Skill UI

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.

A comprehensive guide for structuring and defending academic research designs, particularly for New Media and Society studies. It provides rigorous criteria for qualitative ethnography, content/discourse analysis, computational data validation, and mixed methods integration. The focus is on strengthening the theoretical argument by justifying choices, validating measures, and defeating leading alternative readings.

officecli is a robust, dependency-free CLI tool designed for working with Microsoft Office formats (.docx, .xlsx, .pptx). It allows users to programmatically create, analyze, modify, and extract structured data from complex documents, enabling advanced content manipulation, schema validation, and automated workflows entirely from the command line.

Resolve free-text scientific labels to ontology IDs and validate existing CURIEs using EBI OLS4, Bioregistry, and Identifiers.org. Supports annotating biological fields like tissue, disease, and phenotype. Ensures metadata accuracy for submissions to GEO, ENA, and BioSamples by checking obsolescence and prefix correctness.

Analyze PIV image pairs with OpenPIV: preprocessing, cross-correlation, vector validation, outlier replacement, and computing vorticity, strain rate, and turbulence statistics for fluid flow experiments.

A comprehensive guide for designing and auditing rigorous empirical evaluations for sensing systems, particularly Human Activity Recognition (HAR). It emphasizes best practices such as leave-one-subject-out cross-validation, using real human subjects under diverse conditions, reporting event-level metrics (e.g., Macro-F1) over raw accuracy, establishing fair baselines, and mitigating data leakage (e.g., subject or session leakage). This ensures that reported results are robust, generalizable, and defensible against academic skepticism.

A comprehensive guide for structuring the analysis and results section of academic papers in Production and Operations Management (POM). It covers rigorous theoretical proofs, advanced empirical identification methods (e.g., DiD, IV), and data science validation techniques. The core focus is bridging statistical significance with actionable, operational managerial magnitude, ensuring research findings are decision-relevant.

This guide provides a comprehensive checklist for designing, executing, and presenting empirical evidence for academic papers in Pattern Recognition and AI. It covers critical areas like establishing fair baselines, ensuring statistical rigor (mean ± std dev, significance testing), conducting sufficient ablation studies, preventing data leakage, and reporting computational costs. It helps authors make their findings withstand rigorous external review.

A comprehensive guide to ensuring the quantitative rigor and full reproducibility of econometrics manuscripts, particularly for submissions to high-tier journals like QE. Covers structural estimation (GMM, MSM), moment construction, data cleaning best practices, and comprehensive validation checks (e.g., multi-start, Monte Carlo recovery). It ensures that all results can be regenerated from raw data using pinned environments and documented scripts.

A comprehensive guide detailing the stringent methodological and reporting standards required for submitting medical imaging research (diagnostic, AI, interventional) to a top-tier radiology journal. It covers expectations for diagnostic accuracy (STARD), AI model validation (CLAIM), sample size justification, reproducibility, and ensuring clinical relevance, helping authors frame their work for optimal journal fit.

A comprehensive guide for executing and stress-testing empirical analyses in Industrial Organization (IO). It covers structural and reduced-form model diagnostics, advanced inference techniques (e.g., wild-cluster bootstrap), counterfactual validation, and meeting the rigorous robustness standards demanded by top-tier academic journals. Focuses on methodology, not study design.

A comprehensive guide for authors to determine if their manuscript meets the stringent standards of Science Translational Medicine. This skill assesses the necessary dual mandate: demonstrating both a solid mechanistic insight and a credible, measurable clinical translation arc (drug, device, or biomarker). It covers scope fit, required human evidence standards, necessary preclinical validation, and specific structural requirements for submission.