figures4papers is a collection of Python scripts designed to streamline the creation of high-quality figures for academic publications in the fields of artificial intelligence. The primary objective is to provide a standardized and efficient way to generate figures for conference submissions and journal articles. This repository addresses the challenge of producing publication-ready figures by offering a modular and reusable set of tools, particularly leveraging a skill-based approach for integration with AI coding agents.
The project's key strength lies in its modular design, enabling seamless integration with AI coding agents without requiring installation. It promotes consistent figure styles and leverages a structured skill-based architecture for reusability and extensibility. The repository also incorporates comprehensive documentation on design principles and best practices for creating publication-quality figures.
- Core Functionality: Provides a collection of Python scripts for generating various types of scientific figures (bar plots, trend plots, heatmaps, 3D spheres, etc.).
- Skill Integration: Designed for easy integration into AI coding agents (Cursor, Claude Code, Codex) via path-based or symlink installation.
- Design Principles: Includes a detailed
design-theory.mdfile outlining design rationale and best practices for creating publication-quality figures. - Reusable Patterns: Offers reusable figure patterns and helpers in the
references/directory for consistent figure generation. - Variety of Plot Types: Supports a wide range of common plot types used in AI research, including bar charts, line plots, heatmaps, and more.
- Clear Documentation: Provides detailed instructions and examples for using the skills within AI coding agents.
- Community Resources: Links to related research papers (ImmunoStruct, Dispersion, RNAGenScape, BrainTeaser) for context and inspiration.
The project appears to be actively developed, with recent commits indicating ongoing maintenance and updates. The presence of example figures and detailed documentation suggests a solid level of maturity. The inclusion of related papers and links to datasets/models further strengthens its practical application. The structured approach with a skill-based design indicates a thoughtful and well-organized development process.
This repository is valuable for researchers in artificial intelligence, particularly those preparing manuscripts for top-tier conferences and journals. It provides a practical solution for generating high-quality, publication-ready figures by leveraging a modular and extensible Python ecosystem. By integrating with AI coding agents, it offers researchers a powerful tool to automate and streamline the figure creation process, ensuring consistency and adherence to publication guidelines.