Skill UI

This guide provides a rigorous framework for addressing reviewer comments on scientific manuscripts, especially for high-impact journals. It teaches how to triage feedback into actionable buckets (Do, Do-partial, Defend, Defer), handle specialized statistical reviewer critiques with re-analyses, and draft a professional, point-by-point response letter that enhances the paper's credibility and maximizes acceptance chances.

This guide assists researchers in transforming bloated or loosely structured academic manuscripts into the terse, plain IMRAD format required by top-tier journals like NEJM. It focuses on maximizing clarity, enforcing claim-first results based on statistical evidence, curbing over-stated clinical implications, and ensuring the discussion explicitly covers limitations and necessary scope restriction.

This skill provides a comprehensive framework for authors targeting top-tier AI conferences like NeurIPS. It guides researchers in refining their manuscript's narrative, ensuring the contribution meets the venue's standards for broad, high-novelty machine learning research. It covers topic fit, evidence bar requirements, structuring the argument, handling interdisciplinary claims, and navigating the specific submission cycle rules for maximizing acceptance chances.

This skill guides researchers through the rigorous validation process of their AI/ML paper for NeurIPS. It helps determine if the core contribution is robust enough for the main track, suggests the most appropriate NeurIPS track (e.g., E&D, Position), and refines the contribution type (Theory, Use-Inspired, etc.) to maximize the chances of acceptance.

This comprehensive guide details the strategy and structure required for authors responding to major or minor revisions (R&R) of academic manuscripts. It teaches how to systematically address multiple, often conflicting, interdisciplinary reviewer comments, focusing on point-by-point responses, conflict reconciliation, and enhancing the overall manuscript argument to maximize acceptance chances.

This guide provides a framework for designing and auditing the experimental rigor of academic papers, particularly in systems and programming languages. It emphasizes the crucial link between a claim type (e.g., 'Faster') and the required evidence type (e.g., benchmarks, user studies, formal proofs). Learn how to establish robust baselines, define clear workload rules, size experiments for conference cycles, and ensure statistical compliance for maximum impact.

A comprehensive guide detailing the multi-stage, double-anonymous review process used at major academic conferences like OOPSLA. It explains the four possible outcomes (Accept, Minor/Major Revision, Reject), the mechanics of 'round-hopping' between submission volumes, and provides strategic advice on how to write author responses and manage revision expectations to maximize publication chances.

A comprehensive guide detailing the strategic and technical requirements for submitting research papers to major conferences like OOPSLA. It covers critical steps such as selecting the correct submission round, adhering to strict formatting rules (e.g., page limits), proactively maintaining double-anonymity, properly filing Data-Availability Statements, and managing complex revision or concurrent submissions to maximize acceptance chances.

A comprehensive guide for authors aiming to submit top-tier technical papers (like those for OOPSLA). It teaches how to transform descriptive drafts into highly verifiable, structured academic arguments. Focus areas include developing running examples, articulating falsifiable claims, optimizing article pacing, and anticipating reviewer challenges to maximize the chances of acceptance.

This comprehensive preflight checklist is designed for authors preparing manuscripts for top academic journals. It guides users through crucial submission requirements, including double-blind anonymization, adherence to strict length norms (under 50 pages), structuring the contribution case, and ensuring compliance with modern transparency policies regarding data, code, and AI usage. Use it to maximize submission quality and minimize desk rejection risk.

A comprehensive guide for constructing rigorous and structured exhibits (tables, figures, theorems) for Operations Research (OR) manuscripts, adhering to INFORMS house style. It details requirements for formal statement layout, comparison tables against prior work, computational results, and performance plots, ensuring maximum academic rigor and reproducibility.

A comprehensive guide for researchers preparing system artifacts for major conferences like OSDI. It outlines best practices for structuring deliverables, ensuring permanent and resolvable archiving (e.g., using Zenodo/DOI), and maximizing reproducibility. The guide emphasizes meeting the current academic standard for verifiable and functional system submissions.