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 distribution-plotsgit 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/distribution-plots)<a href="https://agentmods.dev/skills/gptomics/bioskills/distribution-plots"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/distribution-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/gptomics/bioskills/distribution-plots"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/distribution-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 | $0.00095 | $0.03755 |
| Opus 5 | $0.00048 | $0.01878 |
| Sonnet 5 | $0.00019 | $0.00751 |
| Haiku 4.5 | $0.00010 | $0.00376 |
Grade A, and why
bio-data-visualization-distribution-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 7d 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-data-visualization-distribution-plots — 97% identical, 12 lines differ
How it starts
The opening of the file, as written. The whole thing — 266 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+, ggbeeswarm 0.7+, ggdist 3.3+, gghalves 0.1.4+, seaborn 0.13+, matplotlib 3.8+, ptitprince 0.3+ (Python raincloud).
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.
Distribution Plots
"Plot the distribution per group" -> Render boxplot, violin, beeswarm, or raincloud calibrated to N per group, the underlying distribution shape, and the audience's ability to read each encoding. The default geom_bar(stat='summary') is the canonical misleading choice — Weissgerber 2015 PLOS Biol documented that 703 top physiology papers use bar-of-mean despite multiple distinct distributions producing identical bars.
- R:
ggplot2::geom_boxplot,ggplot2::geom_violin,ggbeeswarm::geom_quasirandom,ggdist::stat_halfeye,gghalves::geom_half_violin - Python:
seaborn.boxplot/violinplot/swarmplot/stripplot,ptitprince.RainCloud
The Single Most Important Modern Insight -- Bars of Means Lie
Weissgerber, Milic, Winham & Garovic 2015 PLOS Biol 13:e1002128 surveyed 703 papers in top physiology journals and found that bar-and-line graphs of means dominate, despite many distinct distributions producing identical bar plots. Bimodal data, skewed data, and data with outliers all collapse to the same bar height and error bar. The bar plot is a hypothesis test result rendered as visualization; the visualization should show the data.
The modern alternative is to show every point for n < 30, layer summary on top, and reserve summary-only plots for large N where points would overplot.
Decision Tree by N per Group
| N per group | Recommended | Avoid |
|---|---|---|
| 3-10 | Dot plot or jittered raw points + median bar | Bar of mean |
| 10-30 | Beeswarm OR quasirandom + box overlay | Bare boxplot (hides bimodality) |
| 30-200 | Raincloud (Allen 2019) OR box + jitter | Bare violin (default KDE bandwidth oversmooths) |
| 200-1000 | Letter-value plot (Hofmann 2017) OR violin with explicit bandwidth | Box alone (collapses tails) |
| >1000 | Density (KDE) or histogram + summary stats | Individual points (overplot) |
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.
- 7d ago First seen · 266 lines · 95 tokens per session scan A 0c399eea6c35
bio-data-visualization-distribution-plots is a skill published in the GitHub repository GPTomics/bioSkills (1,201 stars, last pushed 26d ago), licensed MIT. It adds 95 tokens to every session and 3,755 once invoked, about $0.0005 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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