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JunMA98JunMA98/Computer-science-claude-skills/skills/matplotlib/SKILL.md

matplotlib

Skill3 starsChanged 8 months agoMITView on GitHub

Create static, highly customized figures for CS papers, benchmark reports, experiment analysis, and technical documentation. Use when default plotting tools are too limiting, when multi-panel layouts need precise control, or when exporting clean PNG, PDF, or SVG figures for research outputs.

19 other projects use a version of this.

Adopted by

Most recently taken up by

Used with

Projects with it also use usfiscaldata, deeptools and 10 more.

Other files people use alongside this one.

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

6 rules. 0 are common, 5 are distinctive, and 1 is very specific to this project.

  • 0

    Common

    Most of the 19 edited versions we read keep these.

  • 5

    Distinctive

    This author's own choices. Worth a look.

  • 1

    Very specific

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

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Use this skill when you need exact control over figure structure, axes, annotations, and export settings.

    line 8
  2. Distinctive:

    Prefer the object-oriented API for nontrivial figures.

    line 29
  3. Distinctive:

    Keep labels, legends, and tick density readable at paper scale.

    line 31
  4. Distinctive:

    Use seaborn or plotly only when their defaults genuinely save time.

    line 32
  5. Very specific:

    Use scientific-visualization when the task is primarily figure orchestration or publication styling across multiple plots.

    line 33
  6. Distinctive:

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

    line 37

“Common” is measured: kept by at least 6 in 10 of the 19 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

Matplotlib

Use this skill when you need exact control over figure structure, axes, annotations, and export settings.

Good Fits

  • training curves and ablation figures
  • resource tradeoff plots: latency, memory, throughput
  • confusion matrices and calibration-style figures
  • multi-panel paper figures
  • custom benchmark visualizations that higher-level libraries do not support well

Local Resources

  • references/plot_types.md
  • references/styling_guide.md
  • references/common_issues.md
  • references/api_reference.md
  • scripts/plot_template.py
  • scripts/style_configurator.py

Working Rules

  • Prefer the object-oriented API for nontrivial figures.
  • Design the figure around the comparison the reader must notice first.
  • Keep labels, legends, and tick density readable at paper scale.
  • Use seaborn or plotly only when their defaults genuinely save time.
  • Use scientific-visualization when the task is primarily figure orchestration or publication styling across multiple plots.

Default Output

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

  1. figure plan
  2. recommended plot type and layout
  3. styling or export notes
  4. code skeleton if implementation is requested

Download now (ZIP)

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