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/coexpression-networknpx skills add TianGzlab/OmicsClaw --skill coexpression-networkgit clone --depth 1 https://github.com/TianGzlab/OmicsClawWhat 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 | $0.00011 | $0.03871 |
| Opus 5 | $0.00005 | $0.01936 |
| Sonnet 5 | $0.00002 | $0.00774 |
| Haiku 4.5 | $0.00001 | $0.00387 |
Grade A, and why
Weighted Gene Co-expression Network Analysis (WGCNA) 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 3d 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 — 350 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Weighted Gene Co-expression Network Analysis (WGCNA)
Overview
Build weighted gene co-expression networks to identify modules of coordinately expressed genes and discover hub genes that may be key regulators. This workflow uses WGCNA (Weighted Gene Co-expression Network Analysis) to group genes into modules based on their expression patterns across samples, then correlates these modules with experimental conditions or traits.
Key Concept: Unlike single-gene analysis, WGCNA identifies groups of genes that behave similarly across samples, revealing biological pathways and potential regulatory relationships.
Use Cases:
- Identify gene modules associated with experimental conditions
- Discover hub genes (highly connected genes within modules)
- Find genes with similar expression patterns to known genes of interest
- Reduce dimensionality of gene expression data for downstream analysis
- Generate hypotheses about gene function based on co-expression
Default Prompt: "Build a co-expression network to identify gene modules and hub genes from my RNA-seq data"
When to Use This Skill
Use WGCNA when you want to:
- Identify gene modules associated with experimental conditions or phenotypes
- Discover hub genes that are highly connected within modules and may be key regulators
- Find co-expressed genes with similar expression patterns to known genes of interest
- Reduce dimensionality of large gene expression datasets for downstream analysis
- Generate hypotheses about gene function based on co-expression patterns
Requirements:
- ≥15 samples (20+ recommended for robust results)
- Normalized expression data (VST, rlog, TPM, or FPKM - NOT raw counts)
- 5,000-15,000 most variable genes
- Batch effects removed or corrected
Not suitable for:
- Small sample sizes (<15 samples) - consider alternative approaches
- Raw count data - normalize first using DESeq2 or similar
- Data with uncorrected batch effects - correct before WGCNA
What ships with it
18 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.
- references/parameter-tuning-guide.md 15 KB
- references/troubleshooting.md 11 KB
- references/wgcna-best-practices.md 15 KB
- references/wgcna-reference.md 15 KB
- scripts/build_network.R 1.2 KB
- scripts/correlate_modules_traits.R 2.7 KB
- scripts/export_wgcna_results.R 4.4 KB
- scripts/identify_hub_genes.R 1.9 KB
- scripts/load_example_data.R 7.3 KB
- scripts/module_enrichment.R 4.9 KB
- scripts/pick_soft_power.R 2.4 KB
- scripts/plot_all_wgcna.R 4.0 KB
- scripts/plot_eigengene_heatmap.R 1.8 KB
- scripts/plot_hub_genes.R 2.3 KB
- scripts/plot_module_dendrogram.R 1.4 KB
- scripts/plotting_helpers.R 2.9 KB
- scripts/prepare_wgcna_data.R 2.5 KB
- scripts/wgcna_workflow.R 3.5 KB
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.
- 3d ago First seen · 350 lines · 11 tokens per session scan A 6b75b56e72cc
Weighted Gene Co-expression Network Analysis (WGCNA) is a skill published in the GitHub repository TianGzlab/OmicsClaw (159 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 11 tokens to every session and 3,871 once invoked, about $0.0001 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
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…
decoupler
Use for any task involving the decoupler library — inferring biological activity/enrichment scores from omics data (bulk, single-cell, spatial). Triggers on estimating transcription factor (TF) activity, pathway activity, or gene-set enrichment from an AnnData/DataFrame; running ulm, mlm, ora, gsea, gsva, aucell…
bio-agent-skills-hub
Discover and invoke 1,676 deduplicated biomedical AI agent skills from the Awesome Bio Agent Skills repository (20 source repos, 15 categories). Use this skill as a router whenever a user needs a bioinformatics/biomedical task (genomics, transcriptomics, single-cell, proteomics, protein design, clinical, epigenomics…
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…
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…