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name: nature-figure description: "Create, revise, audit or polish submission-grade manuscript figures for Nature-family and other high-impact journals using Python or R. Covers multi-panel scientific plots, matplotlib/seaborn, ggplot2/patchwork/ComplexHeatmap and journal-ready SVG/PDF/TIFF exports. Also use for OpenRouter GPT Image 2 graphical abstracts, concept or mechanism schematics when the user explicitly requests AI generation. Establish the figure conclusion, evidence logic and exports before execution; honor the chosen or saved plotting backend. Trigger on paper figures, scientific/academic plotting, manuscript data visualization, 论文配图、学术写作配图、科研绘图、科研作图、画图、作图、出图、论文图表、可视化、论文示意图、机制示意图、图形摘要. Not for dashboards or Illustrator/Figma-first infographics." license: Apache-2.0

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 the OpenRouter AI-schematic route

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 and the always_load files.
2. Read references/openrouter-image-generation.md.
3. Use scripts/generate_openrouter_schematic.py when the user wants a real API call or a reproducible payload.
4. 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.

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. 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 backend — a blocking gate

Backend selection blocks plotting tasks, but it should not annoy the same user forever. 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 run scripts/nature_figure_backend.py get. If it returns python or r, use the saved preference.
4. If no saved preference exists, ask exactly one concise question — Python or R? I will remember this as your default. — and stop. 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 static/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 (static/core/contract.md) — write the core conclusion, map the evidence chain, classify the archetype, set the journal/export contract, before any code.
2. Default stance (static/core/stance.md) — archetype-first composition, hero panel, restrained palette, statistics/integrity as part of the figure.
3. Backend fragment — the exclusive Python or R quick-start and execution rule.

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/api.md for the Python palette and 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/figure-delivery-bundle.md for the panel-first build pipeline (draw each panel as its own unit, review at 1:1, then assemble the composite) and for handing a figure over as an auditable bundle (directory layout, canonical version, source-data traceability), references/tutorials.md / references/demos.md for worked examples, references/multipanel-evidence-architecture.md when the figure is an evidence chain rather than a single claim, references/nature-article-requirements.md when the target is the flagship journal Nature, references/asset-adaptation.md before reusing someone else's plotting script, and references/ai-graphical-abstract-workflow.md for AI-assisted graphical abstracts.

6. Run the audit tools before delivery

scripts/ holds dependency-free preflight and audit tools that make the prose rules in references/qa-contract.md executable: validate_figure.py on the source before rendering, then audit_pdf_text.py, audit_figure_collisions.py and audit_panel_alignment.py on the export. They share one exit-code contract: 0 pass, 1 fail, 2 usage or I/O error, 3 not run because a dependency is absent, 4 not auditable. 2, 3 and 4 are not passes. A tool that could not check says so; treating that as success ships an unchecked figure. figure_source_data.py loads the table behind a quantitative panel and records which rows were used and which were excluded; figure_safety.py refuses an interpolation or a label position it cannot compute honestly. See the qa_tooling section of manifest.yaml for when each applies.

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 load only when a step needs them.
• The router itself is short on purpose. Update fragments and references, not this file, when adding scope.
• For figure planning (one claim per figure, panel roles, main-vs-supplement) use the repo's figure-planner skill; for one standalone plot's correctness and legibility rules use the sibling figure-style skill. This skill covers figure production.

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Provenance: adapted from github.com/Yuan1z0825/nature-skills (skill nature-figure, Apache-2.0). The ~30 MB demo/gallery/chart-atlas asset bundle was removed to keep this repo lean; it lives upstream. Beyond asset references and sibling-skill cross-references, this skill's reference layer carries local corrections to the scale regime and the export settings, and its audit scripts carry fixes for defects measured against this repository's own figures. ATTRIBUTION.md in the repository root holds the current file-by-file list, which is checked by tests/test_license_shipping.py.