Agent skill · creative production · aiskillstore
nature-figure
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.
Why this skill is useful
Adds a structured workflow for creating high-impact journal figures in Python or R, ensuring adherence to specific academic standards and export requirements.
What it needs
About 3k tokens when loaded. Last updated 2026-08-07. 405 stars on the source repository.
What this skill does
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 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 alwaysload (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 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). …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aiskillstore/nature-figure