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ds-figure-polish

Polish research figures through a mandatory render-inspect-revise workflow for papers and milestones.

1.1k|116|Updated Feb 26, 2026
Search Tags:#research paper#Matplotlib#chart design#academic style#Data Visualization#plot export#figure polish
One-click install
npx skills add https://github.com/OpenLAIR/dr-claw --skill ds-figure-polish
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ds-figure-polish
Source: https://github.com/OpenLAIR/dr-claw/tree/main/skills/ds-figure-polish
Command: npx skills add https://github.com/OpenLAIR/dr-claw --skill ds-figure-polish

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Research figures often ship with cluttered layouts, unreadable labels, or inappropriate chart types because plotting code that looks correct can still produce poor visuals. This Skill enforces an academic visual style and a mandatory self-review pass so figures are actually inspected before being treated as final.

Core Features & Use Cases

  • Surface Classification: Distinguishes connector milestone images, paper main figures, appendix figures, and internal review plots, each with its own export rules (PNG vs vector PDF/SVG plus preview).
  • Academic Style Contract: Applies a bundled Matplotlib style (assets/deepscientist-academic.mplstyle) with muted Morandi palettes, restrained grids, and chart-selection guidance matched to the research question.
  • Mandatory Render-Inspect-Revise Loop: Requires rendering a draft, visually inspecting the output, revising readability issues against a self-review checklist, and re-exporting before acceptance.
  • Use Case: When finishing a main-experiment comparison chart for a paper, use this Skill to generate the figure, inspect the rendered result, fix label collisions and legend clutter, then export both a vector PDF and a PNG preview while recording the figure in the catalog.

Quick Start

Polish my experiment results chart into a paper-ready figure and inspect the rendered output before finalizing it.

Frequently Asked Questions about ds-figure-polish

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

FAQPage Schema
How do I make a paper-ready figure with Matplotlib?▼

Start from the bundled deepscientist-academic.mplstyle, set an explicit figure size in inches, and export both a vector PDF or SVG and a PNG preview. Then inspect the rendered image and revise readability issues before treating it as final.

What chart type should I use for experiment comparisons?▼

Use line charts for trends over steps or budgets, bar charts for a few categorical end-point comparisons, and point-range plots when uncertainty or seed spread matters. Reserve heatmaps for cases where the matrix structure itself is the result.

Should I export paper figures as PNG or PDF?▼

Paper main and appendix figures should be exported as vector PDF or SVG to keep line art and text sharp, plus a PNG preview for visual review and UI display. Connector milestone images are normally exported as PNG only.

Why does my plot look bad even though the code is correct?▼

Correct plotting code does not guarantee a readable figure; label collisions, oversized legends, dense ticks, and weak visual hierarchy only appear in the rendered output. Always open and inspect the rendered image, then revise before finalizing.

When should I not use this figure polishing workflow?▼

Skip it for disposable debug plots used only during transient troubleshooting, unless the user explicitly asks for them to be polished. The workflow targets figures stored as durable artifacts or cited in writing.

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