SKILL.md

$28

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.

Core conclusion: write the one-sentence claim the figure must defend.

Evidence chain: map each planned panel to the claim, and drop panels that do not carry

a unique piece of evidence.

Archetype: classify the figure as quantitative grid, schematic-led composite,

image plate + quant, or asymmetric mixed-modality figure.

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.

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".