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Found 3 Skills
Paper reviewer that evaluates machine learning research projects following official ICML reviewer guidelines. Provides comprehensive reviews with actionable feedback across all key dimensions: claims/evidence, relation to prior work, originality, significance, clarity, and reproducibility. Also provides formative feedback on incomplete drafts, proposals, and research code repositories. MANDATORY TRIGGERS: review paper, ICML review, paper review, evaluate paper, research paper feedback, ML paper review, conference review, academic review, paper critique, NeurIPS review, ICLR review, project proposal, research proposal, paper draft, early feedback, incomplete paper, work in progress, WIP review, review repo, review codebase, research project review
Critiques ML conference papers with reviewer-style feedback. Use when users want to anticipate reviewer concerns, identify weaknesses, check claim-evidence gaps, or find missing citations.
Audit whether an ML or AI paper's experimental baselines are necessary, fair, current, and reviewer-proof. Use this skill whenever the user is planning experiments, comparing methods, choosing baselines, worried about missing SOTA or unfair comparisons, preparing a reviewer-proof experiment section, or converting a literature review into must-have, should-have, optional, and not-comparable baselines.