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 PKU-YuanGroup/OpenAI4S --skill bio-data-visualization-volcano-and-ma-plotsgit clone --depth 1 https://github.com/PKU-YuanGroup/OpenAI4SWrote 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/pku-yuangroup/openai4s/bio-data-visualization-volcano-and-ma-plots)<a href="https://agentmods.dev/skills/pku-yuangroup/openai4s/bio-data-visualization-volcano-and-ma-plots"><img src="https://agentmods.dev/badge/skills/pku-yuangroup/openai4s/bio-data-visualization-volcano-and-ma-plots/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/pku-yuangroup/openai4s/bio-data-visualization-volcano-and-ma-plots"><img src="https://agentmods.dev/badge/skills/pku-yuangroup/openai4s/bio-data-visualization-volcano-and-ma-plots.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 | $0.00106 | $0.05775 |
| Opus 5 | $0.00053 | $0.02887 |
| Sonnet 5 | $0.00021 | $0.01155 |
| Haiku 4.5 | $0.00011 | $0.00577 |
Grade A, and why
bio-data-visualization-volcano-and-ma-plots 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 9d 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.
This is a copy
97% identical to bio-data-visualization-volcano-and-ma-plots — 12 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 333 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+, EnhancedVolcano 1.20+, ggplot2 3.5+, ggrepel 0.9.5+, matplotlib 3.8+, numpy 1.26+, adjustText 1.1+, apeglm 1.28+, ashr 2.2+.
Before using code patterns, verify installed versions match. If versions differ:
- Python:
pip show <package>thenhelp(module.function)to check signatures - 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.
Volcano and MA Plots
"Plot differential-expression results" -> Place per-feature shrunken effect estimate on the x-axis and a significance measure (-log10 padj or -log10 p) on the y-axis. The decision space spans which effect estimate (raw vs shrunken), which significance measure (raw p vs adjusted vs s-value), how to encode categories (color by direction, not by gradient), how to label (top-N is rarely informative), and how to handle the tail (extreme p compresses the plot).
- R:
EnhancedVolcano::EnhancedVolcano(),ggplot2 + ggrepel,DESeq2::plotMA() - Python:
matplotlib.scatterwithadjustText,sanbomics.tools.volcano, custom seaborn
The Single Most Important Modern Insight -- Plot Shrunken LFC
Raw log2 fold change from DESeq2 / edgeR is the maximum-likelihood estimate and inflates wildly at low counts. A gene with 2 vs 0 reads gets log2FC = Inf; one with 4 vs 1 gets log2FC = 2 with a huge standard error. A naive volcano labels these as "top hits" purely because they have extreme estimates, not because they have a real signal.
DESeq2::lfcShrink() applies an empirical-Bayes prior to pull noisy low-count LFCs toward zero while leaving well-estimated genes essentially untouched. Since DESeq2 v1.28, the default shrinkage is type='apeglm' (Zhu, Ibrahim, Love 2019 Bioinformatics 35:2084) which uses a Cauchy prior — heavy enough to preserve large real effects, sharp enough at zero to deflate noise. Plot the shrunken LFC. The unshrunken LFC is a misleading effect estimate for ranking, labeling, or thresholding.
What ships with it
2 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.
- 9d ago First seen · 333 lines · 106 tokens per session scan A 9bbbb9941612
bio-data-visualization-volcano-and-ma-plots is a skill published in the GitHub repository PKU-YuanGroup/OpenAI4S (407 stars, last pushed yesterday), licensed MIT. It adds 106 tokens to every session and 5,775 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. It is 97% identical to bio-data-visualization-volcano-and-ma-plots, differing in 12 lines, and is treated as a copy.
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