Skill UI

A comprehensive audit tool designed to ensure academic paper compliance before submitting to major AI conferences like NeurIPS. It systematically checks adherence to the CFP, double-blind anonymity across all assets (PDF, code, data, supplement), correct track selection, and avoids common desk-rejection pitfalls, ensuring the submission is ready for OpenReview.

A comprehensive guide on structuring research papers for top-tier AI conferences (like NeurIPS). It teaches authors how to strategically allocate contributions, proofs, data details, and code artifacts across the main body, text appendices, and separate ZIP files. Use this skill to ensure reviewers can easily identify the core contribution and assess the work's validity without missing crucial supporting details.

This skill guides researchers through the rigorous validation process of their AI/ML paper for NeurIPS. It helps determine if the core contribution is robust enough for the main track, suggests the most appropriate NeurIPS track (e.g., E&D, Position), and refines the contribution type (Theory, Use-Inspired, etc.) to maximize the chances of acceptance.

This skill acts as a comprehensive router for the NeurIPS academic workflow. It guides users through every stage of preparing a machine learning manuscript, from initial topic and track selection to writing, mandatory paper checklist completion, OpenReview submission, handling the double-blind review process, and finalizing the camera-ready version. It helps ensure the manuscript is robust, evidence-backed, and adheres to all conference policies.

A comprehensive guide for refining machine learning research papers tailored for NeurIPS submission. It teaches how to structure introductions, calibrate claims with evidence, explicitly state limitations, and address mandatory academic checklist items (e.g., reproducibility, broader impact). Use this skill to transform technical writing into a reviewer-proof, high-impact academic manuscript.

This guide helps researchers determine the most appropriate venue (e.g., SIGMETRICS, NSDI, IMC, NeurIPS) for their computer systems performance evaluation research. It emphasizes that contributions must be rigorous performance-evaluation results—such as proven models, principled measurements, or guaranteed learning algorithms—rather than merely built systems or pure network measurements.

This guide helps researchers determine the most appropriate conference (UAI, AISTATS, NeurIPS, ICML, etc.) for their work, particularly when the core contribution revolves around uncertainty representation, probabilistic reasoning, or causal inference. It provides detailed filters and comparison points to refine the paper's scope and target audience.