nature-figure

name nature_figure description Submission-grade Nature/high-impact journal figure workflow for Python or R. Use whenever the user asks to create, revise, audit, or polish manuscript figures, multi-panel scientific plots, figures4papers-style matplotlib plots, or journal-ready SVG/PDF/TIFF outputs, especially for Nature-family or other high-impact journals. Before plotting, define the figure's conclusion, evidence logic, export needs, and review risks. If the user has not chosen Python or R, ask "Python or R?" and stop. Use only the selected backend for figure generation, previewing, exporting, and QA. Supports matplotlib/seaborn and ggplot2/patchwork/ComplexHeatmap. 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, 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. license Unknown metadata {"version":"2.0.0","skill-author":"VenusFactory2 (community contribution)"} Nature Figure Making — Router VenusFactory execution skill_id for read_skill is nature_figure (directory name). Load fragments with read_skill(skill_id="nature_figure", relative_path="manifest.yaml") then the paths listed under always_load / backend maps (e.g. static/core/contract.md ). Response includes skill_root for debugging; prefer relative_path over inventing absolute paths. Exploratory (non-publication) plots may use matplotlib / seaborn skills instead. 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 five steps every time the skill is invoked. 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 everything else. Decide the backend value only from an explicit user choice or a clearly language-specific input file/workflow: python — matplotlib / seaborn. r — ggplot2 / patchwork / ComplexHeatmap. If the user has not explicitly chosen, ask exactly one concise question — Python or R? — and stop. Do not default, guess, generate mock data, or write scripts before the answer. Only recommend a backend when the user explicitly asks you to choose; then use references/backend-selection.md , state the reason, and proceed. Once selected, the backend is exclusive for all drawing, previewing, exporting, and visual QA (see static/core/contract.md ). 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, 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. This structure mirrors nature-writing , nature-polishing , nature-reader , and nature-paper2ppt .