figio-guide

Coordinate reproducible figure construction and validation for scientific workflows.

1|Updated Mar 9, 2026
One-click install
npx skills add https://github.com/arashshahidi1997/projio --skill figio-guide
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: figio-guide
Source: https://github.com/arashshahidi1997/projio/tree/main/.projio/skills/figio-guide
Command: npx skills add https://github.com/arashshahidi1997/projio --skill figio-guide

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Researchers and engineers need a reliable workflow to generate, validate, and reproduce publication-ready figures from experimental results.

Core Features & Use Cases

  • Declarative FigureSpec YAML that defines panels, layout, annotations, and style
  • Panel scripts or generators to produce figures with Matplotlib
  • Built-in target profiles and validation to ensure consistency across outputs
  • Seamless integration with manuscripts and reports for end-to-end figure pipelines

Quick Start

Create a FigureSpec YAML file, implement a panel or script to generate figures, then run figio_build to produce an SVG.

Frequently Asked Questions about figio-guide

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

FAQPage Schema
How do I create reproducible figures for scientific manuscripts using YAML and Matplotlib?

Reproducible figures are created by defining a declarative FigureSpec YAML file that specifies panels, layout, annotations, and style constraints, then running a Matplotlib panel script to generate the final SVG output.

What is a declarative FigureSpec YAML workflow for publication-ready figures?

A declarative FigureSpec YAML workflow separates figure layout and style constraints from plotting logic, allowing researchers to define panels and annotations in YAML while Matplotlib scripts handle data visualization and export.

Can I use Figio to validate figure consistency across presentations and supplementary materials?

Figio provides built-in target profiles and validation steps to ensure visual consistency across manuscripts, presentations, and supplementary materials by enforcing style constraints defined in the YAML specification.

How do I export Matplotlib figures to SVG for research projects?

Matplotlib figures are exported to SVG by implementing a panel generator script, defining the layout in a FigureSpec YAML file, and executing the figio_build command to produce the final vector output.

Does Figio support multi-panel layouts and annotations for complex scientific figures?

Figio supports multi-panel layouts and annotations through its YAML FigureSpec configuration, which allows researchers to define panel arrangements, apply annotations, and enforce style constraints for publication-ready outputs.

What's the best way to maintain consistent figure styling across different research outputs?

The best way to maintain consistent styling is using a declarative YAML FigureSpec with built-in profiles that enforce style constraints, ensuring figures remain uniform across manuscripts, presentations, and supplementary materials.