Skill UI

A comprehensive guide for conducting rigorous analysis for Accounting, Organizations, and Society (AOS) manuscripts. It covers both qualitative methods—such as coding, building evidence chains, and weighing counter-evidence—and quantitative techniques, including experimental, survey, and archival model estimation. The focus is on maintaining a fully reproducible audit trail and reconciling mixed-methods findings.

A comprehensive guide for authors facing a revise-and-resubmit (R&R) status in academic journals. It outlines the entire revision lifecycle, from building a structured response matrix and performing substantive re-analysis (re-coding, new experiments) to strategically drafting the point-by-point rebuttal letter. Focuses on maintaining academic rigor, reconciling conflicting reviews, and ensuring methodological consistency before final resubmission.

This skill prevents AI coding agents from generating generic interfaces by leveraging over 800,000 real web and iOS screens. It helps define design contracts, cover required states, and enforce quality gates during design, implementation, or review. Compatible with agents like Cursor, Copilot, and Claude Code, it ensures product-specific UIs grounded in actual user experience rather than hallucinated patterns.

This guide assists researchers in structuring and presenting academic evidence for top-tier journals like ASQ. It provides best practices for both qualitative (establishing transparent data-to-theory links, coding transparency) and quantitative (ensuring robust mechanism testing and alternative explanation checks) analyses. It ensures all theoretical claims are auditable and fully supported by the raw data.

A comprehensive guide designed to verify academic manuscripts against journal-specific guidelines (e.g., CEQ). It covers all critical aspects before submission, including citation style (author-date, not footnotes), abstract limits, keyword selection, JEL coding, document anonymity, and data reproducibility, ensuring a smooth submission process and minimizing formatting rejections.

A comprehensive guide detailing best practices for designing and auditing empirical evaluations of Large Language Models (LLMs). It addresses critical pitfalls such as data contamination, baseline unfairness, model version drift, and decoding parameter sensitivity, ensuring that published results are robust, reproducible, and scientifically sound for academic publication.

A comprehensive guide detailing how to structure and populate the supplementary material section of technical research papers, especially in NLP and Machine Learning. It distinguishes between load-bearing elements (main text) and audit trails (appendix), covering prompt verbatim, decoding configurations, full result grids, and code anonymization protocols to ensure high reproducibility and academic rigor.

A comprehensive guide for researchers in Human Relations to structure and report analyses, ensuring the direct, auditable link between raw evidence and theoretical claims. It covers best practices for qualitative coding (Gioia-style), critical interpretation, and advanced quantitative modeling, ensuring the final contribution is substantive and transparent.

A comprehensive guide detailing the ICASSP peer review pipeline, covering single-blind review mechanics, technical committee evaluation criteria, and the strategic importance of evidence, significance, and reproducibility in signal processing papers. Use this to craft submission strategies and decode decision letters.

This comprehensive guide details the end-to-end technical program committee (TPC) review process for major academic conferences like INTERSPEECH. It walks through every stage—from initial submission and area assignment to independent review, rebuttal, meta-review, and final decision. Users can learn how to decode reviewer feedback, understand the limitations of the rebuttal, and develop a strategic plan for future submissions and research refinement.

A comprehensive guide for authors navigating the rigorous, double-anonymized review process for top-tier academic journals like the Journal of Marketing (JM). This guide explains the roles of Coeditors, the criteria for substantive impact and relevance, interprets various decision letters (e.g., Major Revision, Conditional Acceptance), and outlines best practices for manuscript improvement and data transparency.

This guide provides comprehensive standards for defending rigorous data analysis in academic submissions, particularly for the Journal of Management Studies (JMS). It details specific requirements for both quantitative work (e.g., robustness checks, SEM, endogeneity) and qualitative work (e.g., coding transparency, abductive logic, trustworthiness), ensuring the analysis credibly supports the theoretical claims.