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name: matplotlib description: 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.

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