Skill UI

A comprehensive guide for creating the two required artifacts for a WACV paper: an anonymous package for double-blind review, and a public release post-acceptance. It emphasizes reproducing deployed system claims (latency, power, robustness) and maintaining consistency across revision rounds.

Provides comprehensive guidelines for packaging and submitting research artifacts (datasets, models, code) for major conferences. It details the difference between mere 'existence' (badge verification) and true academic 'reproducibility,' covering archival strategies, web data licensing, takedown resilience, and minimal viable package structure for long-term scholarly impact.

This guide provides comprehensive advice for positioning applied computer vision research for the IEEE/CVF WACV conference. It helps authors assess the manuscript's fit, re-frame the narrative from journal or arXiv styles, and ensure the contribution highlights practical systems, strong empirical evaluation, and application grounding required by top CV venues.

This comprehensive guide outlines best practices for packaging research artifacts, including code, proprietary data logs, and models, for submission to top-tier academic venues. It addresses the crucial need for verifiability and reproducibility, providing strategies for handling limited space, proprietary datasets, and the absence of formal artifact grading systems, ensuring high academic credibility.

This guide provides a comprehensive framework for designing rigorous and defensible evaluation sections for machine learning and information retrieval papers, particularly for top-tier conferences like WSDM. It covers the four critical evidence quadrants—Effectiveness, Validity, Efficiency, and Robustness—and details necessary protocols, such as temporal splits, proper baseline selection (including heuristics), ablation studies, and hardware-aware performance reporting. Use this when auditing or writing the experimental section of a research paper.

This guide details the rigorous, unique evaluation process for academic articles and essays at Yale Law Journal (YLJ). Unlike traditional peer review, student editors conduct anonymous evaluations, focusing heavily on the originality of the claim, the completeness of the apparatus, and crucially, a thorough 'source-pull' where every citation must be rigorously verified and supported by the text. Use this to understand how to structure and prepare manuscripts for scholarly publication.

This skill provides a rigorous fit test for legal scholarship ideas targeting The Yale Law Journal (YLJ). It helps users determine if their claim has generalist significance beyond a single niche, whether the argument is substantial enough (a claim, not a survey), and what the appropriate format (Article, Essay, Forum) should be. It is crucial for pre-drafting evaluation.

This guide details the rigorous review process for Production and Operations Management (POM), covering departmental screening, double-blind peer review, and the strict evaluation standards. It explains the 'twin standard' (scholarly rigor and managerial relevance), how to interpret decision letters (R&R vs. Reject), and the critical rules governing resubmission, helping authors navigate the publishing journey.

A comprehensive guide for authors submitting to SIGMETRICS, detailing how to structure the Related Work section. It emphasizes shifting from a simple citation catalog to a 'delta-first' contrast, highlighting the specific technical novelty (e.g., tighter bounds, weaker assumptions) relative to existing literature across multiple performance evaluation lanes (systems, measurement, learning, etc.).

Polars is a lightning-fast DataFrame library designed for efficient, memory-efficient data processing in Python and Rust. Built upon Apache Arrow, it supports lazy evaluation and parallel execution, making it ideal for ETL pipelines, large-scale data transformations, and scenarios where standard Pandas performance is insufficient. It excels at handling datasets up to 100GB that fit within available RAM.

This skill provides a comprehensive, reproducible rubric for evaluating scholarly and scientific work. It covers nine critical dimensions—including problem statement, methodology, data evidence, analysis rigor, and citation quality—making it ideal for reviewing research papers, grant proposals, literature reviews, and academic theses. Use it to provide structured, actionable, and professional feedback to enhance scholarly output.

Scikit-learn is the industry-standard Python library for classical machine learning. It provides comprehensive tools for supervised and unsupervised learning tasks, including classification, regression, clustering, and dimensionality reduction. Use it to build robust, production-ready ML pipelines, perform rigorous model evaluation, and preprocess complex datasets for various data science applications.