Skill UI

Structured manuscript and grant review guidance that walks reviewers through methodology, statistics, reporting compliance, and constructive feedback for rigorous scientific evaluation across disciplines.

A specialized tool for searching published, peer-reviewed academic papers from major venues like IEEE, ACM, and Springer. Unlike preprint services (like arXiv), this skill provides verified citation counts, detailed venue metadata, and filtering options for finding established, high-impact research literature. Ideal for formal academic research and literature reviews.

Runs a SHA-pinned integrity-forensics sweep on a paper (evidence ledger, auditor proposals, rules-only report), converting verdicts into typed policy gates (BLOCK/WARN/NO_NEW_BLOCKER) and an obligations ledger. Used for pre-submission integrity checks, peer-review forensics and resubmission pipelines.

Provides comprehensive guidelines for achieving scientific reproducibility in academic research, particularly for control systems, automation, and pattern recognition. It covers fixing environment dependencies, data handling, random seeds, and process pipelines, helping authors withstand rigorous peer review questioning and build verifiable research artifacts.

This comprehensive guide details the structured method for drafting author responses to peer reviewers' comments for major conferences like AISTATS. It covers best practices for addressing statistical validity, novelty, and technical concerns in a constrained, anonymous, text-only format. Learn how to triage objections, anchor claims to existing evidence, and handle specific statistical pushback patterns.

This guide provides a comprehensive framework for understanding the academic peer review process used at AISTATS, covering everything from OpenReview submission workflows and author-reviewer dynamics to advanced decision criteria (correctness, significance, empirical support). It helps researchers strategize their submissions and responses to maximize acceptance chances in statistics and machine learning.

A comprehensive guide for preparing academic exhibits (tables, figures, appendices) for the American Journal of Sociology (AJS). This skill ensures strict compliance with journal house conventions, covering consecutive numbering, lettered appendices, mandatory alt text, and proper post-text placement. It helps authors structure their arguments visually and pass through rigorous peer review and production stages.

This skill provides a comprehensive pre-submission checklist for manuscripts targeting Atmospheric Chemistry and Physics (ACP), an EGU journal. It details the journal's scope—covering aerosols, clouds, chemistry, and dynamics—and outlines the rigorous standards required, including open-access policies, mandatory FAIR data practices, detailed methodological transparency, and preparation for interactive public peer review. Use this guide to assess the fit and completeness of your scientific work.

A comprehensive guide for executing and reporting quantitative, computational, or experimental analyses for top-tier academic journals (e.g., BJPS). It outlines stringent norms for ensuring methodological rigor, transparency, and reproducibility, covering topics like uncertainty reporting, robustness testing, and measurement equivalence. This skill is essential when structuring the results section of a manuscript, particularly when facing expert peer review or preparing for data deposition.

A detailed guide for scrutinizing statistical inference in high-level econometric manuscripts, designed to withstand rigorous technical peer review. It covers critical areas such as selecting appropriate clustering levels, handling weak instrumental variables (IV), correcting for multiple hypothesis testing, and ensuring accurate standard error reporting for panel data and causal inference models.

A comprehensive guide for authors submitting to high-level academic journals, specifically detailing how to construct a point-by-point rebuttal letter after receiving R&R (Revision & Resubmission) feedback. Covers advanced econometric topics, identifying common reviewer critiques (e.g., parallel trends, robustness checks, endogeneity), and structuring professional, evidence-based revisions.

A comprehensive methodological guide for conducting exhaustive robustness checks in academic economics manuscripts, particularly for journals requiring high empirical rigor. It provides an exhaustive checklist covering variable replacement, sample window manipulation, advanced identification strategies (like PSM-DID and placebo tests), and crucially, systematically ruling out alternative explanations and confounding factors. Mastering this process is essential for defending research findings against rigorous peer review.