| name | nature-figure |
| description | Publication-ready matplotlib figures for Nature/high-impact journals and academic papers. Covers bar charts, grouped bars, heatmaps, line/trend plots, forest plots, microscopy-style image panels, schematic + quantitative composites, radar plots, and multi-panel layouts with Nature-style typography (Arial/sans-serif), restrained color systems, and SVG/PDF export conventions. Use when creating scientific figures that must match Nature publication standards. Do NOT use for interactive dashboards (Plotly, Bokeh) or Illustrator/Figma-first infographic workflows. |
Nature Figure Making Skill
A complete guide for producing publication-quality matplotlib figures matching Nature journal standards.
Derived from the figures4papers repository
(papers published in Nature Machine Intelligence and top ML venues), then extended with direct
visual observations from a 2026 Nature sample spanning materials science, genomics,
neuroscience, plant biology and clinical studies.
Color policy: prefer unified method families across all panels over maximal hue separation.
For dense Nature Machine Intelligence-style figure pages, use the low-saturation NMI pastel
family described in references/api.md and reserve green/red mainly for gains, drops, and other directional cues.
Quick-start: Mandatory rcParams
Always apply these at the top of every script:
import matplotlib.pyplot as plt
plt.rcParams['font.family'] = 'sans-serif'
plt.rcParams['font.sans-serif'] = ['Arial']
plt.rcParams['svg.fonttype'] = 'none'
plt.rcParams['font.size'] = 16
plt.rcParams['axes.spines.right'] = False
plt.rcParams['axes.spines.top'] = False
plt.rcParams['axes.linewidth'] = 2.5
plt.rcParams['legend.frameon'] = False
Use text.usetex = True only when LaTeX is installed and math-rich labels are required.
Default operating stance
- Start by classifying the requested figure into one of four archetypes:
quantitative grid, schematic-led composite, image plate + quant, or asymmetric mixed-modality figure.
- Prefer one hero panel plus subordinate evidence panels over filling the canvas with equal-sized subplots.
- Keep the background white for plots and diagrams; switch to black only for microscopy / volume-rendering image plates.
- Prefer direct labels over legends when categories are spatially fixed or the legend would force unnecessary eye travel.
- Keep one restrained palette per figure: usually one neutral family, one signal family, and one accent family.
- When the user asks for broad
Nature style rather than ML/NMI-specific style, read references/nature-2026-observations.md before choosing layout.
When to load this skill
- Matplotlib figures for papers, slides, or reports targeting Nature, NeurIPS, ICLR, or similar venues.
- Requests involving grouped bars, trend lines, heatmaps, radar plots, multi-panel grids, or PDF/SVG/high-DPI output.
- Any mention of "Nature style", "publication figure", "paper figure", or "high-quality scientific plot".
When NOT to load
- Plotly, Altair, Bokeh, or other interactive/web-first plotting.
- EDA-only plots without a publication target.
- Primary workflow is 3D, GIS, or non-matplotlib tooling.
- Illustrator / Figma–first layout.
Related files
| File | Open when |
|---|
| references/design-theory.md | Typography, color theory, layout rationale, export policy |
| references/api.md | PALETTE, helper function signatures, validation rules |
| references/common-patterns.md | Ultra-wide panels, legend-only axes, print-safe bars |
| references/nature-2026-observations.md | Real Nature page archetypes: schematic-led composites, dark image plates, clinical triptychs, asymmetric hero layouts |
| references/tutorials.md | End-to-end walkthroughs: bars, trends, heatmaps |
| references/chart-types.md | Radar, 3D sphere, fill_between, scatter patterns |
