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 comprehensive guide for authors writing theoretical computer science papers. It details how to position a result within the existing academic lineage, ensuring rigorous citation practices, managing version discrepancies (preprint vs. journal), and honestly comparing bounds against prior work. Essential for meeting the high standards of top-tier conferences like STOC.

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.

A detailed guide for planning and executing research submissions for top-tier theoretical computer science conferences (STOC/FOCS). It provides a backward-planning timeline, covering all milestones from initial result completion (T-16 weeks) through drafting, verification, submission, peer review management, and final presentation preparation.

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.

A guide for academic authors on structuring the literature review section for top-tier accounting journals. It emphasizes moving beyond simple 'gap-spotting' and instead focuses on 'problematization'—identifying and resolving core tensions or unexplained results within existing scholarly debates. This skill helps frame the contribution by positioning the research within a live, ongoing academic conversation.

This skill details the rigorous process of converting raw statistical outputs into self-contained, publication-ready tables and figures for top-tier academic journals. It ensures compliance with strict house styles, covering sample selection, descriptive statistics, main regression results, and specific formatting for DiD, RDD, and event studies, all while adhering to strict page limits and providing economic magnitude interpretation.

This comprehensive guide serves as a router for structuring a high-quality academic manuscript targeting The Accounting Review (TAR). It does not write the content but diagnoses the current stage of the research—whether it's an initial vague idea, preliminary results, or addressing reviewer feedback—and recommends the next specialized skill module to use, ensuring a methodical progression from topic selection through final rebuttal.

This skill guides authors on positioning foundational and conceptually general robotics research for submission to The International Journal of Robotics Research (IJRR). It evaluates the manuscript's depth, generality, and conceptual framework, helping authors re-frame single-demonstration results into field-advancing principles. It also assists in choosing between IJRR and competing venues like IEEE Transactions on Robotics (T-RO).

This guide provides a rigorous methodology for achieving scientific reproducibility in technical and user experience papers (e.g., UIST). It details three essential 'ledgers': the Build Ledger (pinning software, hardware, and environment dependencies), the Measurement Ledger (defining precise protocols for metrics like latency and accuracy), and the Study Ledger (documenting human evaluation tasks). Following these protocols ensures that external researchers can accurately rebuild the artifact and validate reported results.

A comprehensive guide detailing the mandatory and optional evaluation phases for academic security artifacts, such as those required for USENIX Security. It covers Phase-1 (availability) checks and Phase-2 (functionality, reproducibility) requirements. Provides best practices for structuring code, managing technical hazards (e.g., dangerous-by-default behavior), and ensuring verifiable, reproducible results for peer review.