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Found 5 Skills
Low-level plotting library for full customization. Use when you need fine-grained control over every plot element, creating novel plot types, or integrating with specific scientific workflows. Export to PNG/PDF/SVG for publication. For quick statistical plots use seaborn; for interactive plots use plotly; for publication-ready multi-panel figures with journal styling, use scientific-visualization.
Help create paper-quality plots and figures with matplotlib or seaborn. Use when the user asks for plots, figures, or visualizations.
Foundational plotting library. Create line plots, scatter, bar, histograms, heatmaps, 3D, subplots, export PNG/PDF/SVG, for scientific visualization and publication figures.
Best practices for Matplotlib data visualization, plotting, and creating publication-quality figures in Python
This skill should be used when the user asks to "update study", "analyze new experiments", "update experiment document", or "refresh study notes". Produces academic-paper-quality experiment reports with matplotlib plots, executive summary with comparison tables, implementation structure, experimental results with figure interpretation, proposed improvements with code examples, hypotheses, limitations, and LaTeX PDF export with figures. Features incremental detection (only analyze NEW experiments), data extraction to DataFrame, automated plot generation, iterative writing improvement loop with quality criteria, zero-hallucination verification, and LaTeX PDF export. Usage - `/update-study logs/experiment.log study.md` or `/update-study "logs/exp1.log logs/exp2.log" results/ablation_study.md`