Skill UI

This skill generates structured, professional summaries for clinical case presentations, handovers, and educational documentation. It supports established medical formats like SBAR and SOAP notes, ensuring all necessary components—from subjective complaints to recommendations—are systematically included. Designed for healthcare professionals to improve documentation clarity and facilitate seamless patient transitions. Note: This output is strictly for educational and documentation purposes and must not replace professional clinical judgment.

This skill provides architectural guidance for writing high-level academic history articles, specifically tailored for the American Historical Review (AHR). It helps writers ensure that the narrative development and the core historical argument are seamlessly interwoven, adhering to strict length and scholarly standards. Use it when outlining complex drafts or restructuring material that feels disjointed.

This guide helps researchers assess the optimal venue for their work, determining if their project aligns best with AISTATS, or if it is better suited for major ML conferences (NeurIPS, ICML, ICLR), UAI, or specific statistics journals. It emphasizes the need for strong statistical foundations, inference guarantees, and the unique intersection of AI, ML, and statistics before starting the writing process.

This comprehensive guide provides expert protocols for writing the 'Related Work' section in Machine Learning papers. It teaches authors how to maintain 'versioned honesty' by meticulously triaging the literature into distinct lanes (e.g., method lineage, model artifacts, evaluation infrastructure). It covers advanced topics like managing concurrent work, citing arXiv status accurately, and avoiding self-citation violations, ensuring the paper is positioned rigorously within the rapidly evolving academic field.

This comprehensive audit checklist guides authors preparing a paper for the COLT Conference on Learning Theory. It details crucial requirements such as the 12-page PMLR format, single-PDF assembly, and double-anonymous formatting. It helps authors preemptively detect common submission flaws, including parallel-submission violations and formatting errors, ensuring the manuscript meets high academic standards.

This guide provides best practices for authors writing the "Related Work" section for interdisciplinary AI conferences. It teaches how to precisely position novelty across multiple domains—including ML fairness, law, HCI, and critical theory—by writing delta-first contrasts and properly attributing borrowed concepts to their original academic source.

This guide outlines the proper placement of various research materials—including proofs, code, and extended results—within major academic conference submissions. It helps authors understand which content must be presented in the main body, which belongs in appendices, and how to correctly manage supplementary manuscripts and code/data supplements to maximize the chance of reviewer acceptance.

A comprehensive guide for revising academic manuscripts for top-tier law and economics journals. It teaches authors how to restructure the introduction and abstract to follow the law-and-economics arc, ensuring the prose is plain, direct, and seamlessly integrates legal doctrines with rigorous economic analysis for a dual audience.

Provides a comprehensive, time-sensitive playbook for authors responding to reviews (rebuttals) at MLSys. It details how to triage objections, prioritize critical missing measurements, address systems objections regarding workload representativeness and baseline tuning, and structure anonymous, data-driven replies within a compressed rebuttal window.

This guide details the critical steps for authors preparing their accepted paper for final publication in MLSys proceedings. It covers de-anonymization, restoring company and repository identity, reconciling promised rebuttals, and ensuring metadata accuracy for long-term findability. Essential for managing the complex post-acceptance timeline.

This guide provides critical structuring advice for authors submitting ML Systems papers, focusing on the strategic division of content between the mandatory 10-page body and the unlimited supplementary appendix. It outlines which material belongs in the main text (claims that must be believed) versus what belongs in the appendix (material that must be verifiable evidence, such as full hyperparameter grids or deep ablations).

This guide provides detailed academic instructions on structuring the 'Related Work' section for mobile systems conferences like MobiSys. It teaches authors how to position their novelty by covering adjacent literature lanes (e.g., on-device ML, offload, mobile OS) and differentiating their work from existing, similar state-of-the-art systems, while maintaining rigorous academic integrity.