Skill UI

This guide provides late-stage polishing for academic manuscripts, specifically targeting the Academy of Management Review (AMR) house style. It helps transform drafts that read like literature reviews or essays into tightly structured, argument-driven theoretical pieces. Key focus areas include establishing a clear theoretical puzzle in the introduction, maintaining continuous argumentative flow, ensuring precise definition of constructs, and articulating propositions without using hypothesis language. It is crucial for authors who need to refine their prose and structure before final subm...

A specialized tool for polishing pure mathematics manuscripts, designed to enhance the rigor and overall flow of academic writing. It guides authors to eliminate soft language (e.g., "clearly," "obviously"), ensure precise quantification, explicitly state constant dependencies, and structure hypotheses meticulously. Ideal for late-stage proof refinement before journal submission.

A comprehensive guide for authors submitting papers to the ACL conference. It assists in positioning the manuscript, verifying topic fit (NLP, language models, semantics), and ensuring the evidence meets the high standards required for top-tier computational linguistics venues. Covers structure, best practices, and submission checklists.

A comprehensive guide for authors targeting ASPLOS, a premier conference at the intersection of programming languages, operating systems, and computer architecture. This tool assists in positioning cross-layer evidence, framing the contribution, and ensuring the manuscript meets the strict technical and academic standards required for top-tier systems research.

A comprehensive guide detailing best practices for designing and auditing empirical evaluations of Large Language Models (LLMs). It addresses critical pitfalls such as data contamination, baseline unfairness, model version drift, and decoding parameter sensitivity, ensuring that published results are robust, reproducible, and scientifically sound for academic publication.

A comprehensive guide detailing best practices for ensuring the reproducibility of computational claims in Large Language Model (LLM) papers. It outlines a layered approach (Exact, Budgeted, Drifting) for verifying results, providing protocols for handling open-weight checkpoints, proprietary API models, and documenting compute costs and failure modes honestly.

This skill helps authors strategically decide the optimal venue for their Language Model research. It provides the critical "object-of-study test" to determine if the LM itself is the subject of the study, or merely a tool for a task. Furthermore, it guides routing against top-tier NLP/AI conferences (ACL, NeurIPS, ICLR, etc.), helping authors target the right audience and maximizing submission chances.

A comprehensive guide for positioning Natural Language Processing (NLP) research papers for the Conference on Computational Natural Language Learning (CoNLL). It helps authors optimize their manuscripts by focusing on conference fit, structuring evidence, identifying necessary baselines, and adapting the narrative for a selective CS conference audience. Essential for submission strategy and manuscript re-framing.

This skill guides authors on how to position, structure, and strengthen empirical Natural Language Processing (NLP) manuscripts for submission to the Conference on Empirical Methods in Natural Language Processing (EMNLP). It provides deep insights into conference fit, required evidence (baselines, error analysis, datasets), proper framing, and anticipating reviewer expectations for top-tier AI venues.

This guide assists researchers in transforming academic findings into highly actionable and evidence-based policy recommendations. It specifically addresses the standards required for top-tier journals in public administration, emphasizing the mandatory link between observed 'Discovery' and proposed 'Suggestion' via an underlying 'Mechanism.' It teaches how to avoid generic filler language and structure policy suggestions around specific actors, stages, and concrete actions.

A comprehensive guide for drafting academic abstracts, specifically tailored for journals focusing on rural and agricultural economics. It enforces a strict five-sentence structure, requiring the inclusion of quantified findings, naming specific household survey datasets (e.g., CFPS, CHIP), and detailing rigorous identification strategies. This skill helps users eliminate boilerplate language and ensure semantic alignment between Chinese and English summaries.

A comprehensive style guide designed for academic manuscripts, particularly those targeting journals like China Rural Economy. This skill helps authors eliminate vague academic jargon, replace generic policy language (e.g., 'strengthen, improve, promote'), and ground abstract concepts in observable household mechanisms. It ensures precise narrative structure, enhancing the rigor and impact of the research findings.