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scientific-visualization

Create and audit publication-ready scientific figures with Matplotlib, Seaborn, and Plotly.

Updated Sep 2, 2026
Search Tags:#data visualization#color palette#matplotlib#publication#scientific figures#wcag contrast#journal export
One-click install
npx skills add https://github.com/ricfulop/cba-agentic-engineering-bootstrap --skill scientific-visualization-ricfulop
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: scientific-visualization
Source: https://github.com/ricfulop/cba-agentic-engineering-bootstrap/tree/main/skills/scientific-visualization
Command: npx skills add https://github.com/ricfulop/cba-agentic-engineering-bootstrap --skill scientific-visualization-ricfulop

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires matplotlib, seaborn, plotly, kaleido, pillow, pypdf, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve? Scientific figures often fail journal submission or mislead readers because of dishonest encodings, inaccessible color choices, wrong physical dimensions, or unverified publisher rules. This Skill guides the design, export, and audit of truthful, accessible figures while preserving raw data and provenance. ## Core Features & Use Cases - Honest figure design: Enforces integrity guardrails for baselines, uncertainty, missing data, binning, normalization, and dual axes before any plotting code is written. - Deterministic CLI tooling: Bundled network-free scripts inspect raster/SVG/PDF/EPS metadata, audit palette contrast against WCAG thresholds, plan publisher exports from dated profiles, and export Matplotlib figures atomically with provenance manifests. - Accessibility and color review: Provides Okabe-Ito and Paul Tol palettes, WCAG 2.2 contrast checks, grayscale screening, and redundant-encoding guidance. - Use Case: A researcher preparing a Nature submission uses the export planner to check width and DPI rules, builds a multi-panel Matplotlib figure with a scoped style, exports PDF and PNG with a provenance manifest, then screens the delivered file's dimensions and fonts before upload. ## Quick Start Ask the assistant to create a publication-ready multi-panel figure from your data using the scientific-visualization workflow and then audit the exported file's dimensions, DPI, and palette contrast.

Frequently Asked Questions about scientific-visualization

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

FAQPage Schema
How do I create a publication-ready figure with Matplotlib?▼

Use Matplotlib's object-oriented API with a scoped style context and constrained layout, then export with explicit DPI, format, and font settings. The bundled figure_export.py writes PDF, PNG, TIFF, and other formats atomically and records a provenance manifest.

How do I check if my figure meets journal DPI and size requirements?▼

Run scripts/export_plan.py with the publisher, figure type, width, and phase to build a plan from dated official-source snapshots, then add --input to screen a local file. The screen reports pass/fail findings but does not certify journal acceptance.

What color palette should I use for colorblind-safe scientific plots?▼

The Okabe-Ito palette and Paul Tol qualitative schemes are bundled with exact hex values and category limits. A five-color Okabe-Ito subset reaches 3:1 contrast on white, but you must still audit rendered contrast and add redundant encodings like markers or line styles.

Does Plotly static export still work with Kaleido 1.3?▼

Yes, but Kaleido 1.3.0 requires a separate Chrome or Chromium installation since it no longer bundles one. Supported static formats are PNG, JPEG, WebP, SVG, and PDF; EPS requires Kaleido v0, and the deprecated engine parameter and Orca are not supported.

Why does bbox_inches='tight' change my figure's physical size?▼

The tight bounding box crops the canvas to the drawn artists, altering the page dimensions you set with figsize. To preserve exact physical dimensions, export with bbox_inches=None and measure the delivered file in millimeters with the metadata inspection tool.

Can automated checks certify my figure is journal-compliant or accessible?▼

No. The bundled tools screen only machine-readable properties like format, DPI, dimensions, and contrast ratios against dated snapshots. Publisher rules must be verified against the live journal page, and accessibility requires manual review of the rendered figure.

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