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visual-engineer

Generate publication-quality scientific figures with Matplotlib and Seaborn.

Updated Jan 26, 2026
Search Tags:#visualization#reproducible#publication-quality#Matplotlib#Seaborn#dpi#Times New Roman
One-click install
npx skills add https://github.com/SPIRAL-EDWIN/MCM-ICM-2601000 --skill visual-engineer
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: visual-engineer
Source: https://github.com/SPIRAL-EDWIN/MCM-ICM-2601000/tree/main/.github/skills/visual-engineer
Command: npx skills add https://github.com/SPIRAL-EDWIN/MCM-ICM-2601000 --skill visual-engineer

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill enables researchers and designers to generate publication-quality figures that meet academic standards (Times New Roman font, 300 DPI, clean layouts) without manual formatting, saving time and reducing inconsistencies.

Core Features & Use Cases

  • Standardized Visuals: Enforce serif fonts, publication-grade DPI, accessible color schemes, and consistent styling for all figures.
  • Reusable Templates: Provide reusable plotting templates (time-series, scatter, heatmaps) that can be applied to different datasets.
  • Use Case: A researcher prepares a conference poster and a journal figure from the same data with a single command, ensuring identical typography and layout.

Quick Start

Use the visual-engineer skill to generate a publication-quality time-series figure from your dataset and save to the organized results directory.

Frequently Asked Questions about visual-engineer

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I generate publication-quality figures with Times New Roman and 300 DPI in Matplotlib?▼

Generate publication-quality figures with Matplotlib by applying automated typography and resolution rules. This skill enforces Times New Roman fonts and 300 DPI output to meet academic journal standards without manual formatting.

How do I ensure reproducible figure pipelines across multiple plots for scientific papers?▼

Ensure reproducible figure pipelines by applying standardized templates for time-series, scatter, and heatmap plots. This skill automatically enforces consistent typography, accessible color palettes, and layout styling across all generated scientific figures.

Do I need Python and Seaborn to create color-safe palettes for journal submissions?▼

Yes, you need Python with Matplotlib and Seaborn to create color-safe palettes for journal submissions. The skill relies on these frameworks to enforce accessible color schemes and generate standardized visual outputs.

Can I reuse the same plotting template for a conference poster and a journal figure?▼

Yes, you can reuse plotting templates for a conference poster and a journal figure from the same dataset. The skill applies identical typography and layout styling across outputs, ensuring visual consistency with a single command.

What is the best way to automate scientific figure generation and save to a standardized directory?▼

Automate scientific figure generation by running the skill to process datasets and output files to a standardized results directory. It enforces publication-ready layouts, serif fonts, and 300 DPI resolution automatically.

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