Skill UI

A Python materials science library for constructing, analyzing, and converting crystal structures, computing phase diagrams and electronic properties, and integrating with the Materials Project API for high-throughput workflows.

A comprehensive tool for generating publication-quality figures essential for Machine Learning and AI academic papers. It supports two distinct workflows: generating complex system architecture and workflow diagrams using AI (Gemini), and creating precise, data-driven visualizations (line charts, heatmaps, etc.) using standard libraries like matplotlib and seaborn. Use it when preparing any figure for a conference submission.

A powerful Python library for analyzing, validating, converting, and transforming complex materials structures and associated computational data. It provides functionalities for handling compositions, periodic crystals, molecules, local phase diagrams, symmetry assignments, and electronic structure I/O, ensuring strict provenance tracking for all transformations.

An advanced AI tool that generates publication-quality scientific diagrams, including neural network architectures, biological pathways, and system flowcharts. It utilizes smart iterative refinement, with Gemini 3.6 Flash automatically reviewing the quality against specific document type thresholds (e.g., journal, poster, presentation) to ensure professional accuracy and high fidelity.

This skill guides the systematic process of conducting comprehensive literature reviews across multiple academic databases (e.g., PubMed, arXiv, Semantic Scholar). It supports meta-analyses, scoping reviews, and state-of-the-art investigations. The rigorous workflow includes structured searching, data extraction, citation verification, thematic synthesis, and generating publication-quality reports (PDF/Markdown) complete with mandatory scientific schematics (like PRISMA flow diagrams) to ensure academic rigor and reproducibility.

This advanced skill is an end-to-end tool for transforming complex research topics into professional, visualized knowledge graph PDFs. It automatically conducts multi-round web searches, extracts key entities, structures related knowledge, and designs the optimal visualization (supporting Graphviz, PlantUML, etc.). Ideal for deep academic analysis, mapping character relationships, or visualizing complex technical architectures where existing structured data is unavailable.

This guide provides rigorous guidelines for creating high-quality figures and tables for complex systems manuscripts, especially in agricultural science. It emphasizes shifting focus from single-factor bar charts to systemic visualizations that reveal interactions, trade-offs (e.g., Pareto fronts), model dynamics, resource flows (Sankey diagrams), and model performance (observed-vs-simulated fit). Exhibits must be self-contained, reproducible, and convey uncertainty.

This guide provides detailed standards for formatting empirical results (tables and figures) for the Academy of Management Journal (AMJ). It covers best practices for reporting correlation matrices, regression/SEM/HLM results, theoretical model diagrams, and interaction plots, ensuring adherence to strict academic publishing house styles (AOM house style). It helps users create self-contained, publication-ready exhibits.

A comprehensive guide for standardizing the presentation of findings in academic manuscripts, particularly for social science and public administration journals. It covers strict formatting rules for quantitative regression tables (three-line style, max 6 columns), variable definition sheets, qualitative data coding tables, and process/mechanism diagrams, ensuring methodological rigor and compliance with high publication standards.

A comprehensive guide for developing theory in the field of Human Relations (HR). This skill helps users move beyond simple constructs and box-and-arrow diagrams to build substantive theoretical arguments. It focuses on defining generative social mechanisms, grounding theory in social processes, specifying boundary conditions, and aligning theoretical form (process vs. variance) with the research phenomenon.

A comprehensive guide and checklist for ensuring that figures and tables in medical manuscripts comply with the rigorous reporting standards of JAMA. It covers mandatory elements such as CONSORT flow diagrams, reporting effect sizes with 95% CIs, structuring baseline characteristics, and avoiding statistical misrepresentation (e.g., using p-values instead of CIs).

A comprehensive guide for presenting complex statistical evidence in academic manuscripts, specifically tailored for journals like JAP. It covers APA 7th-edition standards for creating self-contained exhibits, including correlation matrices, CFA/SEM path diagrams, multilevel modeling (HLM) results, and meta-analytic plots. Emphasis is placed on reporting effect sizes, confidence intervals, and reliabilities.