Skill UI

This guide assists researchers in determining the best fit for their data mining research project. It compares the contribution against major conferences (ICDM, KDD, SDM, etc.) and helps decide the appropriate track (Research, Applied, Blue Sky) based on the nature of the mechanism, evidence, and intended scope for optimal academic impact.

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.

A comprehensive guide for political science scholars to pressure-test their research ideas against the standards of the American Political Science Review (APSR). This skill emphasizes demonstrating general disciplinary significance (matters to the field) over mere subfield novelty, guiding users through framing, contribution type, and appropriate track selection.

This guide assists researchers in determining the optimal venue for their theoretical machine learning contributions. It provides a framework for evaluating whether a result is best suited for COLT, or if it belongs in other top-tier conferences (e.g., NeurIPS, ICML) or journals (e.g., JMLR). It emphasizes differentiating between rigorous mathematical proofs (bounds, separations) and purely empirical findings.

Provides a comprehensive guide for drafting academic abstracts for top-tier public administration journals, such as Chinese Public Administration (CPA). It enforces a strict four-part structure (Question $\rightarrow$ Theory $\rightarrow$ Findings $\rightarrow$ Contribution), ensures semantic alignment between Chinese and English versions, and guides authors on avoiding vague, empty phrases. Use this guide when revising manuscripts before submission to meet high journal standards.

This guide provides essential instructions for authors submitting research to the ECAI conference, focusing on creating credible and reproducible artifacts. It strongly cautions authors about misconceptions regarding separate artifact badges, emphasizing that credibility must be built into the paper and supplement for direct reviewer judgment. Guidelines are provided for matching the artifact (code, proofs, data) to the specific contribution type (Theory, ML, Optimization).

A comprehensive self-auditing guide designed for authors preparing manuscripts for submission to top-tier academic journals. It systematically addresses recurring, structural, and critical flaws across theory contribution, econometric identification, mechanism explanation, heterogeneity analysis, and writing conventions. This tool helps authors proactively identify and correct potential weaknesses before facing peer review.

This guide helps academics precisely define the marginal contribution of a finance research manuscript relative to the existing body of literature. It provides structured advice on how to articulate the 'delta'—the new fact or mechanism—and appropriately size the claim to match the scope of a generalist journal, avoiding pitfalls like over-claiming or mis-targeting.

This guide helps researchers determine if their Machine Learning project is suitable for ICLR, or if a different top-tier conference (such as NeurIPS, ICML, or CVPR) is a better fit. It emphasizes the need for deep representation-learning insights, mechanistic understanding, and theoretical contributions over mere application performance.

This guide helps transform technically sound machine learning drafts into highly readable, conference-ready papers suitable for ICLR and OpenReview. It emphasizes structuring the core learning-representation insight upfront, clarifying contribution types (method, theory, benchmark), optimizing for public skimming, and providing clear reviewer navigation paths. Use this when ensuring maximum impact and clarity in a challenging academic review environment.

A comprehensive guide for researchers deciding the best venue for their machine learning manuscript. It helps evaluate whether a paper is a strong fit for ICML, or if it should be rerouted to specialized tracks, other top-tier conferences (NeurIPS, ICLR), or academic journals based on its core contribution type (theory, method, application) and novelty.

A comprehensive guide to strategically routing robotics research projects. This skill helps determine whether a project is best suited for ICRA, IROS, CoRL, RA-L, or other specialized venues. It evaluates your work based on the physical embodiment test, contribution type (e.g., HRI, manipulation, perception), and provides a decision framework between direct conference submission and journal publication.