Create, revise, audit, and export submission-grade scientific figures for Nature-family and other high-impact venues in Python (matplotlib/seaborn) or R (ggplot2/patchwork/ComplexHeatmap), including multi-panel plots, figures4papers-style work, and journal-ready SVG/PDF/TIFF outputs. Use for paper o

Nature Figure Making — Router This skill is split into two layers: A static layer under static/ that holds versioned, reusable content fragments (the figure contract and default stance, plus a per backend quick start for Python and R). A dynamic layer (this file plus manifest.yaml ) that detects the plotting backend and loads only the fragment needed for the current job. The large design, API, pattern, and QA material lives in on demand references. Do not try to apply the figure logic from memory or from this router. Always load fragments from disk as described below. Routing protocol Follow these steps every time the skill is invoked. 0. Check for graphical abstract and AI schematic routes For every graphical abstract planning, generation, revision, or audit task that uses AI, read [references/ai graphical abstract workflow.md](references/ai graphical abstract workflow.md) first. It owns the message/audience brief, composition and palette workflow, policy gate, human scientific review, disclosure boundary, and provenance requirements. A Nature Careers article is practitioner advice, not submission clearance; verify the current official policy for the exact target journal. If the request is planning or auditing only, do not ask for Python or R unless the user also asks to render or revise a data driven figure. If the user explicitly asks to generate a manuscript schematic, graphical abstract, mechanism diagram, concept illustration, or paper schematic with OpenRouter, GPT Image 2, an image generation API, or similar wording, do not ask "Python or R?". This is a non plotting AI schematic route. For this route: 1. Read [manifest.yaml](manifest.yaml) and the always load files. 2. Read [references/ai graphical abstract workflow.md](references/ai graphical abstract workflow.md). 3. Read [references/openrouter image generation.md](references/openrouter image generation.md). 4. Use [scripts/generate openrouter schematic.py](scripts/generate openrouter schematic.py) when the user wants a real API call or a reproducible payload. 5. Treat output as a draft schematic / graphical abstract, not as a quantitative data panel. Do not invent experimental values, author logos, institutional marks, or unsupported mechanisms. Keep internal usefulness separate from submission eligibility. Only continue to the Python/R backend gate for plotting, charting, data visualization, or manuscript figure assembly tasks that are not explicit OpenRouter AI image generation requests. 1. Load the manifest and the core layer Read [manifest.yaml](manifest.yaml). It declares the backend axis, the allowed values, and the file paths each value maps to. Also read every file listed under always load ( static/core/contract.md and static/core/stance.md ). These hold the figure contract, the backend gate, the missing runtime rule, the privacy rule, and the default operating stance that apply to every figure job. 2. Resolve the plotting backend Backend selection applies only to rendering or editing plotting code. Reuse a choice already established in the same task and its follow ups; do not ask again merely because a new message omits the language. Read only figure review and backend independent data inspection may proceed without this choice. If the backend remains unresolved, retain the one time Python/R question and pause only dependent plotting steps. Explicit approval requirements and backend exclusivity remain in force. Resolve the plotting backend from the current task before consulting the saved default. Decide the backend value in this order: 1. If the current request explicitly chooses Python or R, use that backend and save it with scripts/nature figure backend.py set python or scripts/nature figure backend.py set r . 2. If the request provides a clearly language specific input file/workflow, use that backend and save it. 3. Otherwise reuse a Python/R choice already established in this task. If none exists, run scripts/nature figure backend.py get and use a returned python or r preference. 4. If neither a task choice nor a saved preference exists, ask exactly one concise question — Python or R? I will remember this as your default. — and pause only dependent plotting steps. After the user answers, save the answer before proceeding. python — matplotlib / seaborn. r — ggplot2 / patchwork / ComplexHeatmap. Do not guess or choose a backend by aesthetics alone. Only recommend a backend when the user explicitly asks you to choose; then use references/backend selection.md , state the reason, save the selected backend, and proceed. Once selected, the backend is exclusive for all drawing, previewing, exporting, and visual QA (see core/contract.md ). This gate does not apply to the explicit OpenRouter AI schematic route above. 3. Load the matching backend fragment After the backend is resolved, Read the mapped fragment ( static/fragments/backend/python.md or static/fragments/backend/r.md ). It carries the backend only execution rule and the publication quick start (rcParams/theme and export helper). Do not load the other backend's fragment. 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: 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. Why this split The static layer is versioned and reviewable. The backend gate is now explicit in the manifest rather than buried in prose. The dynamic layer keeps each invocation cheap: only the selected backend's quick start enters context, and the 2,600+ lines of reference depth

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