Skill UI

This guide outlines the standards for ensuring the verifiability and rigor of theoretical computer science papers, particularly for venues like ICDT. It mandates complete proofs, precise pinning of models and assumptions, explicit matching of upper and lower bounds, and strict internal consistency across all claims and full versions. Use this checklist before submission to elevate academic quality.

A comprehensive guide for designing, executing, and reporting high-stakes research studies in Software Engineering (SE), tailored to meet the stringent standards of top academic conferences like ICSE. It covers best practices for evidence-claim matching, advanced statistical rigor (non-parametric tests, effect sizes), handling AI model ablations, and implementing threats-driven design to ensure findings are robust, valid, and maximally reproducible.

A comprehensive guide for authors targeting IEEE Transactions on Information Theory. It assesses whether a manuscript meets the journal's extremely high theoretical standards, focusing on theorem-driven proofs, matching achievability and converse arguments, and fundamental information-theoretic limits (e.g., channel capacity, rate-distortion). Ideal for authors needing a fit check or re-framing applied results into foundational theory.

A comprehensive guide for quantitative economists to assess if a research topic meets the 'rigor and generality' standards of the International Economic Review (IER). It helps users determine the scope of their contribution, matching it against archetypes like pure theory, structural macro, econometrics, and causal micro, ensuring the work has broad appeal beyond a specific field.

This guide provides comprehensive guidelines for designing, conducting, and auditing empirical studies in software engineering. It emphasizes achieving scientific rigor by matching every claim to specific, measured evidence, ensuring fair baselining (equal budget), and applying appropriate advanced statistics (non-parametric tests, effect sizes). Essential reading for researchers submitting papers based on performance evaluations.

This guide outlines the rigorous standards for presenting 'evidence' in highly theoretical computing venues. It emphasizes that results must be supported by deep mathematical proofs, establishing matching upper and lower bounds (tightness), and meticulously auditing all underlying model assumptions (e.g., timing, failure modes). It shifts focus from empirical benchmarks to formal correctness.

This skill guides authors through the complex process of selecting the optimal academic journal venue for their research. It profiles the paper's characteristics (discipline, method, contribution) and shortlists potential targets from a large index. It scores candidates based on fit, acceptance odds, turnaround time, and audience, providing a tiered recommendation (Reach, Match, Safe) and a detailed resubmission ladder. Ideal for rejected drafts or papers needing a first target.

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 detailing when and how to structure theoretical models in development economics research, particularly for World Bank Economic Review submissions. It emphasizes that models must serve a functional purpose—such as interpreting empirical coefficients, running policy counterfactuals, or aggregating to welfare statements—rather than being decorative. Key principles cover parameter identification, matching structural frictions to the local setting, and handling counterfactual assumptions (Lucas critique).