Skill UI

This skill provides a comprehensive framework for authors targeting top-tier AI conferences like NeurIPS. It guides researchers in refining their manuscript's narrative, ensuring the contribution meets the venue's standards for broad, high-novelty machine learning research. It covers topic fit, evidence bar requirements, structuring the argument, handling interdisciplinary claims, and navigating the specific submission cycle rules for maximizing acceptance chances.

A detailed guide for authors drafting responses, rebuttals, and discussions for major conferences like NeurIPS. It emphasizes adhering to strict double-blind constraints, focusing on technical evidence rather than arguing tone, and structuring responses to clarify misunderstandings and move reviewers toward an accurate decision.

A comprehensive guide for stress-testing the evidence supporting ML claims before submitting to top-tier academic conferences. Covers rigorous auditing of baselines, ablation studies, robustness checks, data leakage, and calibrating claims against evidence strength, ensuring scientific reproducibility.

This skill guides authors on how to strategically position their machine learning research against complex academic landscapes. It covers comparing work to neighboring conferences (ICML, ICLR), analyzing contemporaneous preprints, and defining the exact technical delta from prior methods to minimize novelty risk for top-tier AI venues.

A comprehensive guide for authors submitting to machine learning conferences like NeurIPS. It details the rigorous standards for data splitting, hyperparameter reporting, random-seed protocols, and compute transparency required for a main-track paper. It also helps authors strategically decide between the main track and the MLRC/TMLR routes based on their core contribution (replication vs. novelty).

This skill simulates the rigorous peer review process used in top-tier AI and machine learning conferences (like NeurIPS). It helps users diagnose potential weaknesses in a submission regarding quality, clarity, significance, ethical compliance, and reproducibility. It guides the identification of decision-relevant issues that ACs and reviewers are likely to raise, helping authors strategically plan responses for revision.

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.

A comprehensive guide for authors preparing accepted academic papers for artifact evaluation at major conferences (e.g., NSDI). It details how to package code, traces, and testbed recipes to ensure permanent public availability, achieve recognition badges (Functionality, Availability, Reproducibility), and meet community award criteria. Learn best practices for reproducibility and structured submission.

A comprehensive guide to interpreting the structural mechanics of large-scale, multi-deadline academic conferences (like NSDI). It details the difference between acceptance, one-shot revision, and rejection, explaining the strategic implications, associated bans, and the required scope for resubmission. Learn how to decode reviewers' feedback, identify critical baselines, and manage your research workflow across multiple cycles.

This comprehensive guide assists researchers in determining the appropriate scope and venue for their networked systems research. It details the core requirements for top-tier conferences like NSDI, distinguishing between Research, Operational Systems, and Frontiers tracks. Furthermore, it provides critical routing advice for differentiating contributions between related venues such as OSDI, SIGCOMM, and FAST, ensuring the work aligns with the correct technical focus.

A comprehensive guide detailing the cyclical workflow for major academic conferences (e.g., NSDI). It helps research teams strategically plan multi-year projects, managing multiple submission gates (Spring/Fall), revisions, and long waiting periods. Essential for structuring research efforts and avoiding common pitfalls like double-submission or missed deadlines.