Agent skill

Nature Figure

by Citrus-bit in Citrus-bit/Anaxa

Submission-grade Nature/high-impact journal figure workflow for Python or R.

MITAuto-check passedData & Analytics

Install Nature Figure

skills CLI
$ npx skills add Citrus-bit/Anaxa --skill nature-figure -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install Citrus-bit/Anaxa nature-figure --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/Citrus-bit/Anaxa.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/public/nature-figure .claude/skills/nature-figure && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
nature-figure
GitHub stars
120
Used in
2 other repos
Token cost
~2.7k tokens
SKILL.md length
1,087 words
Files
33 (incl. references, assets)
Skills in repo
15
Repo updated
First seen
Licence
MIT

At a glance

Submission-grade Nature/high-impact journal figure workflow for Python or R.

  • Works in 5 steps: Core conclusion: write the one-sentence… → Evidence chain: map each planned panel… → Archetype: classify the figure as… → …
  • The user asks to create
  • SKILL.md covers First move: figure contract…, User-facing privacy rule, Python quick-start and R quick-start, plus 4 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Nature Figure is an agent skill from Citrus-bit/Anaxa. 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, 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…

Its SKILL.md is about 2.7k tokens, which your agent loads only when the skill is triggered. The skill folder holds 35 other files, including reference files and assets (for example `README.md`).

It sits in Data & Analytics, covering Data visualization. It works with Python, Matplotlib, Adobe Illustrator and Figma. The repository describes itself as: Anaxa 是一个面向科研工作流的开源智能体系统。它不是单纯的聊天机器人,也不是无人监管的自动发论文机器,而是把文献检索、证据审计、实验执行、论文写作、同行评审式检查和最终产物打包放进同一个可追踪的研究生命周期中。 The licence is MIT.

When your agent uses it

  • The user asks to create
  • Polish manuscript figures
  • Multi-panel scientific plots
  • Journal-ready SVG/PDF/TIFF outputs

Example prompts

  • “Python or R?”
  • “/nature-figure”

Requirements

  • Python 3

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

  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
  3. Archetype: classify the figure as quantitative grid, schematic-led composite,
  4. Backend: use the selected Python or R track exclusively for all figure drawing,
  5. Journal/export contract: set final dimensions, editable text, source data, statistics,

What it can do on your machine

Read from SKILL.md and the folder at commit d57c708. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are python and r).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Nature Figure loads about 2.7k tokens when it runs, and up to ~24k if it reads all its reference files. Until then it costs about 170 tokens; SKILL.md has 1,087 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~170
When it runs · the whole SKILL.md, loaded when a task matches
~2.7k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~24k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from Citrus-bit/Anaxa at commit d57c708, republished under its MIT licence (© Citrus-bit). 1,087 words, ~2,730 tokens.

Download SKILL.mdSave it as .claude/skills/nature-figure/SKILL.md (or your agent's skills folder). This skill also uses 32 other files; get the full folder from GitHub.
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, 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.

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 cross-render with the other language.
  5. Journal/export contract: set final dimensions, editable text, source data, statistics, image-integrity notes, and export formats before styling.

The highest-priority rule is: the chart serves the scientific logic. Aesthetic polish, template matching, and complex layout are subordinate to making the core conclusion clear, defensible, and reviewable.

User-facing privacy rule

Do not disclose private local paths, private filenames, chat-attachment names, internal reference filenames, template identifiers, or the provenance of private working materials in user-facing replies, generated code comments, figure legends, reports, or manuscript text. Use generic descriptions such as "the provided R template collection", "a private working draft", or "the internal figure contract". Only reveal an exact path or source file when the user explicitly asks for that audit trail.

Show full SKILL.md (482 more words)Show less

Python quick-start

Python-only execution rule. When the user has selected Python, do all figure drawing, previewing, exporting, and visual QA in Python. Do not call R/ggplot2, ComplexHeatmap, patchwork, or any R graphics device to create a temporary preview, fallback export, or layout approximation. If Python or required Python plotting packages are missing, stop before rendering and report the missing dependency. You may still write the Python script, provide pip/environment install commands, or ask permission to install dependencies, but do not cross-render the figure in R.

python
import matplotlib as mpl
import matplotlib.pyplot as plt

mpl.rcParams.update({
    "font.family": "sans-serif",
    "font.sans-serif": ["Arial", "Helvetica", "DejaVu Sans", "sans-serif"],
    "svg.fonttype": "none",     # editable text in SVG
    "pdf.fonttype": 42,         # editable TrueType text in PDF
    "font.size": 7,             # use 15-24 only for large slide-sized panels
    "axes.spines.right": False,
    "axes.spines.top": False,
    "axes.linewidth": 0.8,
    "legend.frameon": False,
})

def save_pub_py(fig, filename, dpi=600):
    fig.savefig(f"{filename}.svg", bbox_inches="tight")
    fig.savefig(f"{filename}.pdf", bbox_inches="tight")
    fig.savefig(f"{filename}.tiff", dpi=dpi, bbox_inches="tight")

Use text.usetex = True only when LaTeX is installed and math-rich labels are required.

R quick-start

r
library(ggplot2)
library(patchwork)

theme_set(
  theme_classic(base_size = 6.5, base_family = "Arial") +
    theme(
      axis.line = element_line(linewidth = 0.35, colour = "black"),
      axis.ticks = element_line(linewidth = 0.35, colour = "black"),
      legend.title = element_text(size = 6.2),
      legend.text = element_text(size = 5.8),
      strip.text = element_text(size = 6.2, face = "bold"),
      plot.title = element_text(size = 7, face = "bold"),
      panel.grid = element_blank()
    )
)

save_pub_r <- function(plot, filename, width_mm = 183, height_mm = 120, dpi = 600) {
  w <- width_mm / 25.4
  h <- height_mm / 25.4
  svglite::svglite(paste0(filename, ".svg"), width = w, height = h)
  print(plot)
  dev.off()
  grDevices::cairo_pdf(paste0(filename, ".pdf"), width = w, height = h, family = "Arial")
  print(plot)
  dev.off()
  ragg::agg_tiff(paste0(filename, ".tiff"), width = w, height = h, units = "in", res = dpi)
  print(plot)
  dev.off()
}

Default operating stance

  • Start by classifying the requested figure into one of four archetypes: quantitative grid, schematic-led composite, image plate + quant, or asymmetric mixed-modality figure.
  • Prefer one hero panel plus subordinate evidence panels over filling the canvas with equal-sized subplots.
  • If the user asks for a single chart, still identify its role in the manuscript claim: discovery, mechanism, validation, comparison, robustness, or clinical/biological relevance.
  • Keep the background white for plots and diagrams; switch to black only for microscopy / volume-rendering image plates.
  • Prefer direct labels over legends when categories are spatially fixed or the legend would force unnecessary eye travel.
  • Keep one restrained palette per figure: usually one neutral family, one signal family, and one accent family.
  • Treat statistics, n, error-bar definitions, source-data traceability, and image-integrity notes as part of the figure, not as optional caption cleanup.
  • When the user asks for broad Nature style rather than ML/NMI-specific style, read references/nature-2026-observations.md before choosing layout.

When to load this skill

  • Python or R figures for papers, slides, or reports targeting Nature, Science, Cell, NeurIPS, ICLR, or similar venues.
  • Requests involving grouped bars, trend lines, heatmaps, radar plots, multi-panel grids, or PDF/SVG/high-DPI output.
  • Any mention of "Nature style", "publication figure", "paper figure", "SCI figure", "R plotting template", or "high-quality scientific plot".
  • Requests to improve a figure's logic, aesthetics, panel layout, figure legend, export quality, or journal-readiness.

When NOT to load

  • Plotly, Altair, Bokeh, or other interactive/web-first plotting.
  • EDA-only plots without a publication target.
  • Primary workflow is 3D, GIS, or non-scientific illustration tooling.
  • Illustrator / Figma–first layout.
FileOpen when
references/figure-contract.mdNeed to convert a user request into core conclusion, evidence hierarchy, panel map, and review-risk checks
references/backend-selection.mdUser has not chosen Python/R, asks for a recommendation, or a mixed Python/R workflow is possible
references/r-workflow.mdUser chooses R or provides R scripts/templates/data
references/r-template-index.mdNeed to adapt a user-provided or private R template collection without exposing source paths
references/qa-contract.mdBefore final delivery, revision package, microscopy/blot figure, or journal-specific audit
references/design-theory.mdTypography, color theory, layout rationale, export policy
references/api.mdPython PALETTE, helper function signatures, validation rules
references/common-patterns.mdPython layout patterns: hero panels, legend-only axes, dark image plates, asymmetric layouts
references/nature-2026-observations.mdReal Nature page archetypes: schematic-led composites, dark image plates, clinical triptychs, asymmetric hero layouts
references/tutorials.mdEnd-to-end walkthroughs: bars, trends, heatmaps
references/chart-types.mdRadar, 3D sphere, fill_between, scatter patterns

© Citrus-bit, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 32 other files (references, assets) in skills/public/nature-figure of Citrus-bit/Anaxa.

  • SKILL.md
  • .gitignore
  • README.md
  • THIRD_PARTY_LICENSE.txt
  • assets/chart-atlas/atlas-01-bar-charts.png
  • assets/chart-atlas/atlas-02-line-trends.png
  • assets/chart-atlas/atlas-03-heatmaps.png
  • assets/chart-atlas/atlas-04-scatter-bubble.png
  • assets/chart-atlas/atlas-05-radar-polar.png
  • assets/chart-atlas/atlas-06-distributions.png
  • assets/chart-atlas/atlas-07-forest-interval.png
  • assets/chart-atlas/atlas-08-area-stacked.png
  • assets/chart-atlas/atlas-09-image-plates.png
  • assets/chart-atlas/atlas-10-network-matrix.png
  • assets/gallery/fig1-material-mechanism-rich.png
  • assets/gallery/fig2-spatial-imaging-rich.png
  • assets/gallery/fig3-in-vivo-efficacy-rich.png
  • assets/gallery/fig4-single-cell-systems-rich.png
  • … and 15 more

Open the folder on GitHubat commit d57c708

Used in 2 other repositories

We found 2 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 2 other GitHub owners. This page covers the copy in Citrus-bit/Anaxa, which our catalogue first saw on October 7, 2026.

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Release Evidence WorkflowAli-Marandi/ClimateDataAnalyzer107—~1.6kAutomated safety check: PassMIT

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Questions about Nature Figure

What does Nature Figure do?

Submission-grade Nature/high-impact journal figure workflow for Python or R. Nature Figure is an agent skill from Citrus-bit/Anaxa. Submission-grade Nature/high-impact journal figure workflow for Python or R.

When should I use Nature Figure?

Nature Figure fits situations like: the user asks to create; polish manuscript figures; multi-panel scientific plots; journal-ready SVG/PDF/TIFF outputs.

How do I install Nature Figure in Claude Code?

Run `npx skills add Citrus-bit/Anaxa --skill nature-figure -a claude-code`. Or copy the skill folder (skills/public/nature-figure in Citrus-bit/Anaxa) into .claude/skills/nature-figure in your project. Claude Code loads it when a task matches its description.

How do I install Nature Figure in Codex?

Run `npx skills add Citrus-bit/Anaxa --skill nature-figure -a codex`. Or copy the skill folder (skills/public/nature-figure in Citrus-bit/Anaxa) into .agents/skills/nature-figure in your project. Codex loads it when a task matches its description.

Can I use Nature Figure in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add Citrus-bit/Anaxa --skill nature-figure -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/nature-figure, .gemini/skills/nature-figure, .github/skills/nature-figure and .opencode/skills/nature-figure in your project.

What does Nature Figure need to run?

SKILL.md names no scripts, command-line tools or credentials: Nature Figure is instructions for the agent only. Our summary lists: Python 3.

Does Nature Figure access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Nature Figure safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Nature Figure use?

Nature Figure is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Nature Figure use?

About 2.7k tokens (SKILL.md is roughly 11k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 21k tokens, read only when the agent opens those files.

What are the alternatives to Nature Figure?

Skills that share tags, products or a category with Nature Figure: Ieee Figure Table (CloudWave818/ieee-skills, 359 stars), Nature Figure (NeuroAIHub/BrainPilot, 1.1k stars), Nature Figure (Tai609/NebulaMat, 100 stars) and CJK Font Setup for Plots (xjtulyc/MedgeClaw, 617 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Nature Figure?

Citrus-bit (a GitHub user) maintains it in Citrus-bit/Anaxa, which has 120 GitHub stars. The repository holds 15 skills in this directory. The repository was last updated on September 7, 2026.

Source: Citrus-bit/Anaxa on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.