Skill UI

Workflow to parse external paper reviews, atomize reviewer concerns, and draft a grounded, venue-compliant text rebuttal under strict limits. Supports multiple reviewers, follow-up rounds, and safety checks to avoid fabrication and overpromising. Use when the user needs to reply to reviewers or run an ICML/OpenReview rebuttal.

This comprehensive guide serves as a pre-flight audit checklist for major academic conference submissions, specifically targeting ICCV. It covers critical process areas, including the single-day upload requirement, strict page budget adherence, maintaining double-blind anonymity, vetting OpenReview fields, and identifying potential identity or formatting leaks. Use this to prevent desk rejection before the deadline.

This comprehensive guide outlines the critical steps for authors preparing their accepted ICLR paper for camera-ready submission. It covers de-anonymization procedures, incorporating all fixes promised during the discussion period, completing OpenReview metadata, and assembling all necessary presentation assets, including posters, slide decks, and videos, according to current conference guidelines.

Provides a comprehensive guide for planning strategy within major academic conference review systems, such as OpenReview. It details the double-blind review lifecycle, author response strategies, managing public vs. private discussions, and understanding how permanent visibility impacts decision-making. Ideal for authors and reviewers preparing for high-stakes academic submissions.

This guide provides a comprehensive audit checklist for submissions to major AI/ML conferences like ICLR. It covers critical areas including maintaining double-blind anonymity, adherence to page limits, required LLM disclosure, and mitigating desk-reject risks. Use this before submission deadlines to ensure your paper is fully compliant and ready for OpenReview.

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.

This skill serves as a comprehensive router for managing the entire lifecycle of an ICML manuscript. It guides users through every phase, including topic selection, drafting, artifact preparation, OpenReview submission, double-blind review, author response, and final camera-ready publication. It helps track risks, evidence, and official conference policies.

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.