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 AndyZhuang/Opentest --skill tooluniverse-single-cellgit clone --depth 1 https://github.com/AndyZhuang/OpentestWrote 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/andyzhuang/opentest/tooluniverse-single-cell)<a href="https://agentmods.dev/skills/andyzhuang/opentest/tooluniverse-single-cell"><img src="https://agentmods.dev/badge/skills/andyzhuang/opentest/tooluniverse-single-cell/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/andyzhuang/opentest/tooluniverse-single-cell"><img src="https://agentmods.dev/badge/skills/andyzhuang/opentest/tooluniverse-single-cell.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 | $0.00230 | $0.06416 |
| Opus 5 | $0.00115 | $0.03208 |
| Sonnet 5 | $0.00046 | $0.01283 |
| Haiku 4.5 | $0.00023 | $0.00642 |
Grade A, and why
tooluniverse-single-cell 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 8d 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 — 720 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Single-Cell Genomics and Expression Matrix Analysis
Comprehensive single-cell RNA-seq analysis and expression matrix processing using scanpy, anndata, scipy, and ToolUniverse. Designed for both full scRNA-seq workflows (raw counts to annotated cell types) and targeted expression-level analyses (per-cell-type DE, correlation, ANOVA, clustering).
IMPORTANT: This skill handles complex multi-workflow analysis. Most implementation details have been moved to references/ for progressive disclosure. This document focuses on high-level decision-making and workflow orchestration.
When to Use This Skill
Apply when users:
- Have scRNA-seq data (h5ad, 10X, CSV count matrices) and want analysis
- Ask about cell type identification, clustering, or annotation
- Need differential expression analysis by cell type or condition
- Want gene-expression correlation analysis (e.g., gene length vs expression by cell type)
- Ask about PCA, UMAP, t-SNE for expression data
- Need Leiden/Louvain clustering on expression matrices
- Want statistical comparisons between cell types (t-test, ANOVA, fold change)
- Ask about marker genes for cell populations
- Need batch correction (Harmony, combat)
- Want trajectory or pseudotime analysis
- Ask about cell-cell communication (ligand-receptor interactions)
- Questions mention "single-cell", "scRNA-seq", "cell type", "h5ad"
- Questions involve immune cell types (CD4, CD8, CD14, CD19, monocytes, etc.)
BixBench Coverage: 18+ questions across 5 projects (bix-22, bix-27, bix-31, bix-33, bix-36)
NOT for (use other skills instead):
- Bulk RNA-seq DESeq2 analysis only → Use
tooluniverse-rnaseq-deseq2 - Gene enrichment only (no expression data) → Use
tooluniverse-gene-enrichment - VCF/variant analysis → Use
tooluniverse-variant-analysis - Statistical modeling (regression, survival) → Use
tooluniverse-statistical-modeling
Core Principles
- Data-first approach - Load, inspect, and validate data before any analysis
- AnnData-centric - All data flows through anndata objects for consistency
- Cell type awareness - Many questions require per-cell-type subsetting and analysis
- Statistical rigor - Proper normalization, multiple testing correction, effect sizes
- Scanpy standard pipeline - Follow established best practices for scRNA-seq
- Flexible input - Handle h5ad, 10X, CSV/TSV, pre-processed and raw data
- Question-driven - Parse what the user is actually asking and extract the specific answer
- Enrichment integration - Chain DE results into GO/KEGG/Reactome enrichment when requested
- Large dataset support - Efficient handling of datasets with >100k cells
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.
- 8d ago First seen · 720 lines · 230 tokens per session scan A 19a14ac6d317
tooluniverse-single-cell is a skill published in the GitHub repository AndyZhuang/Opentest (22 stars, last pushed 6mo ago), licensed MIT. It adds 230 tokens to every session and 6,416 once invoked, about $0.0011 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 2026-09-03.
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