| name | nature-figure |
| description | Submission-grade Nature/high-impact journal figure workflow for Python or R, plus optional OpenRouter GPT Image 2 manuscript schematic generation when the user explicitly asks for AI-generated graphical abstracts, concept schematics, mechanism diagrams, or paper schematic illustrations. Use whenever the user asks to create, revise, audit, or polish manuscript figures, multi-panel scientific plots, figures4papers-style matplotlib plots, journal-ready SVG/PDF/TIFF outputs, or OpenRouter/API-generated schematic drafts, especially for Nature-family or other high-impact journals. Before plotting or image generation, define the figure's conclusion, evidence logic, export needs, and review risks. For plotting tasks, honor an explicit Python/R choice, otherwise reuse the saved nature-figure backend preference; if no preference exists, ask once whether the user prefers Python or R and save that answer for future calls. For explicit OpenRouter/GPT Image 2 schematic generation, do not ask Python or R; use the AI-schematic route. Supports matplotlib/seaborn, ggplot2/patchwork/ComplexHeatmap, and OpenRouter Images API drafts. Not for dashboards or Illustrator/Figma-first infographics. Also trigger on general academic-writing figure needs even without the word "Nature", such as making figures/plots for a paper, scientific/academic plotting, data visualization for a manuscript, AI-generated paper schematics, and Chinese phrasings like 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. |
| 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:
- Read manifest.yaml and the
always_load files.
- Read references/openrouter-image-generation.md.
- Use scripts/generate_openrouter_schematic.py when the user wants a real API call or a reproducible payload.
- 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:
- 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.
- If the request provides a clearly language-specific input file/workflow, use that backend and save it.
- Otherwise run
scripts/nature_figure_backend.py get. If it returns python or r, use the saved preference.
- 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:
- 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.
- Default stance (
static/core/stance.md) — archetype-first composition, hero panel, restrained palette, statistics/integrity as part of the figure.
- 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), and references/tutorials.md / references/demos.md for worked examples.
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.
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. Only asset references and sibling-skill cross-references were changed; the figure-making logic is unmodified.
