Skill UI

This skill helps researchers evaluate whether their manuscript aligns with the scope and practical orientation of specific academic journals. It provides structural advice, outlines key evidence requirements (e.g., patent quality, data analysis), identifies common rejection pitfalls, and guides the user through official submission checks for highly specialized journals.

This skill provides comprehensive guidelines for conducting and reporting high-stakes quantitative data analysis in the social sciences. It emphasizes methodological rigor, requiring honest reporting of uncertainty, probing robustness (specs that could break the result), and ensuring full reproducibility. It covers advanced topics like design-based inference, cluster corrections, and handling missing data, ensuring results withstand expert review.

This comprehensive guide outlines best practices for designing data systems evaluations, ensuring results are robust, reproducible, and convincing to expert reviewers. It covers workload realism, baseline fairness, necessary ablation studies, and advanced metric visualization techniques (e.g., sweeping curves, failure analysis) required for top-tier data systems research.

A comprehensive guide for addressing endogeneity, selection bias, and reverse causality in empirical research, specifically tailored for top-tier journals. It details advanced econometric methods (DID, IV, PSM, Heckman) and outlines best practices for conducting rigorous robustness checks, ensuring that identified effects are genuinely causal, not merely correlated.

This workflow router is designed for academic researchers targeting top-tier strategy journals. It does not write the paper but acts as a systematic guide, analyzing the current state of your manuscript—from vague ideas to final submission—and recommending the precise next specialized skill needed. It ensures that the paper is systematically improved across theory development, methodological rigor, data analysis, and contribution framing, guiding the author through the entire publication lifecycle.

A comprehensive guide for applying advanced quantitative methods (OLS, Multilevel, Survival Analysis) to large-scale social surveys (CGSS, CFPS, etc.). This guide emphasizes shifting the focus from purely statistical identification (econometric coefficient races) to deep sociological interpretation, focusing on revealing social mechanisms, structural heterogeneity, and real-world social consequences.

A comprehensive guide for executing and reporting complex social psychological analyses required for expert peer review. This skill covers norms for reporting uncertainty, assessing measurement quality, ensuring robustness checks, selecting appropriate statistical inference (e.g., multilevel models, clustering), and linking statistical findings back to the theoretical structure. It emphasizes methodological rigor and reproducibility.

A comprehensive guide for conducting and reporting rigorous empirical analysis in academic research, particularly in finance and accounting. It covers selecting appropriate estimators (e.g., DiD, 2SLS, RDD), handling fixed effects, ensuring statistical robustness through placebo and alternative testing, and fulfilling strict data-authenticity and code-access requirements required by top-tier journals.

A comprehensive guide for authors targeting The Astrophysical Journal (ApJ) or its Letters companion (ApJL). It helps determine manuscript fit, ensures methodological rigor, and outlines house style requirements, covering everything from data availability and systematic uncertainty analysis to article length limitations. This tool assists in structuring high-quality, archival-grade astrophysics research papers.

This tool helps authors determine if their food science work is best suited for a critical review or trend analysis within the scope of this specific journal. It guides authors to focus on synthesizing existing literature, identifying research gaps, and charting future directions, rather than presenting original primary experimental data.

This guide outlines the methodology for preparing research artifacts for rigorous academic reproducibility evaluations, such as those run by VLDB/SIGMOD. It details the required components—including prototype code, input data, workloads, and analysis scripts—to structure a comprehensive package. The goal is to ensure that any external evaluator can independently and flawlessly rebuild and validate all claimed results, maximizing the chances of winning reproducibility awards.

A comprehensive guide for assembling a reproducible data and code package for publishing economic manuscripts, specialized for World Bank Economic Review (WBER) standards. It details best practices for data availability statements, handling restricted microdata, pinning software versions, and ensuring full transparency throughout the analysis pipeline.