Skill UI

This skill guides users in refining and polishing academic manuscripts intended for high-impact international journals focused on public administration and political science. It teaches how to transform single-case studies into arguments with a broad, comparative scope, ensuring the core contribution is front-loaded and clearly articulated for a global audience. Use this when structuring the introduction, tightening prose, or conforming to strict journal guidelines (e.g., word caps, abstract length, double-blind submission).

This comprehensive guide assists researchers preparing liver biology or disease studies for submission to Hepatology (AASLD). It meticulously reviews the journal's scope, evidence bar, reporting guidelines (e.g., CONSORT, STROBE), and required structure. It helps authors determine if their mechanistic or clinical work is rigorous enough and appropriately framed for a top-tier, international audience.

A detailed guide on positioning a computer architecture paper within the broader academic literature. It teaches authors to write a 'lineage with structural deltas,' moving beyond mere citation lists. The guidelines cover sweeping multiple databases (like dblp and IEEE Xplore), identifying concrete structural improvements (deltas), and adhering to strict academic rules, such as citing all authors and maintaining un-blinded bibliography integrity.

This guide provides rigorous structural and stylistic guidelines for writing top-tier academic papers in computer architecture (specifically for HPCA). It emphasizes building arguments around a single, measurable mechanism-to-behavior claim, quantifying the motivation with measured costs, ensuring all proposed mechanisms are checkable, and making sure every performance number is anchored to a specific instrument or simulation environment. It details the essential 'first-page arc' structure.

This comprehensive guide outlines the process for packaging artifacts from Human-Robot Interaction (HRI) research. Recognizing that HRI artifacts are complex, involving human subjects, robot behaviors, and extensive study materials, this skill provides guidelines for honest, ethical, and reproducible dissemination. It covers structuring de-identified data, analysis code, and behavioral specifications while adhering to participant consent and academic archiving standards.

This guide provides comprehensive methodological standards for conducting high-quality human-subjects studies in Human-Robot Interaction (HRI). It covers the entire research lifecycle, including choosing optimal designs (within/between-subjects), ensuring statistical power, reporting effect sizes, and adhering to ethical guidelines (e.g., Wizard-of-Oz disclosure). It ensures research meets the rigorous academic standards required for top-tier publications.

This guide provides comprehensive guidelines for advanced statistical and qualitative data analysis in Human Resource Management (HRM) research. It details the rigorous application of methods like Multilevel Modeling (HLM), Structural Equation Modeling (SEM), mediation testing, and fixed-effects models. It emphasizes methodological rigor, transparency, addressing endogeneity, and reporting effect sizes to ensure publication-quality empirical findings.

Provides comprehensive guidelines for selecting and rigorously justifying research designs across various academic paradigms: qualitative/ethnographic, critical, quantitative, and mixed-methods. This guide ensures the chosen methodology aligns perfectly with the theoretical question, emphasizing rigor, transparency, and advanced methodological considerations for academic manuscripts in Human Relations.

A comprehensive guide on how to structure the 'Related Work' section for top-tier theoretical computer science conferences (like ICALP). It teaches authors how to prove their results are genuinely new and better by precisely comparing their bounds against the best known literature, correctly crediting prior venues (STOC, FOCS, etc.), and maintaining academic rigor under double-blind guidelines.

Provides comprehensive guidelines for packaging research artifacts (code, data, logs) for submission to data mining conferences like ICDM. The focus is on ensuring the artifact is completely anonymous for triple-blind review (scrubbing Git history, paths, and internal names) and maximizing reproducibility, allowing reviewers to verify results with minimal effort.

A comprehensive guide for authors preparing submissions for top-tier data mining conferences (e.g., ICDM). It emphasizes the critical strategy of preemptively addressing potential weaknesses—such as baselines, variance, scaling, and data leakage—directly within the submitted manuscript, as author rebuttals are often not permitted. Also provides strict guidelines for drafting anonymous, high-impact responses if a rebuttal window is available.

A comprehensive guide for ensuring the reproducibility of data mining research for top-tier conferences like ICDM. It addresses the unique challenges of triple-blind review and strict page limits by detailing necessary artifacts, such as configuration files, seed lists, and anonymized packages. Learn how to achieve Rerunnable, Rebuildable, or Attested reproducibility tiers to solidify your scientific claims.