Ieee Figure Table
CloudWave818/ieee-skills
Audit, redesign, generate, and improve IEEE manuscript figures, tables, captions, result presentation, plotting scripts, visual polish, hybrid Python/R plus vector-editor workflows…
Submission-grade Nature/high-impact journal figure workflow for Python or R.
$ npx skills add Citrus-bit/Anaxa --skill nature-figure -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install Citrus-bit/Anaxa nature-figure --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ 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-srcUse ~/.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/
Install the "nature-figure" agent skill from https://github.com/Citrus-bit/Anaxa/tree/main/skills/public/nature-figure into .claude/skills/nature-figure/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "nature-figure", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/Citrus-bit/Anaxa/tree/main/skills/public/nature-figureType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add Citrus-bit/Anaxa --skill nature-figure -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install Citrus-bit/Anaxa nature-figure --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Citrus-bit/Anaxa.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/public/nature-figure .agents/skills/nature-figure && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "nature-figure" agent skill from https://github.com/Citrus-bit/Anaxa/tree/main/skills/public/nature-figure into .agents/skills/nature-figure/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "nature-figure", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add Citrus-bit/Anaxa --skill nature-figure -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install Citrus-bit/Anaxa nature-figure --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Citrus-bit/Anaxa.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/public/nature-figure .cursor/skills/nature-figure && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "nature-figure" agent skill from https://github.com/Citrus-bit/Anaxa/tree/main/skills/public/nature-figure into .cursor/skills/nature-figure/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "nature-figure", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/Citrus-bit/Anaxa.git --path skills/public/nature-figure--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add Citrus-bit/Anaxa --skill nature-figure -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install Citrus-bit/Anaxa nature-figure --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Citrus-bit/Anaxa.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/public/nature-figure .gemini/skills/nature-figure && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "nature-figure" agent skill from https://github.com/Citrus-bit/Anaxa/tree/main/skills/public/nature-figure into .gemini/skills/nature-figure/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "nature-figure", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install Citrus-bit/Anaxa nature-figureInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add Citrus-bit/Anaxa --skill nature-figure -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/Citrus-bit/Anaxa.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/public/nature-figure .github/skills/nature-figure && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "nature-figure" agent skill from https://github.com/Citrus-bit/Anaxa/tree/main/skills/public/nature-figure into .github/skills/nature-figure/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "nature-figure", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add Citrus-bit/Anaxa --skill nature-figure -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install Citrus-bit/Anaxa nature-figure --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Citrus-bit/Anaxa.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/public/nature-figure .opencode/skills/nature-figure && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "nature-figure" agent skill from https://github.com/Citrus-bit/Anaxa/tree/main/skills/public/nature-figure into .opencode/skills/nature-figure/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "nature-figure", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
nature-figureSubmission-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. 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.
5 steps, taken from the first numbered list in SKILL.md.
Read from SKILL.md and the folder at commit d57c708. It shows what the files ask for, not the result of running them.
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.
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.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
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.
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.
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.
The full file from Citrus-bit/Anaxa at commit d57c708, republished under its MIT licence (© Citrus-bit). 1,087 words, ~2,730 tokens.
.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.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.
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.
quantitative grid, schematic-led composite,
image plate + quant, or asymmetric mixed-modality figure.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.
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.
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.
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.
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()
}quantitative grid, schematic-led composite, image plate + quant, or asymmetric mixed-modality figure.n, error-bar definitions, source-data traceability, and image-integrity notes as part of the figure,
not as optional caption cleanup.Nature style rather than ML/NMI-specific style, read references/nature-2026-observations.md before choosing layout.| File | Open when |
|---|---|
| references/figure-contract.md | Need to convert a user request into core conclusion, evidence hierarchy, panel map, and review-risk checks |
| references/backend-selection.md | User has not chosen Python/R, asks for a recommendation, or a mixed Python/R workflow is possible |
| references/r-workflow.md | User chooses R or provides R scripts/templates/data |
| references/r-template-index.md | Need to adapt a user-provided or private R template collection without exposing source paths |
| references/qa-contract.md | Before final delivery, revision package, microscopy/blot figure, or journal-specific audit |
| references/design-theory.md | Typography, color theory, layout rationale, export policy |
| references/api.md | Python PALETTE, helper function signatures, validation rules |
| references/common-patterns.md | Python layout patterns: hero panels, legend-only axes, dark image plates, asymmetric layouts |
| references/nature-2026-observations.md | Real Nature page archetypes: schematic-led composites, dark image plates, clinical triptychs, asymmetric hero layouts |
| references/tutorials.md | End-to-end walkthroughs: bars, trends, heatmaps |
| references/chart-types.md | Radar, 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
SKILL.md and 32 other files (references, assets) in skills/public/nature-figure of Citrus-bit/Anaxa.
Open the folder on GitHubat commit d57c708
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.
Nature Figure next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Nature Figure this skillCitrus-bit/Anaxa | 120 | 2 repos | ~2.7k | Automated safety check: Pass | MIT | |
| Ieee Figure TableCloudWave818/ieee-skills | 359 | — | ~1k | Automated safety check: Pass | MIT | |
| Nature FigureNeuroAIHub/BrainPilot | 1.1k | 1 repos | ~1.3k | Automated safety check: Pass | AGPL-3.0 | |
| Nature FigureTai609/NebulaMat | 100 | — | ~1.9k | Automated safety check: Pass | Custom licence | |
| CJK Font Setup for Plotsxjtulyc/MedgeClaw | 617 | 1 repos | ~1.3k | Automated safety check: Pass | None | |
| Release Evidence WorkflowAli-Marandi/ClimateDataAnalyzer | 107 | — | ~1.6k | Automated safety check: Pass | MIT |
CloudWave818/ieee-skills
Audit, redesign, generate, and improve IEEE manuscript figures, tables, captions, result presentation, plotting scripts, visual polish, hybrid Python/R plus vector-editor workflows…
NeuroAIHub/BrainPilot
Submission-grade Nature/high-impact journal figure workflow for Python or R.
Tai609/NebulaMat
Create, revise, audit, and export submission-grade scientific figures for Nature-family and other high-impact venues in Python (matplotlib/seaborn) or R (ggplot2/patchwork/ComplexHeatmap), including…
xjtulyc/MedgeClaw
Detects a usable Chinese, Japanese or Korean font and configures matplotlib so chart labels, titles and legends render instead of showing empty boxes.
Ali-Marandi/ClimateDataAnalyzer
Build an auditable release-evidence workflow for a desktop or packaged application.
MrLee2R/Intelligrapher
科研绘图智能助手。当用户需要科研绘图、数据可视化、配色建议、期刊风格调整、生成 matplotlib 或 seaborn 绘图代码、或询问某专业领域图表规范时触发。支持多领域与顶刊审美,输出可直接运行的 Python 脚本。
Citrus-bit/Anaxa
A skill your agent uses whenever the user wants a chart, graph, plot, dashboard visual, or asks to visualize structured numbers, trends, comparisons, proportions, distributions, correlations…
Citrus-bit/Anaxa
Interact with MedrixFlow AI agent platform via its HTTP API.
Citrus-bit/Anaxa
A skill your agent uses when the user wants to create any technical diagram - architecture, data flow, flowchart, sequence, agent/memory, or concept map - and export as SVG+PNG.
Citrus-bit/Anaxa
Generate a personalized SOUL.md through a warm, adaptive onboarding conversation.
Citrus-bit/Anaxa
A skill your agent uses for general web research that needs current online information, multiple source angles, and synthesis, when no more specific research skill applies.
Citrus-bit/Anaxa
A skill your agent uses for empirical social-science research, applied economics, public policy, education, finance, management, sociology, psychology, epidemiology, or public-health data studies.
Categories
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.
Nature Figure fits situations like: the user asks to create; polish manuscript figures; multi-panel scientific plots; journal-ready SVG/PDF/TIFF outputs.
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.
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.
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.
SKILL.md names no scripts, command-line tools or credentials: Nature Figure is instructions for the agent only. Our summary lists: Python 3.
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.
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.
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.
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.
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.
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.