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Nature figure

Skill kenantang/codex-and-claude-skills/collected-academic-research-skills/sources/Yuan1z0825__nature-skills/nature-figure

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npx -y skills add kenantang/codex-and-claude-skills --skill 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.

SKILL.md

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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:

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

FileOpen when
references/design-theory.mdTypography, color theory, layout rationale, export policy
references/api.mdPALETTE, helper function signatures, validation rules
references/common-patterns.mdUltra-wide panels, legend-only axes, print-safe bars
references/nature-2026-observations.mdReal Nature page archetypes: schematic-led composites, dark image plates, clinical triptychs, asymmetric hero layouts
references/tutorials.mdEnd-to-end walkthroughs: bars, trends, heatmaps
references/chart-types.mdRadar, 3D sphere, fill_between, scatter patterns

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