How do I install the Matplotlib Figures skill?

Run npx skillmds@latest add tomevault-io/matplotlib-figures in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Matplotlib Figures safe to use?

This skill has not completed SkillMD's automated safety review yet. Independent scanners report: SkillSpector: PASS, Skill Scanner: PASS. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Matplotlib Figures?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Matplotlib Figures free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Matplotlib Figures?

tomevault-io (@tomevault-io) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Matplotlib Figures by tomevault-io — Agent Skill

Name: Matplotlib Figures
Author: tomevault-io

Matplotlib Figures

Publication-quality data plots via matplotlib with venue-specific styles. Use for generating your own figures (timelines, comparison charts, data summaries, heatmaps) that you add to the LaTeX report. Your brain prompt supplies the venue-specific style directory as {{VENUE_SPECIFIC_DIR}} — use that value wherever this skill writes `<VENUE_SPECIFIC_DIR>`. Use when this capability is needed.

tomevault-io Updated Sep 17, 2026

File contents

Matplotlib Figures Skill

All generated figures MUST be publication-quality: load a venue-matched style, save as vector PDF, use colorblind-safe palettes.

3-step workflow

Step 1 — Set up the figure style (once per project)

When you have determined the target venue, copy the matching matplotlib style template to your project. Your brain prompt supplies the venue-specific directory as {{VENUE_SPECIFIC_DIR}}:

cp {{VENUE_SPECIFIC_DIR}}figstyles/<style>.mplstyle report/figstyle.mplstyle

Style map:

Venue	Style file	Notes
Physics (PRL, PRX, APS journals)	`physics-aps.mplstyle`	CM fonts, LaTeX, 600 DPI
CS conferences (NeurIPS, ICML, ICLR)	`cs-conferences.mplstyle`	sans-serif, 300 DPI
Nature / Science / Cell / PNAS	`nature-science.mplstyle`	Arial, compact, 300 DPI
Chemistry (JACS, ACS journals)	`chemistry-acs.mplstyle`	Arial, 300 DPI

Step 2 — Use the style in all plotting code

import matplotlib.pyplot as plt
plt.style.use('report/figstyle.mplstyle')

Step 3 — Save as PDF (vector), not PNG

fig.savefig('report/figures/fig_name.pdf')

Rules

Never use the default matplotlib style — always load figstyle.mplstyle.
Format: PDF (vector) for line plots and diagrams; PNG only for raster data (heatmaps, images).
Width: single-column for most figures; override figsize for double-column only when the figure genuinely needs it.
Colors: use colorblind-friendly palettes (Tol / Wong — already bundled in the style files).
Tables: render tabular data with LaTeX \begin{tabular}, NOT as matplotlib table images.
Fallback: if text.usetex fails (LaTeX not installed), set text.usetex=False in the style file.

Source: Muuuun/luxas — distributed by TomeVault.

---name: matplotlib-figuresdescription: Publication-quality data plots via matplotlib with venue-specific styles. Use for generating your own figures (timelines, comparison charts, data summaries, heatmaps) that you add to the LaTeX report. Your brain prompt supplies the venue-specific style directory as {{VENUE_SPECIFIC_DIR}} — use that value wherever this skill writes `<VENUE_SPECIFIC_DIR>`. Use when this capability is needed.---# Matplotlib Figures SkillAll generated figures MUST be publication-quality: load a venue-matched style, save as vector PDF, use colorblind-safe palettes.## 3-step workflow### Step 1 — Set up the figure style (once per project)When you have determined the target venue, copy the matching matplotlib style template to your project. Your brain prompt supplies the venue-specific directory as `{{VENUE_SPECIFIC_DIR}}`:```bashcp {{VENUE_SPECIFIC_DIR}}figstyles/<style>.mplstyle report/figstyle.mplstyle```**Style map:**| Venue | Style file | Notes ||---|---|---|| Physics (PRL, PRX, APS journals) | `physics-aps.mplstyle` | CM fonts, LaTeX, 600 DPI || CS conferences (NeurIPS, ICML, ICLR) | `cs-conferences.mplstyle` | sans-serif, 300 DPI || Nature / Science / Cell / PNAS | `nature-science.mplstyle` | Arial, compact, 300 DPI || Chemistry (JACS, ACS journals) | `chemistry-acs.mplstyle` | Arial, 300 DPI |### Step 2 — Use the style in all plotting code```pythonimport matplotlib.pyplot as pltplt.style.use('report/figstyle.mplstyle')```### Step 3 — Save as PDF (vector), not PNG```pythonfig.savefig('report/figures/fig_name.pdf')```## Rules- **Never** use the default matplotlib style — always load `figstyle.mplstyle`.- **Format**: PDF (vector) for line plots and diagrams; PNG only for raster data (heatmaps, images).- **Width**: single-column for most figures; override `figsize` for double-column only when the figure genuinely needs it.- **Colors**: use colorblind-friendly palettes (Tol / Wong — already bundled in the style files).- **Tables**: render tabular data with LaTeX `\begin{tabular}`, NOT as matplotlib table images.- **Fallback**: if `text.usetex` fails (LaTeX not installed), set `text.usetex=False` in the style file.---> Source: [Muuuun/luxas](https://github.com/Muuuun/luxas) — distributed by [TomeVault](https://tomevault.io).

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