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/thesecondfox/skill/bio-pathway-analysis-enrichment-visualizationnpx skills add thesecondfox/skill --skill bio-pathway-analysis-enrichment-visualizationgit clone --depth 1 https://github.com/thesecondfox/skillWrote 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/thesecondfox/skill/bio-pathway-analysis-enrichment-visualization)<a href="https://agentmods.dev/skills/thesecondfox/skill/bio-pathway-analysis-enrichment-visualization"><img src="https://agentmods.dev/badge/skills/thesecondfox/skill/bio-pathway-analysis-enrichment-visualization.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 5.1 | $0.00058 | $0.01873 |
| Opus 5 | $0.00029 | $0.00937 |
| Sonnet 5 | $0.00012 | $0.00375 |
| Haiku 4.5 | $0.00006 | $0.00187 |
Grade A, and why
bio-pathway-enrichment-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 2d 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 — 279 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Version Compatibility
Reference examples tested with: ggplot2 3.5+
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.
Enrichment Visualization
"Create publication-quality plots from my enrichment analysis" → Generate dotplots, gene-concept networks, enrichment maps, GSEA running score plots, and ridgeplots from clusterProfiler results.
- R:
dotplot(),cnetplot(),emapplot(),gseaplot2()(enrichplot)
Scope
This skill covers enrichplot package functions designed for clusterProfiler results:
dotplot(),barplot()- Summary viewscnetplot(),emapplot(),treeplot()- Network/hierarchical viewsgseaplot2(),ridgeplot()- GSEA-specificgoplot(),heatplot(),upsetplot()- Specialized views
For custom ggplot2 enrichment dotplots (manual implementation), see data-visualization/specialized-omics-plots.
Setup
Goal: Load required packages for visualizing enrichment analysis results.
Approach: Import clusterProfiler, enrichplot, and ggplot2 which provide the plotting functions for enrichment objects.
library(clusterProfiler)
library(enrichplot)
library(ggplot2)
# Assume ego (enrichGO result), kk (enrichKEGG result), or gse (GSEA result) exists
Dot Plot
Goal: Summarize enrichment results showing gene ratio, count, and significance in a single figure.
Approach: Use enrichplot dotplot which maps gene ratio to x-axis, term to y-axis, dot size to count, and color to p-value.
Most common visualization - shows gene ratio, count, and significance.
dotplot(ego, showCategory = 20)
# Customize
dotplot(ego, showCategory = 15, font.size = 10, title = 'GO Enrichment') +
scale_color_gradient(low = 'red', high = 'blue')
# Save
pdf('go_dotplot.pdf', width = 10, height = 8)
dotplot(ego, showCategory = 20)
dev.off()
What ships with it
1 file 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.
- 2d ago First seen · 279 lines · 58 tokens per session scan A 10af7ce0e084
bio-pathway-enrichment-visualization is a skill published in the GitHub repository thesecondfox/skill (3 stars, last pushed 5mo ago), licensed MIT. It adds 58 tokens to every session and 1,873 once invoked, about $0.0003 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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