Skill UI

A comprehensive guide for academic researchers responding to R&R or conditional acceptance decisions for the American Economic Journal: Applied Economics (AEJ: Applied). This skill structures the response-to-referees letter and revision plan, emphasizing the triage of reviewer comments, adherence to editor priorities (especially concerning identification and robustness), and maintaining a professional, evidence-based tone. It helps authors transform critical feedback into a concrete, actionable revision strategy.

This guide provides a comprehensive, advanced strategy for academic researchers writing microeconometric papers for top-tier journals (e.g., AEJ: Applied). It teaches authors how to anticipate sophisticated, technical objections from expert referees (such as issues with causal inference design, selection bias, or parallel trends). The focus is on pre-empting these objections by integrating solutions directly into the manuscript text, rather than waiting for a formal revision letter.

This skill acts as a comprehensive workflow router for manuscripts targeting the American Economic Journal: Macroeconomics (AEJ: Macro). It guides the researcher through the entire publication lifecycle—from initial topic selection and literature positioning, through rigorous quantitative modeling (DSGE, SVAR), empirical identification, writing, to final submission and rebuttal strategy. It ensures the final paper meets the high standards of modern macroeconomics research.

This skill acts as a workflow router, guiding researchers through the entire manuscript lifecycle for submissions to American Economic Journal: Microeconomics (AEJ: Micro). It does not write the paper, but diagnoses the current bottleneck—whether it's topic selection, model refinement, empirical testing, or rebuttal strategy—and specifies which specialized aejmic-* sub-skill to invoke next, ensuring the paper aligns with the specific requirements of AEJ: Micro. Triggered when users are unsure what step to take next, or when revising based on reviewer comments.

This comprehensive guide helps computer vision researchers strategically position their work for the Asian Conference on Computer Vision (ACCV). It covers how to evaluate manuscript fit, restructure the narrative from general to conference-specific, optimize evidence presentation (including ablations and failure modes), and understand ACCV's regional niche compared to global flagships like CVPR or ECCV.

A comprehensive guide for authors aiming to submit machine learning manuscripts to the Asian Conference on Machine Learning (ACML). This skill helps evaluate manuscript fit, re-frame research from journal or arXiv styles, understand required contribution types (e.g., algorithms, theorems, benchmarks), and align the paper's evidence bar with the expectations of a top-tier, regional AI/ML venue. Use it for venue selection and submission strategy development.

This guide provides comprehensive guidelines for selecting, validating, and defending rigorous research designs for Contemporary Accounting Research (CAR) manuscripts. It covers various approaches—including panel archival, controlled experiments, analytical models, field studies, and surveys—while stressing critical rigor checks like identification strategy, internal validity, and model discipline. It also mandates adherence to ethical review processes and data integrity policies.

A comprehensive guide to establishing credible causal identification strategies for economic empirical research. It covers advanced quasi-experimental methods such as Difference-in-Differences (DID), Instrumental Variables (IV), and Regression Discontinuity (RDD), alongside structural modeling. The core principle emphasizes the necessity of explicitly stating, empirically defending, and testing all identification assumptions to meet the rigorous standards of top-tier academic economics journals.

A comprehensive guide for strengthening causal identification strategies in empirical finance and economics. It covers advanced quasi-experimental designs like Difference-in-Differences (DID), Instrumental Variables (IV), Regression Discontinuity (RDD), Propensity Score Matching (PSM), and Double Machine Learning (DML). Use this framework to stress-test your research design, address common econometric pitfalls, and ensure your paper meets the rigorous standards of top academic journals.

This skill provides comprehensive guidance for manuscripts aiming for publication in China Soft Science, an authoritative journal focused on soft science, technology policy, and national innovation strategy. It helps users assess if their research depth, policy implication, and evidence base meet the journal's high academic standards, and outlines common pitfalls and appropriate revision strategies.

This guide helps researchers assess if their work correctly spans the tri-community area of Information Retrieval, Data Mining, and Knowledge Management. It provides a detailed framework for comparing CIKM against major academic venues like SIGIR, KDD, WSDM, and SIGMOD/VLDB, ensuring the paper's scope is optimally matched to the target conference.

A comprehensive guide for positioning Natural Language Processing (NLP) research papers for the Conference on Computational Natural Language Learning (CoNLL). It helps authors optimize their manuscripts by focusing on conference fit, structuring evidence, identifying necessary baselines, and adapting the narrative for a selective CS conference audience. Essential for submission strategy and manuscript re-framing.