Skill UI

This guide provides a comprehensive checklist for evaluating Electronic Design Automation (EDA) research manuscripts for top-tier conferences like DAC. It emphasizes the necessity of using standard, recognized benchmark suites (e.g., ISPD, EPFL, TAU), comparing results against the true State-of-the-Art (SOTA), reporting full PPA metrics and runtime, and implementing ablation studies to ensure fairness, reproducibility, and generalizability, especially for ML-based methods.

A comprehensive guide for economists on refining theoretical models and structures for publication in high-impact journals. It provides rigorous best practices for developing pure economic theory, executing structural quantitative modeling, and ensuring models effectively interpret empirical estimates, focusing on generalizability and interpretability.

This skill guides researchers on evaluating whether their scientific findings—especially those from regional case studies—meet the global, process-level significance required by high-impact journals like EPSL. It provides a structured framework to test for generalizability, process constraint, and global relevance, ensuring the manuscript frames a true scientific frontier rather than an incremental local observation.

A rigorous self-assessment tool for academic manuscripts in game theory and economic behavior. It guides the identification of core assumptions, tests the logical architecture of proofs, and verifies the generalizability of results. It covers both theoretical proofs (e.g., equilibrium, mechanism design) and experimental designs, ensuring the argument withstands specialist scrutiny.

This comprehensive skill guides authors through evaluating if their technically rigorous economics manuscript (covering theory, econometrics, or macroeconomics) is suitable for the International Economic Review (IER). It analyzes the paper's formal contribution, methodological rigor, generalizability, and adherence to high academic standards, helping authors refine their work before submission.

This guide assesses the suitability of research manuscripts on sustainability, cleaner technologies, and circular economy for the Journal of Cleaner Production. It provides detailed criteria covering contribution generalizability, methodological transparency (e.g., LCA scope, system boundaries), necessary framing for submission, and adherence to high academic standards.

This skill assesses whether a research question or study aligns with the scope of top-tier public economics journals, such as JPubE. It guides the user to evaluate the government's economic role, the welfare implications, and the international generalizability of the findings, providing a critical review pass before submission.

A comprehensive guide for authors submitting new methods, tools, or technologies to Nature Methods. It details the journal's specific requirements, including the mandatory criteria for generalizability, rigorous benchmarking against state-of-the-art alternatives, and the required structure and house style for achieving publication success in life sciences.

This skill rigorously stress-tests a clinical study's potential impact against the standards of top-tier medical journals (e.g., NEJM, Lancet, JAMA). It evaluates three critical gates: clinical importance (patient outcomes), methodological rigor (study design), and generalizability (broader applicability). Use this to determine if a study is merely incremental or if it genuinely sets a new standard of care, helping authors decide the best publication venue.

This guide provides a comprehensive strategy for elevating the credibility of finance manuscripts to the standard of top-tier journals. It details rigorous methods for empirical causal identification (DID, IV, RDD, natural experiments) and ensures theoretical rigor by assessing assumptions, generalizability, and proof exposition. It helps researchers stress-test their work's core logic.

A comprehensive guide for authors submitting papers to web conferences. It details how to structure empirical claims, ensuring evidence matches the claim's scope (scale, freshness, generalizability). The guide covers avoiding methodological flaws like temporal or popularity leakage, selecting datasets with proper provenance, and performing rigorous ablations and statistical analysis for maximum academic rigor.