What problem does it solve? Producing a publication-ready ML/AI paper requires coordinating literature review, experiment design, statistical analysis, LaTeX writing, citation verification, and venue-specific formatting — a process where missed baselines, hallucinated citations, or formatting errors cause desk rejection. ## Core Features & Use Cases - End-to-End Pipeline: Covers project setup, iterative literature search, claim-to-experiment mapping, execution monitoring with cron patterns, statistical analysis (McNemar's test, bootstrapped CIs), drafting, self-review, and submission. - Citation Verification: Fetches BibTeX programmatically via Semantic Scholar, CrossRef, and arXiv APIs with a mandatory 5-step verification workflow to prevent hallucinated references. - Venue Templates & Checklists: Ships official LaTeX templates for ICML 2026, ICLR 2026, NeurIPS 2025, ACL, AAAI 2026, and COLM 2025, plus mandatory checklist requirements per venue. - Use Case: Given an existing codebase with experiment results, design the remaining experiments, run them with monitoring, compute significance tests, and produce a complete ICML-formatted draft with verified citations. ## Quick Start Use the research-paper-writing skill to turn my experiment results in this repository into a full ICML submission draft with verified citations and statistical analysis.