Skill UI

A comprehensive guide detailing the rigorous data analysis and reporting standards required for top-tier psychological science journals. It emphasizes moving beyond p-values to report effect sizes with confidence intervals, ensuring full disclosure of exclusions, and maintaining a clear separation between confirmatory and exploratory findings to enhance research credibility and reproducibility.

A guide for structuring the theoretical framework and hypotheses section of a psychological science manuscript. It emphasizes academic rigor, research transparency, and the necessity of clearly separating confirmatory (preregistered) predictions from purely exploratory findings. Learn how to state a clear mechanism, derive directional hypotheses, and avoid 'HARKing' to strengthen your scientific argument.

This guide helps researchers determine if their academic project is best suited for Quantitative Economics (QE) among top economic journals. It provides a structured fit test, guiding users to sharpen the substantive economic question and confirm that the quantitative method is necessary—not merely decorative—to answer it. It helps situate research by comparing QE against pure theory and method-first outlets.

A comprehensive guide for drafting persuasive and structured author responses to major academic conference reviews. This methodology teaches how to triage critical concerns, anchor answers strictly to submitted evidence (tables, appendices, repository), and effectively counter common technical pushbacks such as baseline tuning, temporal data leakage, and offline-online gaps, ensuring the response is professional, anonymous, and high-impact.

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.

A comprehensive guide for revising academic papers in recommendation systems (RecSys). It covers best practices for structuring the first page, ensuring rigorous evaluation protocols (e.g., temporal splits, equal-budget baselines), and avoiding methodological overclaims, helping authors meet the standards of top-tier AI conferences.

A comprehensive guide for authors submitting finance manuscripts. This skill helps assess whether a paper meets the high methodological and conceptual standards required by premier finance journals like Review of Financial Studies (RFS), Journal of Finance (JF), and Journal of Financial Economics (JFE). It covers required rigor in asset pricing, corporate finance, methodological innovation, and structural presentation.

A protocol designed to rehearse the rigorous academic peer review process before actual submission. It simulates an Associate Editor's initial desk-screen and incorporates multiple independent referee perspectives (e.g., identification, robustness). The output provides a calibrated decision band and a prioritized, skill-mapped list of required fixes, helping authors predict and address potential weaknesses.

A specialized guide for researchers assessing manuscript suitability for securities and capital market journals. It analyzes the journal's positioning, preferred topics (e.g., market regulation, corporate governance, asset pricing), required methodologies (e.g., DID, event studies), and common submission pitfalls. Use this skill to structure your paper and improve its fit before formal submission.

A comprehensive guide detailing the mandatory steps for converting an accepted, double-anonymous conference paper into a final, journal-ready (camera-ready) version for academic publication. This process covers de-anonymization, completing journal metadata (DOI, ORCID, CCS), incorporating shepherd-required revisions, and ensuring permanent reproducibility.

This guide details the rigorous academic review process for ACM Symposium on Cloud Computing (SoCC) submissions. It explains the dual-anonymous, two-round review structure, the importance of incorporating both systems performance and data measurement rigor (including tail latency and cost), and how the rebuttal period functions. Essential reading for authors aiming to publish in top-tier cloud computing venues.

A comprehensive guide for authors submitting theoretical computer science papers to SODA. It clarifies the distinction between SODA (theory-led) and ALENEX (experiment-led), advising on when and how to incorporate numerical evidence. The protocol details best practices for honest numerics, including instance generation discipline, claim-class labeling (illustration, evidence, motivation), and proper submission routing to ensure academic rigor.