Conduct a rigorous, two-lens peer review (editorial + technical-domain) of academic papers and produce a polished deliverable as Markdown, a Typst source, and/or a compiled PDF. Use this skill whenever the user asks to review, audit, critique, referee, proofread for rigor, or give peer-review feedback on a paper, manuscript, preprint, thesis chapter, or technical report — or to check a draft before submission, write a reviewer report or reviewer letter, or assess a paper's claims, statistics, citations, baselines, figures, or methodology — even if they never say the word "review". Especially relevant for ML / AI / RAG / IR / NLP / data-systems papers, where domain-specific methodological critique (missing baselines, gameable metrics, evaluation bugs) matters as much as editorial polish.
2026-06-07