Nature-Style Scientific Figures

4. Build the figure using the loaded material

Apply the loaded material in this order:

1. Figure contract (core/contract.md) — write the core conclusion, map the evidence chain, classify the archetype, set the journal/export contract, before any code.
2. Multi-panel evidence architecture — when planning, restructuring, or auditing a labelled multi-panel figure, load references/multipanel-evidence-architecture.md. Make the figure answer one Results-level scientific question; assign panels different inferential roles, not merely different metrics. When figure order must follow the manuscript argument, also load ../nature-shared/core/nature-results-discussion.md.
3. Default stance (core/stance.md) — archetype-first composition, hero panel, restrained palette, statistics/integrity as part of the figure.
4. Backend fragment — the exclusive Python or R quick-start and execution rule.
5. Template adaptation — when reusing built-in original examples, licensed external material, or user-provided plotting code, load references/asset-adaptation.md before mapping data or changing the script.
6. Rendered QA and delivery preflight — load references/qa-contract.md, run the render-time panel-alignment gate for every multi-panel figure, scripts/validate_figure.py on the plotting source, scripts/audit_pdf_text.py on the exported PDF, and scripts/audit_figure_collisions.py on the same final PDF. Then inspect every panel and the complete figure at final physical size. Automated checks do not replace the panel-by-panel uncertainty, salience, spacing, and ambiguity audit.

For every figure containing two or more comparable panels, measure the final rendered plot-area rectangles before export and preserve the alignment JSON. Python figures must call require_matplotlib_panel_alignment() from scripts/audit_panel_alignment.py after the final layout draw. R/patchwork figures must source scripts/panel_alignment.R, write the patchwork layout manifest at the final export dimensions, and run the same backend-neutral JSON auditor. Use a default physical tolerance of 1.5 pt for shared edges, widths, heights, panel-label anchors and repeated gutters. FIX BEFORE DELIVERY or exit code 1 blocks export; NOT AUDITABLE or exit code 2 blocks any claim that alignment passed. A horizontal row of three or four equal-grid-span panels must have equal final plot-area widths as well as equal heights and gutters; an intentional unequal-width design requires a recorded panel-width exemption. Structured unequal-span grids—including two stacked panels beside one panel spanning both rows, in either column—must be inferred from shared grid start/stop boundaries and checked automatically. Nested grids, free-positioned hero panels, insets and colorbars may be excluded only through explicit comparable groups or a recorded exemption with a reason. Do not weaken the global tolerance to hide one intentional exception.

After every generated or revised Python/R scientific figure, export the final PDF and run the collision audit again; this is mandatory after any change to data geometry, text, fonts, legends, annotations, axes, error bars, panel size or layout, not only at final submission. Use:

python skills/nature-figure/scripts/audit_figure_collisions.py figure.pdf \
  --json-out figure.collision-audit.json \
  --overlay-pdf figure.collision-audit.pdf

• FIX BEFORE DELIVERY or exit code 1: repair the figure, re-export with the selected plotting backend, and rerun all rendered QA.
• REVIEW REQUIRED: inspect every WARN at final physical size; record why an intentional overlay is acceptable. Use --strict when WARN must block.
• NOT AUDITABLE or exit code 2: report the dependency/PDF blocker and do not claim collision validation. Install requirements.txt when PyMuPDF is absent.

The collision audit reads PDF geometry for both Python and R output. It does not redraw the scientific figure or authorize cross-backend plotting. Its optional marked PDF is a QA-only diagnostic artifact and must never replace the selected backend's source or submission files.

When the target is the flagship journal Nature, also load references/nature-article-requirements.md. It separates initial-review files from accepted-in-principle main and Extended Data production contracts and owns the flagship legend limit.

When the target is Nature Machine Intelligence, instead load ../nature-shared/journal-formats/nature-machine-intelligence.md. Apply its combined six-item main display budget, ten-item Extended Data maximum, initial-versus-production boundary, 300-dpi/180-mm production checks and source- data contract. NMI's current live pages do not assign a standalone per-legend number, but its official 2018 brief guide set a historical advisory ceiling of fewer than 300 English words per complete figure legend. Count the whole legend, not each panel; aim for 150–250 words and keep it below 300 unless the live submission system or editor gives a newer instruction. Do not import flagship Nature's limit.

The chart serves the scientific logic; aesthetic polish is subordinate to making the core conclusion clear, defensible, and reviewable.

5. Reach for references only when needed

The files under references/ are deep references, not defaults. Open them on demand per the references.on_demand table in the manifest — for example references/figure-contract.md to build the contract, references/multipanel-evidence-architecture.md to turn one Results-level question into complementary panel roles and a claim-escalating figure sequence, references/asset-adaptation.md to reuse a plotting template safely, references/template-catalog.md for validated Python CSV templates, references/api.md for the Python palette and numerical/layout safety helpers, references/r-workflow.md for R, references/design-theory.md for color/typography/export rationale, references/common-patterns.md and references/chart-types.md for layout/chart recipes, references/nature-2026-observations.md for real Nature page archetypes, references/qa-contract.md before final delivery, references/nature-article-requirements.md for exact flagship Nature stage and upload rules, ../nature-shared/journal-formats/nature-machine-intelligence.md for exact NMI figure rules, references/ai-graphical-abstract-workflow.md for AI-assisted graphical-abstract planning, policy gating, human verification, and provenance, and references/tutorials.md / references/demos.md for worked examples.

Do not infer flagship Nature or NMI requirements from a Nature Communications corpus or from the visual-style examples in this skill.