research-paper-writing

To write an ML paper for NeurIPS or ICML, this skill enforces a citation verification workflow using Semantic Scholar and arXiv/CrossRef to prevent citation hallucinations, mapping each paper claim to specific experiments and evaluation protocols.

To map experiment results to claims in a machine learning research paper, this skill enforces experiment-to-claim discipline by linking each paper claim to specific experiments, monitoring execution, and analyzing results to ensure direct evidence-backed support.

You can convert your ML paper format across different conference venues like ICLR because the skill supports venue-aware paper structuring and submission preparation, allowing iterative revision and format conversion tailored to specific conference requirements.

The research paper writing workflow supports LaTeX formatting and Python experiment tracking by orchestrating execution monitoring alongside analysis, utilizing dependencies like scipy, numpy, matplotlib, and SciencePlots to generate experiment-driven results for your draft.

To respond to peer reviews for a machine learning submission, the skill provides an iterative self-review loop that guides experiments-driven revision, allowing you to address reviewer feedback by aligning claims with updated evaluation protocols and verified evidence.