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 agentmods add skills/tiangzlab/omicsclaw/multi-omics-integrationnpx skills add TianGzlab/OmicsClaw --skill multi-omics-integrationgit clone --depth 1 https://github.com/TianGzlab/OmicsClawWrote 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/tiangzlab/omicsclaw/multi-omics-integration)<a href="https://agentmods.dev/skills/tiangzlab/omicsclaw/multi-omics-integration"><img src="https://agentmods.dev/badge/skills/tiangzlab/omicsclaw/multi-omics-integration.svg" alt="Measured on agentmods" 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 | $0.00009 | $0.03143 |
| Opus 5 | $0.00005 | $0.01571 |
| Sonnet 5 | $0.00002 | $0.00629 |
| Haiku 4.5 | $0.00001 | $0.00314 |
Grade A, and why
Multi-Omics Integration (MOFA+) 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 5d 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 — 250 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Multi-Omics Integration (MOFA+)
Identify latent factors driving variation across 2+ omics layers using MOFA+ (Multi-Omics Factor Analysis). Decomposes multi-omics data into interpretable factors, each capturing shared or view-specific biological signal. Handles missing data across views natively.
When to Use This Skill
Use when you:
- ✅ Have 2+ omics layers measured on overlapping samples (RNA-seq + proteomics, methylation + mutations, etc.)
- ✅ Want to find shared sources of variation across omics (not just per-omics analysis)
- ✅ Need to identify which omics layers contribute to each source of variation
- ✅ Have incomplete data (not all samples measured in all views) — MOFA handles this
- ✅ Want factor scores for downstream patient stratification or survival analysis
Don't use for:
- ❌ Single omics data (use
bulk-rnaseq-counts-to-de-deseq2orbulk-omics-clustering) - ❌ Supervised prediction (use
lasso-biomarker-panelinstead) - ❌ Single-cell multi-modal (MOFA2 supports it, but consider
scrna-trajectory-inference) - ❌ Fewer than 10 samples per view
Runtime: ~5-8 minutes total (CLL example). First run adds ~1-3 min for Python environment setup.
Installation
# Bioconductor packages
if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager")
BiocManager::install(c("MOFA2", "MOFAdata", "ComplexHeatmap"))
# CRAN packages
install.packages(c("ggprism", "circlize", "reshape2", "RColorBrewer"))
| Package | Version | License | Commercial Use | Installation |
|---|---|---|---|---|
| MOFA2 | ≥1.12.0 | LGPL (≥3) | ✅ Permitted | BiocManager::install("MOFA2") |
| MOFAdata | ≥1.8.0 | Artistic-2.0 | ✅ Permitted | BiocManager::install("MOFAdata") (example data) |
| ComplexHeatmap | ≥2.18.0 | MIT | ✅ Permitted | BiocManager::install("ComplexHeatmap") |
| ggprism | ≥1.0.3 | GPL (≥3) | ✅ Permitted | install.packages("ggprism") |
| circlize | ≥0.4.15 | MIT | ✅ Permitted | install.packages("circlize") |
| reshape2 | ≥1.4.4 | MIT | ✅ Permitted | install.packages("reshape2") |
| RColorBrewer | ≥1.1 | Apache-2.0 | ✅ Permitted | install.packages("RColorBrewer") |
| rmarkdown | ≥2.25 | GPL-3 | ✅ Permitted | install.packages("rmarkdown") (optional, PDF) |
What ships with it
5 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.
- 5d ago First seen · 250 lines · 9 tokens per session scan A 30fee880cd21
Multi-Omics Integration (MOFA+) is a skill published in the GitHub repository TianGzlab/OmicsClaw (160 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 9 tokens to every session and 3,143 once invoked, about $0.0000 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.
Other skills, from other repositories
scanpy
Standard single-cell RNA-seq analysis pipeline. Use for QC, normalization, dimensionality reduction (PCA/UMAP/t-SNE), clustering, differential expression, and visualization. Best for exploratory scRNA-seq analysis with established workflows. For deep learning models use scvi-tools; for data format questions use…
cellxgene-census-query
Query CZ CELLxGENE Census (61M+ cells). Filter by cell type/tissue/disease, retrieve expression data, and integrate with scanpy/PyTorch for population-scale single-cell analysis. Use this skill when: (1) Querying single-cell expression data by cell type, tissue, or disease, (2) Exploring available single-cell datasets…
single-cell-scrna-seq-analysis-scanpy
Complete single-cell RNA-seq analysis workflow built on Scanpy and AnnData. Use this skill when: (1) Loading diverse single-cell data formats (10X, h5ad, CSV), (2) Performing quality control and filtering, (3) Normalization, dimensionality reduction, and clustering, (4) Marker gene identification and cell type…
single-cell-multi-omics-analysis-scvi
Probabilistic deep learning framework for single-cell multi-omics data analysis. Use this skill when: (1) Analyzing single-cell RNA-seq data with batch correction, (2) Integrating multi-modal data (CITE-seq, ATAC-seq, multi-omics), (3) Performing cell type annotation with scANVI, (4) Spatial transcriptomics…
differential-analysis
Find differentially expressed genes between conditions, regions, or cell types. Use when user wants to compare gene expression, find markers, or identify condition-specific changes. Triggers: "differential expression", "DEG", "marker genes", "compare conditions", "what genes differ", "find markers", "condition…
functional-analysis
Understand the biological meaning of gene sets through pathway and functional enrichment analysis. Use when user has gene lists (DEGs, markers, SVGs) and wants to know what pathways or functions they represent. Triggers: "pathway analysis", "enrichment", "GSEA", "GO terms", "what pathways", "biological function"…