paper-figure

Generate LaTeX-compatible vector PDF figures and statistics tables from JSON experiment data.

1|Updated May 14, 2026
One-click install
npx skills add https://github.com/lix965996-art/MMM --skill paper-figure-lix965996-art
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: paper-figure
Source: https://github.com/lix965996-art/MMM/tree/main/resources/app/skills/paper-figure
Command: npx skills add https://github.com/lix965996-art/MMM --skill paper-figure-lix965996-art

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill solves the problem of manually styling and exporting low-quality charts by automatically generating publication-quality figures and tables from your experiment results.

Core Features & Use Cases

  • Publication-quality output: Generates vector-ready PDF figures (e.g., suitable for LaTeX), using an academic visual style baseline with quality targets like ≥300 DPI, readable fonts, and grayscale-distinguishable design.
  • Data-to-visual pipeline: Discovers your planned figure/table requirements, verifies that available JSON data can support the needed visuals, and then generates every planned figure rather than picking ad-hoc plots.
  • Workflow guardrails for paper standards: Enforces rules such as no in-figure titles (captions belong in LaTeX), consistent styling palettes, and a self-check gate to prevent obvious quality issues.
  • Use Case: If you have experiment outputs in figures/*.json and you need “paper figures” for a report, thesis, or competition write-up, this Skill produces the complete set of charts and statistics tables.

Quick Start

Ask your AI to run paper-figure to generate publication-quality figures and tables from your figure data by using the figure plan and JSON results already placed under figures/.

Frequently Asked Questions about paper-figure

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

FAQPage Schema
How do I generate publication-quality figures from JSON data for LaTeX papers?

Generate publication-quality figures from JSON data by parsing your figure plan, validating JSON data coverage, and rendering vector PDFs with academic styling, consistent palettes, and grayscale-safe designs at 300 DPI or higher.

How do I automatically create statistics tables and charts from experiment results?

Create statistics tables and charts automatically by placing your JSON experiment outputs and a figure plan document under the figures directory to let the pipeline discover, validate, and render all planned visuals without manual chart tweaking.

Can I use JSON experiment data to produce vector PDFs without manual chart styling?

Yes, you can produce vector-ready PDFs from JSON inputs without manual styling because the pipeline enforces academic visual baselines, applies quality gates, and removes in-figure titles since captions belong in LaTeX.

Does this academic figure generation pipeline enforce completeness checks for planned tables?

Yes, the pipeline enforces completeness by parsing your planning document to build a numbered figure checklist and then validating your JSON data integrity to ensure every planned figure and table is fully supported before rendering.

Why are in-figure titles removed when generating academic charts for LaTeX?

In-figure titles are removed because captions belong in LaTeX, so the pipeline enforces this academic standard along with consistent styling palettes and a self-check gate to prevent obvious visual quality issues.