research-paper-writing

To manage the iterative ML research loop, this Skill integrates experiment tracking with LaTeX document generation, allowing you to cycle between analyzing experimental findings and updating your publication-ready draft. It ensures your paper remains grounded in actual results.

To prevent citation hallucinations when writing an academic paper, this Skill programmatically fetches and verifies BibTeX entries using Semantic Scholar and arXiv. This ensures all references are validated before being included in your LaTeX draft.

The best way to structure experiment design for AI conferences is to use a Skill that targets major venues like NeurIPS and ICML, which helps you design experiments, track compute budgets, and log findings to ensure reproducibility and claim support.

Yes, you can generate a LaTeX draft directly from machine learning experiment results. This Skill integrates experiment tracking with LaTeX document generation to produce structured drafts grounded in your actual experimental data.

You do not need to manually format matplotlib plots for academic publication. This Skill uses the SciencePlots library alongside matplotlib and numpy to format experimental visualizations automatically for publication-ready documents.

To initialize a new project workspace for ML research and paper writing, use this Skill to generate a TODO list based on your current repository structure, streamlining the lifecycle from experiment design to literature review and drafting.