Skill UI

This guide details best practices for creating high-impact tables and figures for psychological science manuscripts, adhering to APA 7th edition guidelines. It emphasizes showing effect sizes, uncertainty (like confidence intervals), and embedding visuals directly within the main text near their first discussion, moving beyond traditional end-of-document placement. It covers data visualization principles and manuscript triage.

A comprehensive guide for designing and documenting systematic literature searches for meta-analyses or systematic reviews. It ensures reproducibility by covering multiple databases (e.g., PsycINFO, PubMed), controlled vocabulary, grey literature, and adhering strictly to PRISMA-S guidelines. It helps build a robust, traceable research pool.

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.

Provides comprehensive guidelines for designing tables and figures (exhibits) for academic manuscripts. The focus is on creating self-contained, highly accessible visualizations that effectively communicate quantitative effect magnitude and uncertainty to both specialized scholars and public practitioners, optimizing impact for publication.

A comprehensive framework for designing, auditing, and reporting empirical results in recommender systems research. It covers best practices for handling temporal splits, mitigating evaluation bias (e.g., exposure/popularity), utilizing advanced off-policy estimators (IPS, SNIPS), and structuring the critical link between offline metrics and real-world A/B test results. Essential for academic submissions.

This guide outlines best practices for creating quantitative exhibits in dynamic macroeconomics papers. It covers crucial components like Impulse-Response Functions (IRFs), calibration tables, moment-matching results, and policy counterfactuals. The focus is on ensuring the model's mechanism and quantitative fit are clear, legible, and self-contained for expert review.

This guide provides best practices for building self-explanatory, high-impact exhibits for interdisciplinary academic manuscripts. It covers the structured design of complex visualizations, including regression tables, bibliometric maps, event-study plots, and qualitative data structures. The goal is to ensure every figure and table carries the core innovation mechanism and is immediately interpretable by diverse readers (e.g., policymakers, managers, and economists).

This guide outlines the standards for creating publication-quality tables and figures for economics journals, particularly adhering to the rigorous 'REStat' house style. It ensures that the main findings are legible in a single, self-contained exhibit, emphasizing standard errors, clear notes, and load-bearing visualizations.

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 rigorous framework for designing reproducible and scientifically sound experiments in robotics and AI, specifically for academic publication (e.g., RSS). It guides users to shift focus from simple system demonstration to testing a falsifiable scientific claim. Key areas covered include deriving conditions from a claim, establishing hardware trial protocols, managing simulation vs. real-world evidence splits, and performing detailed failure attribution analysis.

Provides comprehensive guidelines for designing high-impact scientific figures for major academic journals. It covers technical aspects like optimal sizing, font legibility, and colorblind safety, while also enforcing strict data visualization practices (e.g., using dot plots over bars, defining 'n', ensuring data integrity). Use this as a final quality check before submission to guarantee scientific rigor and maximize publication chances.

This guide provides a comprehensive audit checklist for designing and evaluating sensor-based systems (SenSys). It mandates rigorous, real-world measurements across five core axes: energy efficiency, on-device latency distribution, ground truth accuracy, deployment realism, and embedded AI footprint. It ensures that research results are based on measured hardware performance, not simulations or idealized baselines.