scientific-artist

name	scientific-artist
description	Generate scientifically accurate visual output from descriptions of scientific concepts or models. Use for sketches, figures, schematics, diagrams, publication graphics, Excalidraw drafts, Matplotlib figures, or TikZ output.

Scientific Artist

Generate scientifically accurate visual output from a description of a scientific concept or model.

Workflow

1. Read the user's description. Identify: equations, variables, spatial relationships, physical processes.
2. Ask which tool stage to use (or infer from context — see selection table below).
3. At publishing stage, disambiguate Matplotlib Advanced vs. TikZ if unclear.
4. Generate output following the accuracy and style rules below.
5. Present output for user feedback; iterate.

Tool selection

User signal	Stage	Tool
"sketch", "draft", "quick diagram", "for a talk"	Drafting	Excalidraw MCP
"figure", "prototype", "next to my code", "iterate"	Prototyping	Matplotlib
"paper", "journal", "publication", "submit"	Publishing	Matplotlib Advanced or TikZ — ask if unclear
Ambiguous	—	Ask the user

Scientific accuracy rules

These are non-negotiable across all stages:

• All labels and annotations use LaTeX math symbols.
• Model parameters are named constants at the top of the script, never magic numbers.
• Equations must match the actual model: correct variable names, subscripts, operator precedence.
• Arrow directions and spatial layout reflect physical meaning (gradients toward sources, torque arcs show rotation, sensing radii to scale).
• At publication stage: parameters from simulation output, not hardcoded.

Stage-specific guidance

Excalidraw (drafting)

Use the Excalidraw MCP tools (create_view, save_checkpoint). Call read_me before first use.

• Correct topology: right components connected to right components.
• Proper variable names on all labels (e.g., "Cell i", not "Cell 1").
• Physically meaningful spatial arrangement from the start.
• No equations required, but variable names must be correct.
• See references/excalidraw-patterns.md for MCP usage and layout conventions.

Matplotlib (prototyping)

• Enable full LaTeX: matplotlib.rcParams['text.usetex'] = True and font.family = 'serif'.
• Parameters as named constants at script top.
• Plot functions accept ax argument (project convention).
• Save as PDF/SVG to figures/ or outputs/.
• See references/matplotlib-patterns.md for style patterns and helpers.

Matplotlib Advanced (publishing — Python pipeline)

• GridSpec multi-panel layouts with labeled panels (A, B, C...).
• Simulation-coupled panels: run real physics to generate data (Bessel functions, Monte Carlo, phase diagrams).
• Comparison tables via FancyBboxPatch.
• panel_bg helper for consistent panel styling.
• See references/matplotlib-advanced-patterns.md for multi-panel and simulation patterns.

TikZ (publishing — LaTeX)

• Native LaTeX math via amsmath/amssymb.
• Reusable \tikzset styles for cells, boxes, arrows.
• Relative positioning via TikZ libraries.
• Compile with pdflatex for vector PDF.
• See references/tikz-patterns.md for style definitions and patterns.

Examples

Working examples in examples/:

File	What it demonstrates
schematic-matplotlib.py	Single-panel schematic with LaTeX math, named parameters, color palette
schematic-advanced-matplotlib.py	6-panel simulation-coupled figure: K0 field, 2D heatmap, sine torque, heading update, Monte Carlo, phase diagram, comparison table
chemoattraction-model-plan.md	Example "plan" input — the physics document that drove the advanced figure
schematic-tikz.tex	Publication-ready TikZ schematic with reusable styles