mathmodel-figure-templates

jihe520/MathModelAgent · Agent Skill

Generates scientific visualizations using ready-made Python templates within the MathModel LaTeX sandbox.

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https://github.com/jihe520/MathModelAgent/tree/main/skills/mathmodel-figure-templates

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SKILL.md contents

Original author text · read-only

MathModel Figure Templates

This skill is bundled into the LaTeX sandbox at /home/user/.claude/skills/mathmodel-figure-templates. It contains ready-to-run Python/matplotlib scripts for the figure templates exposed in the MathModel Improve tab.

Fast Path

1. Match the requested chart in references/figure-catalog.md.

2. From /home/user/workspace, run the renderer with the template id:

python3 /home/user/.claude/skills/mathmodel-figure-templates/scripts/render_template.py paired-raincloud

3. The renderer copies the bundled template script into 绘图复刻/scripts/, runs it there, and writes outputs to 绘图复刻/outputs/.

4. Return the generated PNG/PDF/SVG paths and the copied script path to the user.

Use --list to show supported ids:

python3 /home/user/.claude/skills/mathmodel-figure-templates/scripts/render_template.py --list

Output Contract

  • Work under the current workspace unless the user gives another path.
  • Default project folder: 绘图复刻.
  • Script path: 绘图复刻/scripts/make_<template>.py.
  • Outputs: 绘图复刻/outputs/<template>_replica.png, .pdf, .svg.
  • Use the bundled scripts as the first choice; edit the copied workspace script only when the user requests customization.
  • The bundled scripts use deterministic simulated data. Do not claim simulated values reproduce a source study exactly.

Template Ids

  • multiclass-shap-combo
  • paired-raincloud
  • cv-roc-ci
  • taylor-diagram
  • correlation-pairgrid
  • prediction-marginal-grid
  • rf-tpe-surface
  • grouped-corr-split-violin
  • grouped-circular-heatmap
  • urban-park-cooling-combo
  • nature-chord-diagram

When Customizing

If the user asks for changes, copy/run the nearest template first, then edit the copied file in 绘图复刻/scripts/. Preserve:

  • MPLCONFIGDIR before importing matplotlib.
  • deterministic seeds for simulated data.
  • PNG/PDF/SVG export.
  • readable labels, legends, and high-DPI output.

Use references/plot-recipes.md for implementation patterns.