Skill UI

This guide provides rigorous guidelines for structuring theoretical papers submitted to the International Conference on Database Theory (ICDT). It emphasizes that proofs and formal evidence are the primary contribution, not benchmarks or experiments. It teaches authors how to match their specific theoretical claims (e.g., complexity, expressiveness) to the appropriate academic evidence, ensuring that any proposed experiment is proportional, honest, and merely supplements the core theoretical results.

This guide provides comprehensive best practices for designing and auditing empirical experiments for top-tier ML conferences. It covers necessary ablations, robustness checks (e.g., data shift, seeds), fair baselines, and detailed compute reporting to ensure that reported gains are scientifically genuine, reproducible, and address common reviewer skepticism.

This guide helps authors structure lengthy academic manuscripts, ensuring the main text maintains a clear, focused narrative while relegating supportive, detailed evidence to the appendices or supplementary materials. It outlines the 'determinative' principle, advising where to place hyperparameters, extended experiments, proof details, and code links to meet high journal standards and maximize readability for reviewers.

This skill guides researchers through the entire end-to-end lifecycle of submitting a research paper to a journal. It structures a 12-step pipeline, covering everything from initial topic selection and literature review to experimentation, reproducibility, submission, peer review, revision, and final camera-ready versioning. It acts as a comprehensive project dashboard for managing the entire academic publishing journey.

This guide provides a comprehensive framework for stress-testing Machine Learning experimental evidence before submission or rebuttal. It covers critical areas such as establishing strong baselines, conducting mechanism-isolating ablations, reporting variance, checking for data leakage, and disclosing compute costs. Use this to ensure your ML claims are scientifically sound, robust, and fair against rigorous academic scrutiny.

A comprehensive guide for designing and reporting rigorous experimental results in robotics research. It details the 'evidence ladder' (from simulation to real-world deployment), statistical best practices (e.g., sample size, confidence intervals), ensuring baseline fairness, and quantifying sim-to-real transfer gaps. Essential reading for researchers submitting to top-tier robotics conferences.

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 methodology guide for designing and auditing empirical studies in software engineering, specifically focused on maintenance, evolution, and change impact. It outlines best practices for ensuring evidence is proportional to the claim, managing data provenance (SHAs, criteria), handling statistical confounds (survivorship, effect sizes), and integrating rigorous LLM evaluation protocols.

This tool guides authors on the rigorous standards required for submission to IEEE Transactions on Industrial Electronics (TIE). It focuses on re-framing purely theoretical work into concrete, industrially relevant advances in control, power electronics, and mechatronics. It emphasizes the necessity of experimental validation, system-level industrial context, and adherence to specific journal guidelines.

This skill guides authors on submitting power conversion manuscripts to IEEE Transactions on Power Electronics (TPEL). It evaluates the manuscript's fit by checking for the required balance between theoretical novelty and rigorous experimental validation, covering topics like converter topologies, control methods, and wide-bandgap devices. Use it when evaluating a paper's suitability for TPEL or re-framing a design from a single build to a generalizable, highly benchmarked contribution.

This guide helps authors determine if their robotics research is suitable for IEEE Transactions on Robotics (T-RO). It focuses on establishing the necessary rigor, emphasizing that contributions must be complete and rigorously validated, preferably demonstrated on real robots or high-fidelity simulation platforms. It covers scope, experimental validation standards, and manuscript structure.

This guide provides rigorous standards for designing, validating, and reporting security research, particularly for high-stakes academic conferences like IEEE S&P. It covers critical areas including adaptive adversary modeling, measurement validity (sampling, ethics), statistical reporting, establishing strong baselines/ablations, and adhering to ethical constraints (IRB, authorization) to ensure evidence is robust and reproducible.