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Alexander-M-Dickerson
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publication-figures

Standardize publication-ready figure styling for empirical finance visuals.

58|10|Updated Apr 2, 2026
Search Tags:#style#visualization#matplotlib#reproducibility#palette#publication#figure
One-click install
npx skills add https://github.com/Alexander-M-Dickerson/ai-asset-pricing --skill publication-figures-alexander-m-dickerson
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: publication-figures
Source: https://github.com/Alexander-M-Dickerson/ai-asset-pricing/tree/main/.claude/skills/publication-figures
Command: npx skills add https://github.com/Alexander-M-Dickerson/ai-asset-pricing --skill publication-figures-alexander-m-dickerson

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Publication-quality figures enable consistent, clear visuals for empirical finance and economics, reducing manual styling overhead and improving reproducibility.

Core Features & Use Cases

  • Uses the shared publication style and color palettes (Okabe-Ito) for all figures generated with fintools.figures.
  • Provides sizing helpers (set_size) and export defaults to ensure publication-ready output across journals.
  • Supports common figure types (time series, decile bars, coefficient plots, event studies) and multi-panel layouts for coherent publications.
  • Maintains legacy compatibility by including an optional style pathway and ready-to-copy styling guidelines.
  • Improves reproducibility by enforcing font embedding, vector exports, and consistent tick/axis conventions.

Quick Start

Import the shared figure utilities and apply the predefined publication style to your plots to ensure publication-grade visuals.

Frequently Asked Questions about publication-figures

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

FAQPage Schema
How do I create publication-ready figures for empirical finance plots using matplotlib?▼

You create publication-ready figures by applying predefined style utilities that enforce consistent palettes, sizes, and export settings for your matplotlib visuals. This ensures your empirical finance plots meet standard journal requirements.

What color palette is best for ensuring reproducibility and accessibility in academic figures?▼

The Okabe-Ito color palette is applied to ensure reproducibility and accessibility in academic figures. It provides CI-safe color usage, maintaining visual clarity and consistency across all generated empirical finance visuals.

How do I standardize figure sizes and export settings for multi-panel time-series charts?▼

You standardize figure sizes and export settings by using the provided set_size helper and export defaults. These utilities enforce house style defaults, font embeddings, and vector exports for multi-panel time-series charts.

Can I generate coefficient plots and decile bar charts that match standard publication styles?▼

Yes, you can generate coefficient plots and decile bar charts that match standard publication styles. The styling utilities apply consistent tick and axis conventions alongside predefined palettes directly to these empirical finance figure types.

Do I need specific dependencies to apply consistent styling to my event study figures?▼

No specific external dependencies are required to apply consistent styling to event study figures. The skill provides built-in plotting helpers and style pathways that enforce font embedding and vector exports directly within your existing environment.

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