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cjh-solvercjh-solver/MathModeling-Skills-Arsenal/skills/04-figures/figure-generation/SKILL.md

figure-generation

Skill14 starsChanged 11 days agoApache-2.0View on GitHub

Generate publication-quality scientific figures using matplotlib/seaborn with a three-phase pipeline (query expansion, code generation with execution, VLM visual feedback). Handles bar charts, line plots, heatmaps, training curves, ablation plots, and more. Use when the user needs figures, plots, or visualizations for a paper.

6 other projects use a version of this, 1 of them edited.

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The file

We split this skill into the rules it gives an agent

8 rules. About 0 are common, 3 are distinctive, and 5 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈3

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 5

    Very specific

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

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    Write script to a temp file and execute: python figure_script.py

    line 33
  2. Very specific:

    If error: capture traceback, feed back, regenerate (see ERROR_PROMPT in references)

    line 34
  3. Very specific:

    If no .png produced: add explicit save instruction, retry

    line 35
  4. Very specific:

    Read the generated PNG file and visually inspect using the VLM feedback prompts from references/figure-prompts.md:

    line 39
  5. Distinctive:

    If improvements needed: generate corrective instructions and re-execute.

    line 45
  6. Very specific:

    All MatPlotAgent prompts: ~/.claude/skills/figure-generation/references/figure-prompts.md

    line 49
  7. Distinctive:

    All text ≥ 8pt at print size

    line 68
  8. Distinctive:

    No matplotlib default title — use LaTeX caption

    line 70

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “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 Figure Generation

Generate publication-quality figures for research papers.

Input

  • $0 — Description of the desired figure
  • $1 — (Optional) Path to data file (CSV, JSON, NPY, PKL) or results directory

Scripts

Generate figure template

python ~/.claude/skills/figure-generation/scripts/figure_template.py --type bar --output figure_script.py --name comparison
python ~/.claude/skills/figure-generation/scripts/figure_template.py --list-types

Available types: bar, training-curve, heatmap, ablation, line, scatter, radar, violin, tsne, attention

Three-Phase Pipeline (from MatPlotAgent)

Phase 1: Query Expansion

Expand the user's figure description into step-by-step coding specifications using the prompts in references/figure-prompts.md. Determine: figure type, data mapping (x/y/color/hue), style requirements, paper conventions.

Phase 2: Code Generation with Execution Loop (up to 4 retries)

  1. Generate a self-contained Python script using the template from scripts/figure_template.py as a starting point
  2. Write script to a temp file and execute: python figure_script.py
  3. If error: capture traceback, feed back, regenerate (see ERROR_PROMPT in references)
  4. If no .png produced: add explicit save instruction, retry
  5. On success: report the generated figure path

Phase 3: Visual Refinement

Read the generated PNG file and visually inspect using the VLM feedback prompts from references/figure-prompts.md:

  • Does the figure type match the request?
  • Are labels, titles, and legends correct?
  • Is the color scheme appropriate and consistent?
  • Are axis scales sensible? Is text readable at publication size?

If improvements needed: generate corrective instructions and re-execute.

References

  • All MatPlotAgent prompts: ~/.claude/skills/figure-generation/references/figure-prompts.md
  • Figure templates: ~/.claude/skills/figure-generation/scripts/figure_template.py

Output

Both PNG (preview, 300 DPI) and PDF (vector, for paper) formats. Plus the LaTeX include code:

\begin{figure}[t]
    \centering
    \includegraphics[width=\linewidth]{figures/figure_name.pdf}
    \caption{Description. Best viewed in color.}
    \label{fig:figure_name}
\end{figure}

Quality Requirements

  • DPI ≥ 300, or vector PDF
  • Colorblind-friendly palette (no red-green only)
  • All text ≥ 8pt at print size
  • Consistent styling across all paper figures
  • No matplotlib default title — use LaTeX caption

Related Skills

  • Upstream: data-analysis, experiment-code
  • Downstream: paper-writing-section, paper-compilation, slide-generation
  • See also: table-generation

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