 tokens per session scan A a118c237f0b8
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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