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nature-figure

Convert figure requests into publication-ready Nature-style multi-panel figures with editable SVG/PDF exports.

Updated May 15, 2026
Search Tags:#scientific figures#multi-panel layout#journal-ready exports#editable svg#python or r plotting#figure contract#submission quality assurance
One-click install
npx skills add https://github.com/lth0/codexSkill --skill nature-figure-lth0
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: nature-figure
Source: https://github.com/lth0/codexSkill/tree/main/skills/codex/nature-figure
Command: npx skills add https://github.com/lth0/codexSkill --skill nature-figure-lth0

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) and assets (resource) components.

What problem does it solve?

This Skill turns manuscript figure requests into submission-grade, journal-style plots by enforcing a figure contract (core conclusion, evidence hierarchy, panel map, and review-risk checks) before any plotting or styling decisions.

Core Features & Use Cases

  • Figure contract first: builds a defensible one-sentence claim, assigns panels to unique evidence, and selects a figure archetype so the result reads like a scientific argument.
  • Backend-exclusive, publication-safe rendering: generates previews and exports using either Python or R exclusively, with hard guardrails against cross-backend fallback when runtimes or packages are missing.
  • Nature-family style constraints: enforces editable SVG/PDF text rules (e.g., svg.fonttype = 'none'), consistent typography, spine/legend policies, and export bundles for paper/presentation workflows.
  • Multi-panel anti-redundancy design: ensures each subplot answers a distinct question, preventing common redundancy traps in composite figures.
  • Chart-family breadth: supports common journal chart types (bars, trends, heatmaps, scatter/bubble, radar/polar) and multi-panel layout patterns.

Quick Start

Prepare a Nature-style figure plan by asking the Skill to build a panel-by-panel figure contract for your figure request and then generate editable SVG outputs in Python or R after you confirm which backend to use.

Frequently Asked Questions about nature-figure

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

FAQPage Schema
How do I create journal-ready scientific figures in Python or R?▼

To create journal-ready scientific figures, you define a claim-first figure contract that maps evidence to panels, then generate editable SVG or PDF exports using exclusively Python or R backends with Nature-style typography constraints.

What is a figure contract and why is it needed for multi-panel layouts?▼

A figure contract establishes a defensible one-sentence scientific claim, assigns panels to unique evidence, and selects a figure archetype, ensuring multi-panel layouts read as coherent arguments without redundant subplots.

How do I export editable SVG files with intact text for manuscript submission?▼

You export editable SVG files by enforcing font type rules like 'svg.fonttype = none' during rendering, ensuring all text remains editable in vector graphics applications for journal submission quality assurance.

Can I use both Python and R plotting libraries in the same multi-panel figure?▼

No, backend exclusivity is strictly enforced. You must choose either Python or R for rendering previews and exports; cross-backend fallback is blocked even when runtimes or packages are missing.

What chart types are supported for Nature-style publication figures?▼

Supported chart types for Nature-style publication figures include bars, trends, heatmaps, scatter and bubble plots, and radar or polar charts, organized into multi-panel layout patterns.

What is the best way to prevent redundancy in multi-panel scientific figures?▼

The best way to prevent multi-panel redundancy is designing each subplot to answer a distinct question during the figure contract phase, ensuring every panel contributes unique evidence to the scientific claim.

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