Skill UI

This tool guides the transformation of academic manuscripts, particularly in the social sciences, from a policy-report or work-summary tone into a rigorous scholarly argument. It helps users replace vague slogans and empty claims with concrete theoretical mechanisms, ensuring the writing maintains a scholarly, restrained, and macro-perspective, and that every paragraph centers on a discernible core proposition.

A comprehensive guide for authors submitting to high-impact scientific journals. It outlines the essential principles for structuring experiments and presenting evidence, emphasizing claims-evidence matching, fair benchmarking, differentiating result sources (simulation, hardware, theory), and ensuring statistical rigor. Covers best practices across computer science, control, communication, and microelectronics.

A comprehensive guide for authors submitting technical papers, detailing methods to ensure that all reported claims, figures, and benchmark numbers can be independently regenerated. This involves creating VM-ready, self-contained artifacts, mapping every claim to a specific script, and defining clear reproducibility levels (Turnkey, Scripted) to satisfy conference requirements.

This guide provides comprehensive rules for structuring academic technical papers, specifically addressing the division of content between the main body, appendices, supplementary websites, and mandatory artifacts. It ensures that all decision-critical arguments, soundness claims, and core comparisons are contained within the primary reviewed pages, making the paper fully judgeable without external resources.

This guide provides comprehensive rules for authors submitting research papers to verification experts (TACAS/LNCS). It emphasizes stating the core contribution immediately, explicitly claiming soundness or completeness, supporting empirical claims with fair and reproducible benchmarks, and aligning the paper structure with its specific category (research, tool, case study).

This guide instructs researchers on how to build robust, theoretically grounded economic mechanisms and derive testable predictions for accounting and finance research. It covers the methodologies required for archival, analytical, and experimental papers, ensuring claims move beyond mere correlation to establish clear causal economic channels.

This guide details the methodology for packaging scientific artifacts (code, solvers, data, logs) for machine learning submissions. It focuses on creating double-blind, self-contained ZIP archives that allow reviewers to independently and quickly verify complex probabilistic claims, even under strict time constraints. It covers best practices for anonymity, structured README generation, and handling data/licensing.

This guide details advanced best practices for achieving scientific reproducibility in probabilistic modeling and deep learning research. It mandates rigorous disclosure of seeds, hyperparameters, diagnostic traces (e.g., R-hat, ELBO), and compute environments to ensure that all claims made in academic papers are verifiably backed by traceable evidence. It elevates reproducibility from a simple code run to a comprehensive audit.

A comprehensive guide for authors submitting to venues focused on probabilistic AI, including UAI. It emphasizes achieving maximum formal rigor by enforcing strict notational discipline, clearly separating conditioning from intervention, explicitly stating assumptions, structuring the main body for maximum impact, and calibrating claims to match empirical evidence. Essential for papers involving graphical models, causality, and Bayesian methods.

Provides comprehensive guidelines for designing rigorous technical evaluations for interactive systems, specifically tailored for top-tier venues like UIST. It emphasizes matching evaluation methods to specific artifact claims, reporting distributions over means, closing the interaction loop, and performing thorough ablations against real-world baselines. This framework ensures the scientific rigor and credibility of submitted research.

A comprehensive guide to navigating the UIST (User Interface Software and Technology) academic review pipeline. It details every stage, from initial submission and expert review to the rebuttal phase and final committee decisions. The guide teaches authors how to critically analyze reviewer feedback, identify evidence gaps, strengthen novelty claims, and strategically plan for conditional acceptance or resubmission.

A comprehensive guide for producing the mandatory supplementary video figure for UIST submissions. This material treats the 3-minute video as core evidence, detailing requirements for resolution (1080p/4K), codec (H.264), and anonymity. It guides authors on scripting the video around key claims, ensuring uncut interaction loops, and maintaining academic rigor to convince reviewers of the system's reality.