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-color-palettesgit 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-color-palettes)<a href="https://agentmods.dev/skills/pku-yuangroup/openai4s/bio-data-visualization-color-palettes"><img src="https://agentmods.dev/badge/skills/pku-yuangroup/openai4s/bio-data-visualization-color-palettes/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-color-palettes"><img src="https://agentmods.dev/badge/skills/pku-yuangroup/openai4s/bio-data-visualization-color-palettes.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 | $0.00099 | $0.04016 |
| Opus 5 | $0.00049 | $0.02008 |
| Sonnet 5 | $0.00020 | $0.00803 |
| Haiku 4.5 | $0.00010 | $0.00402 |
Grade A, and why
bio-data-visualization-color-palettes 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.
This is a copy
95% identical to bio-data-visualization-color-palettes — 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 — 320 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Version Compatibility
Reference examples tested with: viridis 0.6+, RColorBrewer 1.1+, scico 1.5+ (Crameri colormaps in R), khroma 1.12+ (Tol/Crameri palettes in R), matplotlib 3.8+, colorcet 3.0+, ggsci 3.0+, colorspace 2.1+.
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.
Color Palettes for Scientific Visualization
"Pick a color palette" -> Choose a colormap that (a) is perceptually uniform along the relevant data axis, (b) remains interpretable under common color-vision deficiencies, (c) prints correctly to grayscale, and (d) matches the data type — sequential, diverging, cyclic, or qualitative.
- R:
viridis::viridis,scico::scale_color_scico,khroma::color,RColorBrewer::brewer.pal - Python:
matplotlib.colormaps,colorcet,seaborn.color_palette,cmcrameri.cm
The Three Modern Standards
-
Perceptual uniformity -- equal data steps produce equal perceived color steps. viridis (van der Walt 2015), cividis (Nuñez 2018), and the Crameri family (batlow, roma, vik) are designed for this. Jet, rainbow, and red->green are not.
-
Color vision deficiency safety -- ~6% of males have deuteranopia / protanopia (red-green deficiency). cividis was explicitly designed to be near-identical under normal and CVD viewing (Nuñez 2018 PLOS ONE 13:e0199239). The Okabe-Ito 8-color qualitative palette (popularized in Wong 2011 Nat Methods 8:441) is the CVD-safe categorical default.
-
Grayscale monotonicity -- a perceptually-uniform sequential colormap has monotonically increasing luminance. Convert the figure to grayscale; if the order is still readable, the colormap is luminance-monotonic. This is the single most actionable test.
What ships with it
3 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 · 320 lines · 99 tokens per session scan A edd6f1c68b1f
bio-data-visualization-color-palettes is a skill published in the GitHub repository PKU-YuanGroup/OpenAI4S (403 stars, last pushed today), licensed MIT. It adds 99 tokens to every session and 4,016 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. It is 95% identical to bio-data-visualization-color-palettes, differing in 12 lines, and is treated as a copy.
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