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 GPTomics/bioSkills --skill de-visualizationgit clone --depth 1 https://github.com/GPTomics/bioSkillsWrote 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/gptomics/bioskills/de-visualization)<a href="https://agentmods.dev/skills/gptomics/bioskills/de-visualization"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/de-visualization/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/gptomics/bioskills/de-visualization"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/de-visualization.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 | $0.00171 | $0.05397 |
| Opus 5 | $0.00086 | $0.02698 |
| Sonnet 5 | $0.00034 | $0.01079 |
| Haiku 4.5 | $0.00017 | $0.00540 |
Grade A, and why
bio-differential-expression-de-visualization 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- bio-differential-expression-de-visualization — 95% identical, 12 lines differ
How it starts
The opening of the file, as written. The whole thing — 356 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Version Compatibility
Reference examples tested with: DESeq2 1.42+, edgeR 4.0+, limma 3.58+, ggplot2 3.5+, pheatmap 1.0+, RColorBrewer 1.1+, ggrepel 0.9+, EnhancedVolcano 1.20+, matrixStats 1.2+
Before using code patterns, verify installed versions match. If versions differ:
- R:
packageVersion('<pkg>')then?function_nameto verify parameters
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
DE Visualization
"Make the standard DE figure panel" -> Use built-in functions or thin wrappers to produce diagnostic plots (dispersion, p-value histogram, PCA, sample distance) and result plots (MA, volcano, heatmap of top DE genes, per-gene counts), interpreted as diagnostics of the underlying model.
Scope
This skill covers DE-specific built-in plots and immediate wrappers. For richer customization:
- Custom volcano/MA with apeglm-shrunken LFC and ggrepel labelling ->
data-visualization/volcano-and-ma-plots - PCA / UMAP / t-SNE customization ->
data-visualization/dimensionality-reduction-plots - Heatmap customization and ComplexHeatmap recipes ->
data-visualization/heatmaps-clustering
The Single Most Important Modern Insight -- A volcano with shrunken LFC compresses the cloud, but the p-values are unchanged
lfcShrink() pulls noisy estimates toward zero. On the volcano, that pulls genes horizontally toward the center. But the y-axis (-log10(pvalue)) is the unshrunken Wald p-value -- shrinkage does NOT recompute p-values (Zhu, Ibrahim, Love 2019 Bioinformatics 35:2084). A naive reader sees fewer extreme dots and concludes "fewer genes are significant". Wrong: the same genes are significant; the effect sizes are smaller and more honest.
Always label the volcano x-axis "shrunken log2 fold change (apeglm)" and note the y-axis comes from the unshrunken Wald test. The whole point of the apeglm volcano is the honest effect-size axis; if a publication shows an unshrunken volcano, it is showing inflated effects from low-count noise.
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.
- 8d ago First seen · 356 lines · 171 tokens per session scan A a9b7be1033ef
bio-differential-expression-de-visualization is a skill published in the GitHub repository GPTomics/bioSkills (1,201 stars, last pushed 27d ago), licensed MIT. It adds 171 tokens to every session and 5,397 once invoked, about $0.0009 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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