What problem does it solve? Drafting ML conference papers is slow and error-prone: researchers struggle with venue-specific formatting, narrative structure, and AI-generated citations that have a ~40% error rate and can cause desk rejection. ## Core Features & Use Cases - Structured Paper Workflows: Step-by-step checklists for drafting abstracts, introductions, methods, experiments, related work, and limitations sections targeting NeurIPS, ICML, ICLR, ACL, AAAI, and COLM. - Citation Verification Pipeline: Programmatic BibTeX retrieval via Semantic Scholar, CrossRef, and arXiv APIs to prevent hallucinated references, with explicit placeholder handling for unverifiable citations. - Official LaTeX Templates & Checklists: Ready-to-compile templates for six ML venues plus conference checklists, reviewer guidelines, and format-conversion workflows for resubmissions. - Use Case: Given a research repository with code and results, produce a complete first draft of an ICML submission with verified citations, then convert it to ICLR format after rejection. ## Quick Start Ask the assistant to read your research repository and draft a complete NeurIPS-style paper with verified citations using this skill.