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.