scientific-visualization
by K-Dense-AIscientific-visualization is a meta-skill for publication-ready figures. Use it for journal submission plots with multi-panel layouts, significance annotations, error bars, colorblind-safe palettes, and Nature/Science/Cell-style formatting. It orchestrates matplotlib, seaborn, and plotly for scientific-visualization for Data Visualization work.
This skill scores 68/100, which means it is worth listing for users who need publication-ready scientific figures. The repository gives a clear trigger, substantial workflow content, and concrete publication targets, but directory users should still expect some adoption friction because there are no companion scripts, references, or install command to reduce setup guesswork.
- Clear use case for journal-ready scientific figures, including Nature/Science/Cell-style publication needs.
- Operational guidance is substantial: the skill body is large, structured, and includes workflow details for layouts, error bars, significance annotations, and export formats.
- Good agent leverage for figure production with explicit mention of matplotlib, seaborn, and plotly plus accessibility and colorblind-safe requirements.
- No install command and no support files, so users may need to infer setup and style resources from the text alone.
- The excerpt shows code examples and references to scripts like style_presets.py, but the repository evidence does not include those supporting assets.
Overview of scientific-visualization skill
What the scientific-visualization skill does
The scientific-visualization skill helps turn raw scientific data into publication-ready figures with the structure and styling expected in journals. It is best for work that must be accurate, legible, and exportable, not just visually appealing.
Who should use it
Use this scientific-visualization skill if you need multi-panel layouts, error bars, significance annotations, colorblind-safe palettes, consistent typography, or journal-specific formatting for papers, preprints, posters, or slide figures.
Why it differs from a generic prompt
A generic prompt can suggest “make it look nice,” but this skill is aimed at the practical constraints that block real publication work: figure size, readability at print scale, grayscale fallback, and output formats like PDF/EPS/TIFF. That makes the scientific-visualization guide more useful when the figure has to survive review.
How to Use scientific-visualization skill
Install scientific-visualization for your workflow
Install the scientific-visualization skill with:
npx skills add K-Dense-AI/claude-scientific-skills --skill scientific-visualization
After install, verify the skill path under scientific-skills/scientific-visualization and start from SKILL.md so you understand the intended workflow before adapting it to your project.
Read the right files first
The most useful first read is SKILL.md. If you want the broader context, check any referenced helpers or examples in the same skill folder. This repository does not ship extra rules/, resources/, or scripts/ folders for this skill, so the main value is in the skill instructions themselves.
Give the skill a real figure brief
For best scientific-visualization usage, do not ask for “a publication figure” in the abstract. Provide the data type, audience, target journal or venue, panel count, axis units, statistical annotations, and export format.
A stronger prompt looks like this:
Create a 4-panel scientific figure for a manuscript: time series, grouped bar chart, scatter with regression, and summary schematic. Use a colorblind-safe palette, readable labels at 85 mm width, significance markers, and export-ready formatting for PDF.
That level of detail makes the scientific-visualization install pay off because the output can be designed around the actual figure constraints.
Work from rough idea to final figure
A good scientific-visualization workflow is:
- Define the message of the figure.
- Specify what each panel must show.
- State journal or format constraints.
- Ask for a draft layout first.
- Refine labels, colors, annotations, and export settings after the draft.
If your goal is only exploratory analysis, this skill may be more process than you need; direct plotting in seaborn or plotly may be faster.
scientific-visualization skill FAQ
Is scientific-visualization only for journal figures?
No. The scientific-visualization skill is best known for journal-style output, but it also fits conference slides, lab meeting figures, reports, and any case where scientific data must be clear and defensible.
When should I not use it?
Do not use the scientific-visualization skill if you only need quick exploratory charts, dashboards, or interactive analytics. In those cases, a standard plotting workflow is usually simpler and faster.
Does it replace matplotlib, seaborn, or plotly?
No. It orchestrates them. The scientific-visualization guide is about how to use those tools with publication constraints in mind, not about replacing them.
Is it beginner-friendly?
Yes, if you can describe your figure goal clearly. The skill is most helpful when you know what story the figure must tell but need help with layout, styling, and publication-ready output.
How to Improve scientific-visualization skill
Give sharper input constraints
The biggest quality jump comes from specifying the target audience and output limits. Include figure width, number of panels, preferred file format, and whether the figure must work in color and grayscale. That helps the scientific-visualization skill avoid generic styling.
Provide the data shape, not just the topic
Instead of “make a figure about gene expression,” say whether the data is grouped categorical, time series, distributions, correlations, or trajectories. The more exact the data shape, the better the scientific-visualization usage will match the plot type and annotation choices.
Ask for layout before polish
Many failures happen when users request final styling before the structure is right. Ask first for panel order, annotations, and hierarchy; then refine fonts, colors, and export settings. This is the fastest way to improve scientific-visualization results.
Iterate on readability and publication fit
After the first draft, check whether labels remain readable at final print size, colors remain distinct for colorblind readers, and statistical marks are unambiguous. If not, revise the prompt with concrete fixes instead of vague feedback like “make it cleaner.”