Skill UI

A comprehensive guide for academics submitting AI and statistical research papers. It details how to map every claim (theoretical, algorithmic, or empirical) to verifiable evidence, covering necessary details like datasets, hyperparameters, random seeds, compute resources, and uncertainty estimates. It outlines the standards for achieving high levels of reproducibility, from descriptive to turnkey, ensuring scientific rigor for top-tier conferences.

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.

This guide provides comprehensive best practices for preparing supplementary materials for major academic conferences like AISTATS. It details the proper structure and handling of appendices, mathematical proofs, code archives, and additional data tables, ensuring that the main paper remains fully understandable even if the supplementary material is ignored by reviewers. Essential for publishing high-quality, robust research.

This guide helps researchers assess the optimal venue for their work, determining if their project aligns best with AISTATS, or if it is better suited for major ML conferences (NeurIPS, ICML, ICLR), UAI, or specific statistics journals. It emphasizes the need for strong statistical foundations, inference guarantees, and the unique intersection of AI, ML, and statistics before starting the writing process.

This guide provides comprehensive rules for authors revising technical papers for top AI/Statistics conferences like AISTATS. It emphasizes achieving statistical rigor, explicitly stating assumptions, pairing every claim with proof or evidence, ensuring conciseness (e.g., adhering to an 8-page limit), and maintaining double-blind academic style. Perfect for PhD students or researchers preparing manuscripts for highly technical review.

This guide details the rigorous data analysis and reporting standards required for top-tier academic journals, such as AJPS. It focuses intensely on reproducibility, ensuring that all results—from primary findings to exploratory models—are generated by a single, seeded master script. Key requirements include reporting uncertainty intervals, proving robustness, adhering to pre-registration discipline, and accommodating mandatory third-party code verification. Use this when conducting complex empirical research in social sciences or political science, needing to withstand intensive methodolo...

This guide provides detailed instructions for structuring the introduction of academic manuscripts, particularly for high-stakes, double-blind review journals. It teaches authors how to frame their research contribution by engaging a live, recognized academic debate, precisely locating the marginal intellectual gap, and strictly maintaining anonymity through third-person self-citation.

A comprehensive guide for strengthening the methodological rigor of academic manuscripts, particularly for high-stakes journals. It covers advanced techniques for causal identification (e.g., Difference-in-Differences, Instrumental Variables, Regression Discontinuity), pre-analysis planning, linking theory to empirical tests, and defending claims against skeptical reviewers. This skill focuses on methodological architecture, not code.

This guide outlines the stringent standards for creating tables and figures (exhibits) for the American Journal of Political Science (AJPS). It emphasizes that exhibits must be self-contained, highly accessible, and, critically, regenerated exactly by deposited code for third-party verification. It provides best practices for structuring research results, managing word counts by differentiating between core arguments in the main text and supporting details in the Supporting Information (SI).

A comprehensive guide for structuring academic arguments, specifically tailored to the high theoretical standards of top-tier journals like AJPS. It helps researchers transform initial ideas into a rigorous framework, defining core mechanisms, stating directional and testable hypotheses before results, and establishing clear scope conditions for empirical, formal, or methodological papers.

This skill provides a rigorous framework for political scientists to pre-test their research projects against the standards of the American Journal of Political Science (AJPS). It evaluates whether a topic has a sharp, generalizable question, demonstrates strong causal identification, provides a substantive theoretical contribution, and fits the appropriate submission type (Article, Research Note, Correspondence). Use this before committing significant time to writing.

This is the comprehensive orchestrator for submitting manuscripts to the American Journal of Political Science (AJPS). It guides users through the entire academic writing lifecycle, helping them determine the correct submission type (Article, Research Note, Correspondence) and ensuring critical steps, such as mandatory third-party code verification, are integrated from the start. It dispatches to specific sub-skills rather than drafting content.