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JunMA98JunMA98/Computer-science-claude-skills/skills/scientific-visualization/SKILL.md

scientific-visualization

Skill3 starsChanged 8 months agoMITView on GitHub

Orchestrate publication-ready figures for CS papers, posters, slides, and benchmark reports. Use when assembling multi-panel result figures, harmonizing styles across plots, preparing export settings, or turning experiment outputs into clear visual evidence for submissions and technical communication.

21 other projects use a version of this.

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Used with

Projects with it also use usfiscaldata, etetoolkit and 9 more.

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Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
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The file

We split this skill into the rules it gives an agent

5 rules. 0 are common, 5 are distinctive, and 0 are very specific to this project.

  • 0

    Common

    Most of the 21 edited versions we read keep these.

  • 5

    Distinctive

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Use this skill when figure quality, consistency, and evidence communication matter more than raw plotting mechanics.

    line 8
  2. Distinctive:

    Use consistent semantics across panels.

    line 48
  3. Distinctive:

    Do not overload a paper figure with dashboard-level detail.

    line 50
  4. Distinctive:

    If the figure is exploratory rather than final, use seaborn or plotly directly instead.

    line 51
  5. Distinctive:

    When the user asks for figure help without more detail, return:

    line 55

“Common” is measured: kept by at least 6 in 10 of the 21 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Scientific Visualization

Use this skill when figure quality, consistency, and evidence communication matter more than raw plotting mechanics.

Typical CS Outputs

  • benchmark comparison figures
  • ablation summaries
  • latency / memory / accuracy tradeoff panels
  • error analysis views
  • embedding or representation comparison panels
  • reproducibility or scaling plots
  • poster or slide figure sets with consistent styling

Workflow

  1. Identify the claim each figure must support.
  2. Choose the smallest set of plots that makes the comparison interpretable.
  3. Standardize colors, labels, scales, and panel conventions.
  4. Export in formats appropriate to the destination: paper, slide, poster, or README.

Local Resources

Assets:

  • assets/publication.mplstyle
  • assets/presentation.mplstyle
  • assets/color_palettes.py
  • assets/nature.mplstyle as an inherited legacy style, not the default

References:

  • references/publication_guidelines.md
  • references/color_palettes.md
  • references/matplotlib_examples.md
  • references/journal_requirements.md only when a venue genuinely requires it

Scripts:

  • scripts/figure_export.py
  • scripts/style_presets.py

Working Rules

  • Optimize first for truthful comparison, then for polish.
  • Use consistent semantics across panels.
  • Label uncertainty clearly when seeds, folds, or repeated trials matter.
  • Do not overload a paper figure with dashboard-level detail.
  • If the figure is exploratory rather than final, use seaborn or plotly directly instead.

Default Output

When the user asks for figure help without more detail, return:

  1. figure set recommendation
  2. panel structure and style notes
  3. export guidance for the target medium
  4. risks that could make the visual misleading

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