Skill UI

These guidelines provide best practices for creating self-contained, rigorous, and ethically sound tables, figures, and exhibits for academic manuscripts, particularly within the New Media & Society domain. It covers various exhibit types, including quote-to-claim tables and network diagrams, emphasizing anonymization, showing uncertainty, and adhering to strict word count limits.

A guide for creating high-impact, conceptual figures and tables for academic review essays. Focuses on synthetic works like typologies, framework diagrams, and concept genealogies, which map arguments and relationships rather than plotting empirical data. Learn how to make your exhibits legible, claim-bearing, and highly reusable for maximum scholarly impact.

A comprehensive guide for constructing academic exhibits, such as conceptual framework diagrams, 'who-found-what' summary tables, and meta-science plots, for literature review and perspective pieces. It ensures adherence to APA style, emphasizes comparing like-with-like constructs, and mandates systematic data sourcing to maintain scientific credibility and reproducibility.

This skill guides the creation of standard, high-quality exhibits for meta-analysis manuscripts, specifically adhering to APA 7th edition standards required by journals like Psychological Bulletin. It generates critical figures such as PRISMA flow diagrams, forest plots, funnel plots, and summary tables, ensuring the final report is self-contained, reproducible, and scientifically rigorous.

A comprehensive guide for designing conceptual figures for highly theoretical manuscripts (e.g., Psychological Review). This skill teaches how to create two types of non-experimental exhibits: structured model/architecture diagrams and simulation-of-model-behavior plots. It emphasizes rigorous academic standards, including defining constructs vs. processes, distinguishing predictions from fits, and ensuring figures are self-contained and diagnostic.

A comprehensive guide for designing essential exhibits—such as PRISMA flow diagrams, forest plots, and evidence maps—for systematic reviews, meta-analyses, and critical syntheses. These exhibits are crucial for allowing readers to audit the search process and understand the synthesis at a glance, ensuring methodological rigor and clarity.

A comprehensive guide for constructing non-empirical, theoretical figures for sociological theory manuscripts. It details how to design conceptual exhibits—including mechanism diagrams, process models, typologies, and concept maps—that carry theoretical work. Crucially, it emphasizes that these figures must illustrate mechanisms or classifications, and must never include raw data, regression tables, or empirical results.

The Diagram Generator converts complex, unstructured inputs—such as natural language, code snippets, schemas, and notes—into professional, editable visual diagrams. It supports diverse standards including Mermaid, PlantUML, and Graphviz, enabling the creation of flowcharts, sequence diagrams, UML models, ER diagrams, architecture maps, and more. Outputs are designed to be reviewable, version-controllable, and highly precise.

This gate review forces a transition from vague product direction to a concrete, buildable technical specification. It mandates the creation of architectural diagrams, data flow maps, state machines, and failure mode inventories before a single line of implementation code is written. Essential for complex features with async components or external dependencies.

This skill provides comprehensive tools for creating and refining visual assets. It supports generating entirely new images, artwork, diagrams, or product mockups using advanced AI models. It also facilitates sophisticated image editing, allowing users to preserve, modify, or combine elements from existing visuals turn by turn. Ideal for designers, marketers, and content creators needing high-quality, context-aware visual content.

A systematic methodology for resolving discrepancies between conceptual models (documentation, tests, diagrams, metrics) and actual observed behavior (live code execution or real data). This process mandates that observed reality (the 'territory') always overrules the theoretical map, ensuring system integrity and accurate knowledge updates.