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 zamushwani/biomedical-ai-skills --skill multiomics-integrationgit clone --depth 1 https://github.com/zamushwani/biomedical-ai-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/zamushwani/biomedical-ai-skills/multiomics-integration)<a href="https://agentmods.dev/skills/zamushwani/biomedical-ai-skills/multiomics-integration"><img src="https://agentmods.dev/badge/skills/zamushwani/biomedical-ai-skills/multiomics-integration/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/zamushwani/biomedical-ai-skills/multiomics-integration"><img src="https://agentmods.dev/badge/skills/zamushwani/biomedical-ai-skills/multiomics-integration.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 | $0.00000 | $0.04146 |
| Opus 5 | $0.00000 | $0.02073 |
| Sonnet 5 | $0.00000 | $0.00829 |
| Haiku 4.5 | $0.00000 | $0.00415 |
Grade A, and why
multiomics-integration 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 — 384 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Multi-Omics Integration
Joint analysis of two or more molecular layers on the same samples. Covers method selection, the preprocessing that decides whether integration works at all, MOFA+ factor analysis, similarity network fusion, joint clustering for cancer subtyping, supervised integration, and survival models built on integrated features.
When to Use This Skill
Activate when the user requests:
- Combining expression with methylation, mutation, CNV, or proteomics
- MOFA+, MOFA2, SNF, iClusterPlus, DIABLO, or mixOmics
- Cancer subtyping from more than one data type
- Latent factors or components shared across omics layers
- Survival modelling on integrated multi-omics features
- Deciding which integration method suits their design
Inputs
| Data Type | Form | Note |
|---|---|---|
| Expression | genes x samples, log-CPM or VST | usually the largest view |
| Methylation | probes/regions x samples, M-values | beta-values are heteroscedastic |
| CNV | segments or gene-level x samples | discrete-ish, often bimodal |
| Proteomics | proteins x samples | far fewer features, more missingness |
| Clinical | samples x variables | outcome for supervised methods |
Every view must be indexed by the same sample identifiers. Integration is a join before it is a model.
Environment
Versions verified 2026-08.
BiocManager::install("MOFA2") # 1.22.0 factor analysis
BiocManager::install("iClusterPlus") # 1.48.0 joint clustering
BiocManager::install("mixOmics") # 6.36.0 supervised (DIABLO)
install.packages("SNFtool") # 2.3.1 similarity fusion
INSTALL mixOmics FROM BIOCONDUCTOR, NOT CRAN.
CRAN mixOmics 6.3.2, published 2018-06-01
Bioconductor mixOmics 6.36.0
install.packages("mixOmics") silently gives you an eight-year-old build with
a different API. This is the same trap as PharmacoGx. Check which one you
actually loaded: packageVersion("mixOmics").
SNFtool 2.3.1 was last released 2021-06-11. It still works and the algorithm
is unchanged, but expect no fixes. Say so when you depend on it.
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.
- 11d ago First seen · 384 lines · 0 tokens per session scan A 3538485e1395
multiomics-integration is a skill published in the GitHub repository zamushwani/biomedical-ai-skills (1 stars, last pushed 12d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 4,146 tokens. 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 2026-08-31.
Other skills, from other repositories
cibersort-immune-infiltration-analysis
Use when estimating relative immune cell infiltration from a bulk expression matrix with a CIBERSORT-style nu-SVR deconvolution workflow based on an LM22 signature matrix, comparing one case group against one control group, and generating structured tables plus immune-fraction plots. NOT for single-cell RNA-seq…
cerna-analysis
Use when building a ceRNA regulatory network from a key gene list by combining bundled miRNA-mRNA and miRNA-lncRNA database files, with flat-file CSV exports and PDF visualization in a single output directory. NOT for: differential expression, single-cell analysis, enrichment analysis, or workflows without a key gene…
gene-protein-expression-matrix-normalization
Use when normalizing bulk gene or protein expression matrices with log2 transform, z-score standardization, or min-max scaling before downstream visualization or exploratory analysis. NOT for count-model normalization such as TPM/DESeq2 size factors, batch correction, or single-cell preprocessing.
torch-geometric
PyTorch Geometric (PyG) for graph neural networks — node/link/graph classification, message passing (GCN, GAT, GraphSAGE, GIN), heterogeneous graphs, neighbor sampling, and custom datasets. Use when working with torchgeometric, not for general NetworkX analytics or non-graph PyTorch models.
bids
Use this skill when working with Brain Imaging Data Structure (BIDS) datasets: organizing neuroscience and biomedical data (MRI, EEG, MEG, iEEG, PET, microscopy, NIRS, motion capture, EMG, MR spectroscopy, behavioral), querying BIDS layouts, validating compliance, converting DICOM to BIDS, writing metadata sidecars…
bulk-rnaseq
End-to-end bulk RNA-seq orchestrator — takes raw FASTQ reads through QC and trimming (FastQC, fastp/Trim Galore), alignment and quantification (STAR, Salmon, featureCounts), assembles a gene-level counts matrix, then hands off to differential expression (pydeseq2), pathway/GSEA enrichment (pathway-enrichment), and…