nature-figure

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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.

5installs

Sourceyuan1z0825/nature-skills

Added on2026-05-02

NPX Install

npx skill4agent add yuan1z0825/nature-skills nature-figure

SKILL.md Content

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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:

python

import matplotlib.pyplot as plt

plt.rcParams['font.family'] = 'sans-serif'
plt.rcParams['font.sans-serif'] = ['Arial']
plt.rcParams['svg.fonttype'] = 'none'          # editable text in SVG/PDF
plt.rcParams['font.size'] = 16                 # 24 for large bar panels
plt.rcParams['axes.spines.right'] = False
plt.rcParams['axes.spines.top'] = False
plt.rcParams['axes.linewidth'] = 2.5           # 3 for big bars, 2 for compact
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