Scientific Figure Making
ChenLiu-1996/figures4papers
Covers publication-ready matplotlib figures for academic papers, slides, and reports—bars, trends, scatter, heatmaps, and multi-panel layouts—with this…
A skill your agent uses when selecting, designing, generating or reviewing scientific figures, complex modeling charts, paper illustrations or image2-assisted visuals.
Install Mathodology Figure Presets
$ npx skills add sweetcornna/mathodology --skill mathodology-figure-presets -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install sweetcornna/mathodology mathodology-figure-presets --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/sweetcornna/mathodology.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.claude/skills/mathodology-figure-presets .claude/skills/mathodology-figure-presets && 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 "mathodology-figure-presets" agent skill from https://github.com/sweetcornna/mathodology/tree/main/.claude/skills/mathodology-figure-presets into .claude/skills/mathodology-figure-presets/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mathodology-figure-presets", 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/sweetcornna/mathodology/tree/main/.claude/skills/mathodology-figure-presetsType 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 sweetcornna/mathodology --skill mathodology-figure-presets -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install sweetcornna/mathodology mathodology-figure-presets --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/sweetcornna/mathodology.git skills-src && mkdir -p .agents/skills && cp -r skills-src/.claude/skills/mathodology-figure-presets .agents/skills/mathodology-figure-presets && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "mathodology-figure-presets" agent skill from https://github.com/sweetcornna/mathodology/tree/main/.claude/skills/mathodology-figure-presets into .agents/skills/mathodology-figure-presets/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mathodology-figure-presets", 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 sweetcornna/mathodology --skill mathodology-figure-presets -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install sweetcornna/mathodology mathodology-figure-presets --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/sweetcornna/mathodology.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/.claude/skills/mathodology-figure-presets .cursor/skills/mathodology-figure-presets && 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 "mathodology-figure-presets" agent skill from https://github.com/sweetcornna/mathodology/tree/main/.claude/skills/mathodology-figure-presets into .cursor/skills/mathodology-figure-presets/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mathodology-figure-presets", 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/sweetcornna/mathodology.git --path .claude/skills/mathodology-figure-presets--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 sweetcornna/mathodology --skill mathodology-figure-presets -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install sweetcornna/mathodology mathodology-figure-presets --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/sweetcornna/mathodology.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/.claude/skills/mathodology-figure-presets .gemini/skills/mathodology-figure-presets && 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 "mathodology-figure-presets" agent skill from https://github.com/sweetcornna/mathodology/tree/main/.claude/skills/mathodology-figure-presets into .gemini/skills/mathodology-figure-presets/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mathodology-figure-presets", 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 sweetcornna/mathodology mathodology-figure-presetsInstalls 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 sweetcornna/mathodology --skill mathodology-figure-presets -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/sweetcornna/mathodology.git skills-src && mkdir -p .github/skills && cp -r skills-src/.claude/skills/mathodology-figure-presets .github/skills/mathodology-figure-presets && 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 "mathodology-figure-presets" agent skill from https://github.com/sweetcornna/mathodology/tree/main/.claude/skills/mathodology-figure-presets into .github/skills/mathodology-figure-presets/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mathodology-figure-presets", 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 sweetcornna/mathodology --skill mathodology-figure-presets -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install sweetcornna/mathodology mathodology-figure-presets --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/sweetcornna/mathodology.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/.claude/skills/mathodology-figure-presets .opencode/skills/mathodology-figure-presets && 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 "mathodology-figure-presets" agent skill from https://github.com/sweetcornna/mathodology/tree/main/.claude/skills/mathodology-figure-presets into .opencode/skills/mathodology-figure-presets/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mathodology-figure-presets", 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.
Facts
- Skill name
mathodology-figure-presets- GitHub stars
- 305
- Token cost
- ~1k tokens
- SKILL.md length
- 518 words
- Files
- 43 (incl. scripts, references)
- Skills in repo
- 8
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when selecting, designing, generating or reviewing scientific figures, complex modeling charts, paper illustrations or image2-assisted visuals.
- Works in 5 steps: Write the figure's intended conclusion… → Use the 20-preset selector and recipes.… → Adapt the card's plotting and caption… → …
- Reviewing scientific figures
- SKILL.md covers Agent-facing guidance, Before the first figure, Default rendering route and Choose and build, plus 1 more section
- Calls python3
What it does
Mathodology Figure Presets is an agent skill from sweetcornna/mathodology. Use when selecting, designing, generating or reviewing scientific figures, complex modeling charts, paper illustrations or image2-assisted visuals.
Its SKILL.md is about 1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 45 other files, including scripts and reference files (for example `agents/openai.yaml`, `examples/README.md` and `references/README.md`).
It sits in Data & Analytics, covering Data visualization. The repository describes itself as: 专为数学建模竞赛设计的数模 Agent Skills：MCM/ICM 美赛、CUMCM 国赛、华数杯、M3、HiMCM 等，面向 Claude Code 与 Codex 的获奖级建模工作流。Math modeling contest skills for Claude Code & Codex. The licence is MIT.
When your agent uses it
- Reviewing scientific figures
- Complex modeling charts
- Paper illustrations
- Image2-assisted visuals
Example prompts
- “/mathodology-figure-presets”
Requirements
- Python 3
Workflow steps
5 steps, taken from the first numbered list in SKILL.md.
- Write the figure's intended conclusion as a question before seeing the result.
- Use the 20-preset selector and recipes. Load the
- Adapt the card's plotting and caption prompts using the actual columns,
- Use numerical plotting tools for quantitative marks. image2 can assist
- Inspect the rendered figure at publication size, then inspect its placement
What it can do on your machine
Read from SKILL.md and the folder at commit 0cfcd93. 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
Ships 1 file in scripts/, which the agent can run.
Shell commands in SKILL.md call:
python3
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
Mathodology Figure Presets loads about 1k tokens when it runs, and up to ~144k if it reads all its reference files. Until then it costs about 44 tokens; SKILL.md has 518 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.
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); the scripts in this folder are not scanned.
SKILL.md
The full file from sweetcornna/mathodology at commit 0cfcd93, republished under its MIT licence (© sweetcornna). 518 words, ~1,022 tokens.
Download SKILL.mdSave it as
.claude/skills/mathodology-figure-presets/SKILL.md (or your agent's skills folder). This skill also uses 42 other files; get the full folder from GitHub.- name
- mathodology-figure-presets
- description
- Use when selecting, designing, generating or reviewing scientific figures, complex modeling charts, paper illustrations or image2-assisted visuals.
Mathodology Scientific Figure Presets
Choose a figure from the question the reader needs to answer. Complex figures are useful when their panels expose related evidence; complexity itself is not a quality measure. Use actual data and the user's paper language.
Agent-facing guidance
Read default figure guidance when starting figure work or briefing another agent. It is the canonical reusable instruction: select a code template, fill real data, run it and inspect the resulting PNG/PDF. Use it as adaptable guidance, not a fixed pipeline.
Before the first figure
Ask once per modeling task, unless the answer is already known:
是否有可用的 image2 模型？通过当前工具、已配置接口，还是手动生成使用？
Use the host's question mechanism and continue independent analysis while waiting. Do not repeat the question in every specialist or turn. Read image2 usage for available, manual, unavailable and pending cases. Do not claim a generic image tool is image2 or collect API keys in chat.
Default rendering route
Without image2, with a pending answer, or with only manual image2 access, use the 20 callable code templates by default. Copy the matching function into the working task, bind actual data, execute it, and export PNG/PDF. Do not stop at a plotting prompt or wait for image2. Quantitative figures use this same data-driven route even when image2 is available. Adapt the closest template when needed; never substitute synthetic preview values for missing data.
Choose and build
- Write the figure's intended conclusion as a question before seeing the result. Identify data, units, comparison, uncertainty and the available paper space.
- Use the 20-preset selector and recipes. Load the relevant cards, not the entire reference collection. If the data cannot support a preset, choose its simpler alternative or explain the missing input.
- Adapt the card's plotting and caption prompts using the actual columns, measured results and scientific meaning. Do not force the data to match a preview. Follow style and export guidance.
- Use numerical plotting tools for quantitative marks. image2 can assist illustrations, mechanism diagrams and layout concepts; rebuild quantitative layers from data. Never use generated pixels as computed evidence.
- Inspect the rendered figure at publication size, then inspect its placement in the compiled paper. Fix illegibility and misleading encodings; remove decorative panels that do not support the argument.
Show full SKILL.md (147 more words)Show less
Deliver the figure, a caption, its source-data or calculation location and a rerun instruction. The format is flexible. State actual limitations; do not invent statistical significance or claim that appearance implies an award.
Reference and example library
- Sources and visual references: six PLOS reference figures (including counterexamples), eight Matplotlib source-text snapshots, licenses and a per-file provenance manifest. Read snapshots before adaptation; they are not executable installation or workflow steps.
- Synthetic example gallery: six previews and the optional demonstration script. The examples illustrate presentation and statistical labeling, not evidence for a contest problem.
python3 .claude/skills/mathodology-figure-presets/scripts/render_examples.py --output work/figure-examplesThe demos use NumPy and Matplotlib; the F08 code template additionally requires SciPy. If dependencies are absent, use the host's available runtime or install them in an isolated environment; no dependencies are needed just to read and use the prompts. Existing numerical tools in R, MATLAB or another language are equally acceptable for task-specific figures.
© sweetcornna, 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 42 other files (scripts, references) in .claude/skills/mathodology-figure-presets of sweetcornna/mathodology.
- SKILL.md
- agents/openai.yaml
- examples/README.md
- examples/f01-mosaic.pdf
- examples/f01-mosaic.png
- examples/f02-forecast.pdf
- examples/f02-forecast.png
- examples/f03-raincloud.pdf
- examples/f03-raincloud.png
- examples/f10-pareto.pdf
- examples/f10-pareto.png
- examples/f16-sensitivity.pdf
- examples/f16-sensitivity.png
- examples/f20-scenarios.pdf
- examples/f20-scenarios.png
- references/README.md
- references/figure-guidance.md
- references/image2.md
- … and 25 more
Open the folder on GitHubat commit 0cfcd93
Compare with similar skills
Mathodology Figure Presets 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 |
|---|---|---|---|---|---|---|
| Mathodology Figure Presets this skillsweetcornna/mathodology | 305 | — | ~1k | Automated safety check: Pass | MIT | |
| Scientific Figure MakingChenLiu-1996/figures4papers | 8.3k | — | ~557 | Automated safety check: Pass | Custom licence | |
| Academic Figure SkillTingxiYu/academic-figure-skill | 483 | 1 repos | ~7k | Automated safety check: Pass | Apache-2.0 | |
| Visualizezai-org/ZCode | 7.7k | — | ~8.7k | Automated safety check: Pass | Custom licence | |
| FigMirrorVILA-Lab/FigMirror | 521 | — | ~2.4k | Automated safety check: Pass | None | |
| Figure Libraryxuzhougeng/ScientificFigureLibrary | 110 | — | ~2.1k | Automated safety check: Pass | MIT |
Similar skills
Academic Figure Skill
TingxiYu/academic-figure-skill
Academic-grade scientific figure creation for Nature/Cell/Science journals.
Visualize
zai-org/ZCode
Create visualizations and interactive tools directly in conversation.
FigMirror
VILA-Lab/FigMirror
Mirrors the visual style of a top-conference paper figure onto your own data, producing a camera-ready PDF and a self-contained matplotlib script.
Figure Library
xuzhougeng/ScientificFigureLibrary
Search, browse, select, import, review, publish, and materialize reusable scientific-figure assets through Scientific Figure Library.
Agent Figure Gallery
Dsadd4/AgentFigureGallery
Lets a plotting agent query a gallery of scientific figure references, show candidates for you to pick from, and export the chosen ones before it writes plot code.
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Related
Categories
Questions about Mathodology Figure Presets
What does Mathodology Figure Presets do?
A skill your agent uses when selecting, designing, generating or reviewing scientific figures, complex modeling charts, paper illustrations or image2-assisted visuals. Mathodology Figure Presets is an agent skill from sweetcornna/mathodology. Use when selecting, designing, generating or reviewing scientific figures, complex modeling charts, paper illustrations or image2-assisted visuals.
When should I use Mathodology Figure Presets?
Mathodology Figure Presets fits situations like: reviewing scientific figures; complex modeling charts; paper illustrations; image2-assisted visuals.
How do I install Mathodology Figure Presets in Claude Code?
Run `npx skills add sweetcornna/mathodology --skill mathodology-figure-presets -a claude-code`. Or copy the skill folder (.claude/skills/mathodology-figure-presets in sweetcornna/mathodology) into .claude/skills/mathodology-figure-presets in your project. Claude Code loads it when a task matches its description.
How do I install Mathodology Figure Presets in Codex?
Run `npx skills add sweetcornna/mathodology --skill mathodology-figure-presets -a codex`. Or copy the skill folder (.claude/skills/mathodology-figure-presets in sweetcornna/mathodology) into .agents/skills/mathodology-figure-presets in your project. Codex loads it when a task matches its description.
Can I use Mathodology Figure Presets 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 sweetcornna/mathodology --skill mathodology-figure-presets -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/mathodology-figure-presets, .gemini/skills/mathodology-figure-presets, .github/skills/mathodology-figure-presets and .opencode/skills/mathodology-figure-presets in your project.
What does Mathodology Figure Presets need to run?
Going by SKILL.md and its folder, Mathodology Figure Presets needs the command-line tools its instructions call (python3). Our summary lists: Python 3.
Does Mathodology Figure Presets 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 Mathodology Figure Presets 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. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
What licence does Mathodology Figure Presets use?
Mathodology Figure Presets 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 Mathodology Figure Presets use?
About 1k tokens (SKILL.md is roughly 4.1k 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 143k tokens, read only when the agent opens those files.
What are the alternatives to Mathodology Figure Presets?
Skills that share tags, products or a category with Mathodology Figure Presets: Scientific Figure Making (ChenLiu-1996/figures4papers, 8.3k stars), Academic Figure Skill (TingxiYu/academic-figure-skill, 483 stars), Visualize (zai-org/ZCode, 7.7k stars) and FigMirror (VILA-Lab/FigMirror, 521 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Mathodology Figure Presets?
sweetcornna (a GitHub user) maintains it in sweetcornna/mathodology, which has 305 GitHub stars. The repository holds 8 skills in this directory. The repository was last updated on September 7, 2026.
Source: sweetcornna/mathodology on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.