Scientific Agent Skills is a collection of reusable procedures that give AI agents capabilities for scientific research across areas such as biology, chemistry, medicine, and drug discovery. It is used by researchers and by people building AI scientist workflows with compatible coding agents. The catalogue contains many of the project's skills and supporting instructions.
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 K-Dense-AI/scientific-agent-skills --skill cellxgene-censusgit clone --depth 1 https://github.com/K-Dense-AI/scientific-agent-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/k-dense-ai/scientific-agent-skills/cellxgene-census)<a href="https://agentmods.dev/skills/k-dense-ai/scientific-agent-skills/cellxgene-census"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/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/k-dense-ai/scientific-agent-skills/cellxgene-census"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/cellxgene-census.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- 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 | $0.00097 | $0.03251 |
| Opus 5.5 | $0.00039 | $0.01300 |
| Sonnet 5.5 | $0.00019 | $0.00650 |
| Haiku 4.5 | $0.00010 | $0.00325 |
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 14d 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.
Copies of this mod
4 near-identical copies found in the catalogue:
- cellxgene-census — 92% identical, 96 lines differ
- cellxgene-census — 86% identical, 409 lines differ
- cellxgene-census — 86% identical, 409 lines differ
- cellxgene-census — 86% identical, 409 lines differ
How it starts
The opening of the file, as written. The whole thing — 335 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==1.18.0"
For spatial workflows:
uv pip install "cellxgene-census[spatial]==1.18.0"
For PyTorch model training, use TileDB-SOMA-ML. The old cellxgene_census.experimental.ml loaders are absent from 1.18.0:
uv pip install "cellxgene-census==1.18.0" 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 — TileDB-SOMA-ML 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
4 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.
- 14d ago Changed · +18 lines 123b66d6701c
- 15d ago Changed · +16 lines 23e44c7d7799
- 1mo ago Changed · +17 lines 3e7cee2dc141
- 1mo ago First seen · 284 lines · 97 tokens per session scan A 9ce161d06d4c
cellxgene-census is a skill published in the GitHub repository K-Dense-AI/scientific-agent-skills (50,453 stars, last pushed today), licensed MIT. It adds 97 tokens to every session and 3,251 once invoked, about $0.0004 per session on Opus 5.5. 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.
Other skills, from other repositories
discovery-toolbox
A routed repertoire of 90 scientific thinking operators for biological research agents - visual reasoning, detectability and information budgets, search reframing, causal identification, competing explanations, observation and selection processes, pipeline artifact diagnosis, effort allocation, and confirmation…
discovery-director
Operate as a research director making original discoveries from a given biological question and dataset. Use when the task is open-ended scientific research, exploring omics or experimental data for findings, hypothesis generation and testing, screening a large candidate space of genes, variants, features or…
bio-interdomain-hgt
Detect and polarize interdomain horizontal gene transfer with reciprocal hits, genomic context, and gene trees. Use for virus-host gene exchange, endogenous viral elements, or donor direction.
csag-extraction
Extract a Conditional Scientific Argumentation Graph and grounded Q&A from a manuscript. Use when representing assertions, contexts, evidence links, and inference steps in machine-readable form.
notebooks
Author, run and convert marimo or Jupyter notebooks. Use when delivering an analysis notebook executed end to end with figures embedded.
bio-annotation
Annotate proteins with functions, domains, and homology-based taxonomy. Use when assigning functional or taxonomic labels to proteins from genomes, contigs, or MAGs.