nature-figure

Nature Figure Making Skill

A guide for producing publication-quality scientific figures as a visual argument, not as isolated pretty plots. Every figure starts from a claim, an evidence hierarchy, and a review-risk check before code or aesthetics.

The older Python/matplotlib rules in this skill remain valid. The skill now also supports R, especially ggplot2 + patchwork + ComplexHeatmap + ggrepel + svglite/cairo_pdf + ragg. If the user provides a private plotting template collection, use it only as an internal adaptation source and do not reveal its path, filenames, or provenance in user-facing output.

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.

First move: figure contract before plotting

Before generating or editing code, establish the contract below.

Backend selection is a blocking gate. If the user has not explicitly chosen Python or R in the current request or provided a clearly language-specific input file/workflow, ask one concise question: Python or R? Then stop and wait for the user's answer. Do not generate mock data, write scripts, create figures, or choose Python/R by default. This overrides general autonomy/default-execution behavior for figure tasks.

The selected backend is exclusive for all figure generation. Once Python or R is selected, every plotting script, preview image, SVG/PDF/TIFF/PNG export, QA render, and visual workaround must be produced by that same backend. Do not use Python to draw a preview for an R figure, and do not use R to draw a preview for a Python figure, even if the selected runtime or packages are missing locally. The non-selected language may only be used for non-visual file inspection or data conversion when it does not open a graphics device, import plotting libraries, create image/vector files, or change the final visual appearance.

Missing runtime/package rule. After the backend is selected, check the selected runtime early (Rscript/R for R; Python and required plotting packages for Python). If the selected runtime or required packages are unavailable, stop before rendering and report the exact blocker. You may provide a selected-backend script and installation commands, or ask permission to install dependencies, but you must not fall back to the other language to make a substitute figure.

Only recommend a backend when the user explicitly asks you to choose or recommend one. In that case, use references/backend-selection.md, state the reason, and then proceed with the recommended backend.

1. Core conclusion: write the one-sentence claim the figure must defend.
2. Evidence chain: map each planned panel to the claim, and drop panels that do not carry a unique piece of evidence.
3. Archetype: classify the figure as quantitative grid, schematic-led composite, image plate + quant, or asymmetric mixed-modality figure.
4. Backend: use the selected Python or R track exclusively for all figure drawing, previewing, exporting, and visual QA. Do not cross-render with the other language.
5. Journal/export contract: set final dimensions, editable text, source data, statistics, image-integrity notes, and export formats before styling.

The highest-priority rule is: the chart serves the scientific logic. Aesthetic polish, template matching, and complex layout are subordinate to making the core conclusion clear, defensible, and reviewable.

User-facing privacy rule

Do not disclose private local paths, private filenames, chat-attachment names, internal reference filenames, template identifiers, or the provenance of private working materials in user-facing replies, generated code comments, figure legends, reports, or manuscript text. Use generic descriptions such as "the provided R template collection", "a private working draft", or "the internal figure contract". Only reveal an exact path or source file when the user explicitly asks for that audit trail.

Python quick-start

Python-only execution rule. When the user has selected Python, do all figure drawing, previewing, exporting, and visual QA in Python. Do not call R/ggplot2, ComplexHeatmap, patchwork, or any R graphics device to create a temporary preview, fallback export, or layout approximation. If Python or required Python plotting packages are missing, stop before rendering and report the missing dependency. You may still write the Python script, provide pip/environment install commands, or ask permission to install dependencies, but do not cross-render the figure in R.

import matplotlib as mpl
import matplotlib.pyplot as plt

mpl.rcParams.update({
    "font.family": "sans-serif",
    "font.sans-serif": ["Arial", "Helvetica", "DejaVu Sans", "sans-serif"],
    "svg.fonttype": "none",     # editable text in SVG
    "pdf.fonttype": 42,         # editable TrueType text in PDF
    "font.size": 7,             # use 15-24 only for large slide-sized panels
    "axes.spines.right": False,
    "axes.spines.top": False,
    "axes.linewidth": 0.8,
    "legend.frameon": False,
})

def save_pub_py(fig, filename, dpi=600):
    fig.savefig(f"{filename}.svg", bbox_inches="tight")
    fig.savefig(f"{filename}.pdf", bbox_inches="tight")
    fig.savefig(f"{filename}.tiff", dpi=dpi, bbox_inches="tight")

Use text.usetex = True only when LaTeX is installed and math-rich labels are required.

R quick-start

library(ggplot2)
library(patchwork)

theme_set(
  theme_classic(base_size = 6.5, base_family = "Arial") +
    theme(
      axis.line = element_line(linewidth = 0.35, colour = "black"),
      axis.ticks = element_line(linewidth = 0.35, colour = "black"),
      legend.title = element_text(size = 6.2),
      legend.text = element_text(size = 5.8),
      strip.text = element_text(size = 6.2, face = "bold"),
      plot.title = element_text(size = 7, face = "bold"),
      panel.grid = element_blank()
    )
)

save_pub_r <- function(plot, filename, width_mm = 183, height_mm = 120, dpi = 600) {
  w <- width_mm / 25.4
  h <- height_mm / 25.4
  svglite::svglite(paste0(filename, ".svg"), width = w, height = h)
  print(plot)
  dev.off()
  grDevices::cairo_pdf(paste0(filename, ".pdf"), width = w, height = h, family = "Arial")
  print(plot)
  dev.off()
  ragg::agg_tiff(paste0(filename, ".tiff"), width = w, height = h, units = "in", res = dpi)
  print(plot)
  dev.off()
}

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.
• If the user asks for a single chart, still identify its role in the manuscript claim: discovery, mechanism, validation, comparison, robustness, or clinical/biological relevance.
• 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.
• Treat statistics, n, error-bar definitions, source-data traceability, and image-integrity notes as part of the figure, not as optional caption cleanup.
• When the user asks for broad Nature style rather than ML/NMI-specific style, read references/nature-2026-observations.md before choosing layout.
• When the user references figures4papers or the older scientific-figure-making skill, treat this skill as the successor and open references/demos.md for bundled Python demo scripts.

When to load this skill

• Python or R figures for papers, slides, or reports targeting Nature, Science, Cell, 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", "SCI figure", "figures4papers", "scientific-figure-making", "R plotting template", or "high-quality scientific plot".
• Requests to improve a figure's logic, aesthetics, panel layout, figure legend, export quality, or journal-readiness.

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-scientific illustration tooling.
• Illustrator / Figma–first layout.

Related files

File	Open when
references/figure-contract.md	Need to convert a user request into core conclusion, evidence hierarchy, panel map, and review-risk checks
references/backend-selection.md	User has not chosen Python/R, asks for a recommendation, or a mixed Python/R workflow is possible
references/r-workflow.md	User chooses R or provides R scripts/templates/data
references/r-template-index.md	Need to adapt a user-provided or private R template collection without exposing source paths
references/qa-contract.md	Before final delivery, revision package, microscopy/blot figure, or journal-specific audit
references/design-theory.md	Typography, color theory, layout rationale, export policy
references/api.md	Python PALETTE, helper function signatures, validation rules
references/common-patterns.md	Python layout patterns: hero panels, legend-only axes, dark image plates, asymmetric layouts
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
references/demos.md	Bundled figures4papers Python scripts and output previews for concrete pattern adaptation

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nature-figure skill

Submission-grade scientific figures for Nature-tier journals and high-impact academic venues, with both Python and R plotting tracks.

The skill starts from a figure contract: core conclusion, evidence hierarchy, archetype, backend choice, journal/export constraints, statistics, and source-data traceability. Plotting templates are used only after the scientific logic is clear.

Python remains the best-supported low-level layout path through matplotlib, seaborn, subplot_mosaic, and statsmodels. R is supported through ggplot2, patchwork, ComplexHeatmap, ggrepel, svglite, cairo_pdf, and ragg. If private template collections are used, their paths, filenames, and provenance must not appear in user-facing output.

Derived from production scripts in figures4papers (published in Nature Machine Intelligence and top ML/bioinformatics venues). The original figures4papers demo scripts and preview images are also bundled under assets/figures4papers/ for pattern-level adaptation.

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Example output gallery

The images below are simulated data mockups generated with this skill's rules: editable SVG-first export, restrained semantic palettes, lowercase panel labels, and asymmetric multi-panel information architecture. They are PNG previews for README display; production use should still export SVG/PDF from the plotting script.

Figure	Preview	What the skill demonstrates
Material design and physical validation		Schematic-led composite, SEM-like image panel, rheology, release kinetics, retention map, correlation and endpoint quantification
Spatial retention and uptake		Dark microscopy plate, channel rows, zoom crops, depth profiles, uptake histograms, 3D penetration heatmap and image-derived correlation
In vivo efficacy and tolerability		Experimental timeline, longitudinal tumour curves, individual growth traces, waterfall response, forest plot, histology, immune composition and toxicity panels
Single-cell systems figure		UMAP-style embedding, composition, marker heatmap, pseudotime, volcano plot, enrichment, ligand-receptor bubble matrix and spatial niche adjacency
Perturbation validation		Mechanistic perturbation timeline, relapse endpoint, polar summary, dose response, synergy matrix, biodistribution, cytokines, flow-like scatter and safety score

Gallery file policy
Keep only lightweight PNG previews in assets/gallery/. Do not commit large generated SVG/PDF outputs unless they are needed for a tutorial, because real users should regenerate editable outputs from source data and scripts.

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Chart-type atlas

The gallery below classifies the skill by chart family. Each preview is a dense 4 x 4 atlas of small panels, designed to show the range of visual grammars that can be combined inside a larger Nature-style result figure.

Type	Preview	Common use
Bar charts		Group comparisons, signed deltas, grouped-within-grouped designs, stacked composition
Line and longitudinal trends		Time courses, uncertainty ribbons, intervention marks, individual traces
Heatmap

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