SKILL.md
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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)
- Generate a self-contained Python script using the template from
scripts/figure_template.pyas a starting point
- Write script to a temp file and execute:
python figure_script.py
- If error: capture traceback, feed back, regenerate (see ERROR_PROMPT in references)
- If no
.pngproduced: add explicit save instruction, retry
- 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