Skill UI

This guide functions as a sophisticated workflow router for authors targeting submission to Molecular Cell. It diagnoses the current stage of a manuscript (e.g., missing data, weak mechanism, need for rebuttal) and recommends the precise next specialized skill needed to address bottlenecks. It ensures the manuscript meets the high bar of mechanistic depth and orthogonal validation required by top-tier molecular biology journals.

A comprehensive guide for authors submitting biomedical engineering research. It assesses the manuscript's fit, biomedical significance, and translational relevance against Nature's rigorous standards. Covers scope, validation requirements (benchtop vs. clinical), structural guidelines, and official submission checklists for devices, biomaterials, and engineered systems.

A comprehensive workflow router designed to guide researchers through the entire process of preparing manuscripts for top-tier business journals, such as the Nankai Business Review. This guide does not replace specialized skills but provides a structured, stage-gated approach covering theory gap identification, hypothesis development, rigorous measurement validation (e.g., SEM, validity), contextualization, and strategic revision (R&R). It ensures that the paper meets the necessary standards for theoretical contribution and empirical depth.

A comprehensive guide for authors targeting top-tier security conferences like NDSS. This skill helps determine manuscript fit, refine the academic framing, strengthen evidence, and understand venue-specific expectations (e.g., realistic attacker models, empirical validation). It serves as a strategic tool for conference submission planning, comparing submissions against related venues like USENIX Security and CCS.

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.