nature-figure

name	nature-figure
description	Submission-grade Nature/high-impact journal figure workflow for Python or R. Use whenever the user asks to create, revise, audit, or polish manuscript figures, multi-panel scientific plots, or journal-ready SVG/PDF/TIFF outputs, especially for Nature-family or other high-impact journals. Before plotting, define the figure's conclusion, evidence logic, export needs, and review risks. If the user has not chosen Python or R, ask "Python or R?" and stop. Use only the selected backend for figure generation, previewing, exporting, and QA. Supports matplotlib/seaborn and ggplot2/patchwork/ComplexHeatmap. Not for dashboards or Illustrator/Figma-first infographics.
skill_role	companion

Nature Figure Making Skill

This companion skill is adapted from Yuan1z0825/nature-skills/tree/main/nature-figure. See UPSTREAM_LICENSE.txt for the upstream MIT license.

DeepScientist integration

• Follow the shared interaction contract injected by the system prompt.
• Use this for Nature-family or other high-impact journal figure work when the figure itself is a submission-grade deliverable, especially multi-panel or journal-export work.
• Keep paper-plot as the faster default for simple structured bar, line, scatter, or radar figures from measured data; use nature-figure when the venue/export/review contract is the main constraint.
• Keep figure-polish available for final render-inspect-revise checks when a figure already exists and the remaining issue is local readability or surface quality.
• Respect this skill's Python/R backend gate even in autonomous mode.

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 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", "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