Skill UI

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 researchers submitting academic papers, detailing the standards required for scientific reproducibility. It covers fixing experimental environments (toolchains, datasets, hardware), differentiating result sources (simulation vs. physical measurement), statistical reporting best practices, and providing clear guidelines for code and data availability statements.

A detailed guide for authors submitting theoretical computer science papers, outlining the required evidence objects (proofs, certificates, code) and best practices for preparing the public, full version of the work. It emphasizes structuring arguments to meet the standards of top-tier academic conferences like STOC.

This guide provides a comprehensive strategy for authors submitting to top theoretical computing conferences (like STOC). It covers best practices for pre-emptive objection handling, structuring the main submission to anticipate critiques, responding effectively to chair-mediated technical clarifications, and planning the subsequent resubmission memo for maximum impact.

This guide details best practices for structuring supplementary material in highly technical academic submissions (e.g., STOC). It teaches authors how to design an appendix that facilitates selective verification by reviewers, ensuring that complex proofs, deferred results, and extended discussions are navigable. Key techniques include optimizing the Table of Contents, maintaining strict pointer discipline, and distinguishing between material required for core understanding versus material saved for the full version.

This comprehensive guide outlines best practices for designing and auditing empirical evaluations of formal verification tools and algorithms. It stresses the use of shared, community-accepted benchmarks, ensuring fair comparison against the strongest baselines with equal resources, meticulous reporting of all metrics, validating results for soundness, and guaranteeing full reproducibility on clean virtual environments.

This comprehensive guide details the rigorous review process used by top academic journals. It explains the tiered editor structure, the critical double-blind review model, and how to interpret various decision letters (desk reject, reject, revise and resubmit). It helps authors set realistic expectations, understand professional academic integrity rules, and prepare best practices for manuscript submission.

This skill provides a comprehensive guide for authors submitting art-history manuscripts to highly selective venues like The Art Bulletin. It evaluates whether a work possesses the necessary original, cross-disciplinary argument, deep visual/material analysis, and mastery of primary sources required for publication. The guide covers scholarly framing, structural requirements, and submission best practices, acting as a crucial fit and refinement aid for serious scholars.

A comprehensive guide for scholars targeting The Classical Quarterly, covering best practices in classical philology, textual criticism, and literary interpretation of Greek and Latin texts. It details the expected standard of a philologically controlled argument, manuscript handling, academic style, and official submission requirements to ensure the manuscript is a strong fit for the venue.

This comprehensive skill acts as a venue-selection and re-framing tool for authors targeting The Lancet Oncology. It provides deep insight into the journal's specific requirements, including methodological rigor (CONSORT, PRISMA), required evidence strength (Phase III RCTs, definitive biomarkers), structural elements (structured abstract, 'Added value' panel), and current submission best practices. Use this guide to assess if your clinical oncology manuscript is sufficiently advanced and impactful for this prestigious global journal.

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.

A comprehensive guide for designing, auditing, and reporting advanced statistical and causal inference experiments. It emphasizes moving beyond simple accuracy metrics to quantify uncertainty, model robustness, and causal mechanisms. Covers metrics like coverage, KL divergence, and regret, and outlines best practices for seeding, compute fairness, and creating standardized reporting blocks (e.g., UAI standards).