Proposal: An AI-assisted UltraPlot skill for scientific plotting

[lwq-star]

Hi UltraPlot community,

First of all, thank you for building and maintaining UltraPlot. I have been using it for scientific plotting in my research for about two years, and I really enjoy working with it. It has helped me focus more on the data, the message, and the figure design, instead of repeatedly writing low-level Matplotlib boilerplate.

With the rapid rise of AI agents, I am thinking about creating an AI-assisted scientific plotting skill based on UltraPlot.

By “skill,” I mean a reusable guidance package for an AI assistant or agent: a structured set of plotting principles, UltraPlot best practices, code templates, examples, and quality-check rules that can help users generate clear, publication-ready scientific figures with UltraPlot.

My current idea is roughly:

1. Review classic scientific/data visualization principles
   I would like to start by collecting and summarizing well-known principles for scientific and data visualization, including visual perception, color choice, annotation, layout, accessibility, and graphical integrity.

2. Combine these principles with practical UltraPlot workflows
   The skill could include recommended UltraPlot patterns for common scientific plotting tasks, such as multi-panel layouts, figure-level formatting, colorbars, legends, colormaps, geographic plots, and publication-style figures.

3. Create an AI-agent workflow for scientific plotting
   The agent could guide users through questions such as: What is the scientific message of the figure? Which variables and uncertainties should be shown? What plot type and layout are appropriate? Which UltraPlot APIs should be used?

I would love to hear your thoughts:

• Would this be useful for the UltraPlot community?
• Which UltraPlot features or workflows should such a skill emphasize?
• Are there any concerns about AI agents generating plotting code, such as hallucinated APIs, version compatibility, or reproducibility?

Thanks again for the great library!

[cvanelteren]

hmm not really sure what you are asking here. I think coding is rather personal and I am not enforcing any particular pattern necessarily on people -- at least this is not my intention. I think coding agents can in principle access the source and therefore see how some of the boilerplate code could be replaced with higher level UltraPlot functions (such as format).

I tend to prefer neater (read shorter) code but some of the plots I produce are still hundreds of lines long. Over my time with scientific plotting, I stopped optimizing and started producing. That is, initially I thought I could somehow take short cuts by cutting through and putting repeated calls in functions over my plotting career. Later, I noticed that this is moot and just started considering each plot as a piece of unique art.

You are of course free to make a skill that would produce such a thing (and happy to take a look at it), but as of now I don't really now what those best types of practices would look like.

[lwq-star]

I fully agree with the point that coding is personal.

I probably did not describe the idea clearly enough. I do not intend to make scientific plotting more template-driven, or to define a preferred coding style for UltraPlot users. I also agree that each scientific figure is often quite specific to the data, the argument, and the paper, so it cannot be fully standardized.

What I have in mind is more of an AI-agent skill that helps generate plotting code from the user’s scientific goal and data context. The purpose would be to reduce the amount of manual coding where possible, while still leaving room for the user to inspect, refine, and customize the details. For fine-grained figure adjustments, I agree that the user will almost always need to modify the code manually.

So the skill would not be a set of rules saying “this is how people should write UltraPlot code.” Instead, it would provide the AI agent with useful context, examples, and guardrails, such as:

• how to recognize when UltraPlot provides higher-level functions that can replace repeated Matplotlib boilerplate;
• how to use UltraPlot features such as format(), layouts, colorbars, legends, colormaps, and figure-level formatting more naturally;
• how to ask the user about the scientific message of the figure before generating code;
• how to avoid hallucinated or outdated APIs;
• how to review the generated figure for readability, reproducibility, and publication quality.

The kind of skill I have in mind is similar in spirit to existing scientific visualization / figure-generation skill projects such as:

• scientific-agent-skills/skills/scientific-visualization
• scipilot-figure-skill
• nature-skills/skills/nature-figure

These projects are not necessarily trying to force one plotting style either. They are more like guidance systems for AI agents: helping the agent understand the task, choose an appropriate visualization strategy, generate code, and then review the output.

For an UltraPlot-based version, the focus would be on helping AI agents understand the UltraPlot API and use its higher-level abstractions effectively, rather than falling back to verbose low-level Matplotlib code by default.

Once there is something concrete, I would be very happy to share it here and get your feedback.