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TerryFYL
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academic-figure-engine

Generate publication-ready figures from raw analysis results with data provenance.

8|Updated Mar 18, 2026
Search Tags:#quality-assurance#data-visualization#data-provenance#journal-compliance#figure-generation#publication-graphics#biomedical-figures
One-click install
npx skills add https://github.com/TerryFYL/ai-research-army --skill academic-figure-engine
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: academic-figure-engine
Source: https://github.com/TerryFYL/ai-research-army/tree/main/skills/academic-figure-engine
Command: npx skills add https://github.com/TerryFYL/ai-research-army --skill academic-figure-engine

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires matplotlib, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This engine automates the generation of publication-grade figures from raw analysis results while enforcing traceable data provenance, reducing manual plotting errors, and ensuring compliance with journal guidelines.

Core Features & Use Cases

  • Automated rendering of common figure types (Kaplan-Meier curves, forest plots, heatmaps, box plots, scatter plots, radar charts, tables) powered by a five-layer quality framework that includes data truth, standardization, verification, manuscript synchronization, and visual refinement.
  • Output in publication-ready formats (PNG, PDF, TIFF) with per-figure data sources and reproducible pipelines, plus verification reports suitable for manuscript submission.
  • Use Case: A biomedical research team analyzes a clinical dataset, automatically generates a complete figure suite, and obtains a delivery package aligned with journal requirements.

Quick Start

Start by providing your analysis results and instruct the engine to generate publication-ready figures.

Frequently Asked Questions about academic-figure-engine

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

FAQPage Schema
How do I generate publication-ready figures with data provenance for a biomedical manuscript?▼

The engine generates publication-ready figures with data provenance by automating rendering from raw analysis results, enforcing a five-layer quality framework that traces source data and ensures journal compliance.

What chart types are supported for biomedical publication graphics?▼

Supported biomedical publication graphics include Kaplan-Meier curves, forest plots, heatmaps, box plots, scatter plots, radar charts, and tables, all rendered through a standardized visual refinement layer.

Can I output figures in TIFF, PDF, and PNG formats for journal submission?▼

Yes, you can output figures in TIFF, PDF, and PNG formats for journal submission. The engine provides per-figure data sources and verification reports suitable for manuscript delivery.

Does the figure generation engine work with matplotlib for style compliance?▼

Yes, the figure generation engine works with matplotlib to apply style guides and journal presets, ensuring deterministic data rendering and visual refinement for manuscript-ready delivery.

How does data provenance and source traceability work when generating publication figures?▼

Data provenance and source traceability work by enforcing a data truth layer within the five-layer framework, capturing per-figure data sources and reproducible pipelines to verify figure accuracy.

What is the best way to automate forest plots and Kaplan-Meier curves for clinical datasets?▼

The best way to automate forest plots and Kaplan-Meier curves is using an engine that applies standardization and automated verification layers to raw clinical data, ensuring deterministic rendering and manuscript synchronization.

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