Nature Figure
Plan and render Nature-family–ready multi-panel figures in Python or R with claim-first logic, export targets, and submission QA.
Overview
nature-figure is an agent skill most often used in Build (also Ship) that produces submission-grade Nature-style scientific figures from a claim-first Python or R workflow.
Install
npx skills add https://github.com/yuan1z0825/nature-skills --skill nature-figureWhat is this skill?
- Claim-first workflow: conclusion, evidence hierarchy, export needs, and review risks before plotting
- Backend gate: ask Python or R once and use only that stack for generate, preview, export, and QA
- matplotlib/seaborn and ggplot2/patchwork/ComplexHeatmap/ggrepel/svglite/cairo_pdf/ragg support
- figures4papers-style matplotlib discipline and unified color families across panels
- Explicitly excludes dashboards and Illustrator/Figma-first infographic pipelines
Adoption & trust: 2.5k installs on skills.sh; 17.8k GitHub stars; 3/3 security scanners passed (skills.sh audits).
What problem does it solve?
You have results to publish but your plots are pretty panels without a clear claim, consistent styling, or journal-safe exports.
Who is it for?
Academic or R&D solo builders preparing multi-panel paper figures with strict journal export and color discipline.
Skip if: Business dashboards, marketing infographics built first in Figma/Illustrator, or users who refuse to pick Python or R.
When should I use this skill?
User asks to create, revise, audit, or polish Nature/high-impact manuscript figures, figures4papers-style plots, or journal-ready SVG/PDF/TIFF in Python or R.
What do I get? / Deliverables
You get a reviewed figure plan, backend-consistent code, and Nature-oriented SVG/PDF/TIFF outputs with QA checks on evidence logic and visual risk.
- Figure concept with conclusion and panel hierarchy
- Backend-consistent plot code and exports
- Pre-submission visual QA notes
Recommended Skills
Journey fit
Spans multiple journey phases - primary shelf plus alternate fits below.
Manuscript figures are primary Build → docs deliverables for research products; the skill governs how plots become citable visual arguments. It targets publication docs (SVG/PDF/TIFF), not app dashboards—subphase docs captures scientific writing outputs.
Where it fits
Draft a four-panel mechanism figure with shared method colors before writing the results section.
Audit an existing PDF for weak evidence hierarchy and mis-sized axis labels before resubmission.
Export ComplexHeatmap and ggplot2 panels to TIFF at journal DPI using the locked R backend.
How it compares
A manuscript figure workflow skill—not a charting library cheat sheet and not a general UI design kit.
Common Questions / FAQ
Who is nature-figure for?
Researchers and technical founders writing high-impact papers who want Claude, Cursor, or Codex to enforce journal figure logic and exports.
When should I use nature-figure?
In Build docs while drafting figures; in Ship review when auditing panels before submission; anytime the user mentions Nature, figures4papers, or journal-ready SVG/PDF/TIFF.
Is nature-figure safe to install?
It is procedural plotting guidance without bundled binaries; review the Security Audits panel on this page and run generated code in your own environment.
SKILL.md
READMESKILL.md - Nature Figure
# Nature Figure Making Skill A guide for producing publication-quality scientific figures as a visual argument, not as isolated pretty plots. Every figure starts from a claim, an evidence hierarchy, and a review-risk check before code or aesthetics. The older Python/matplotlib rules in this skill remain valid. The skill now also supports R, especially `ggplot2 + patchwork + ComplexHeatmap + ggrepel + svglite/cairo_pdf + ragg`. If the user provides a private plotting template collection, use it only as an internal adaptation source and do not reveal its path, filenames, or provenance in user-facing output. Color policy: prefer **unified method families across all panels** over maximal hue separation. For dense Nature Machine Intelligence-style figure pages, use the low-saturation `NMI pastel` family described in `references/api.md` and reserve green/red mainly for gains, drops, and other directional cues. ## First move: figure contract before plotting Before generating or editing code, establish the contract below. **Backend selection is a blocking gate.** If the user has not explicitly chosen Python or R in the current request or provided a clearly language-specific input file/workflow, ask one concise question: **Python or R?** Then stop and wait for the user's answer. Do not generate mock data, write scripts, create figures, or choose Python/R by default. This overrides general autonomy/default-execution behavior for figure tasks. **The selected backend is exclusive for all figure generation.** Once Python or R is selected, every plotting script, preview image, SVG/PDF/TIFF/PNG export, QA render, and visual workaround must be produced by that same backend. Do not use Python to draw a preview for an R figure, and do not use R to draw a preview for a Python figure, even if the selected runtime or packages are missing locally. The non-selected language may only be used for non-visual file inspection or data conversion when it does not open a graphics device, import plotting libraries, create image/vector files, or change the final visual appearance. **Missing runtime/package rule.** After the backend is selected, check the selected runtime early (`Rscript`/R for R; Python and required plotting packages for Python). If the selected runtime or required packages are unavailable, stop before rendering and report the exact blocker. You may provide a selected-backend script and installation commands, or ask permission to install dependencies, but you must not fall back to the other language to make a substitute figure. Only recommend a backend when the user explicitly asks you to choose or recommend one. In that case, use `references/backend-selection.md`, state the reason, and then proceed with the recommended backend. 1. Core conclusion: write the one-sentence claim the figure must defend. 2. Evidence chain: map each planned panel to the claim, and drop panels that do not carry a unique piece of evidence. 3. Archetype: classify the figure as `quantitative grid`, `schematic-led composite`, `image plate + quant`, or `asymmetric mixed-modality figure`. 4. Backend: use the selected Python or R track exclusively for all figure drawing, previewing, exporting, and visual QA. Do not c