Skill UI

This skill implements User and Entity Behavior Analytics (UEBA) using Elasticsearch/OpenSearch. It goes beyond static rules to build behavioral baselines (e.g., login times, data volume) for users and entities. By calculating anomaly scores and performing peer group analysis, it detects suspicious activities such as data exfiltration, privilege abuse, and unauthorized access, allowing organizations to proactively identify high-risk insider threats.

This skill performs User and Entity Behavior Analytics (UEBA) to identify anomalous user activities such as impossible travel, unusual access patterns, and privilege abuse. It establishes behavioral baselines using SIEM logs and statistical analysis, helping SOC teams detect insider threats and compromised accounts by flagging deviations from normal user behavior. Use when monitoring security hygiene and suspicious activity is critical.

This comprehensive guide outlines the process for packaging artifacts from Human-Robot Interaction (HRI) research. Recognizing that HRI artifacts are complex, involving human subjects, robot behaviors, and extensive study materials, this skill provides guidelines for honest, ethical, and reproducible dissemination. It covers structuring de-identified data, analysis code, and behavioral specifications while adhering to participant consent and academic archiving standards.

This guide ensures that the chosen research methodology genres (e.g., behavioral empirical, analytical modeling, design science) are correctly aligned with the core research claim or phenomenon. It helps researchers identify the necessary design rigor, appropriate identification strategies (like DiD or IV), and the level of analysis required to ensure the study can genuinely support its contribution in the IS domain.

A comprehensive guide for designing rigorous experimental studies in behavioral science. It covers establishing causal links, testing psychological mechanisms (mediation and moderation), controlling confounding variables, conducting power analysis, and adhering to modern academic standards like pre-registration, ensuring research meets the high bar of top-tier journals.

This comprehensive guide assists users in analyzing and reporting data for high-impact academic journals, specifically following the rigorous standards of the Journal of Consumer Research (JCR). It covers advanced statistical process evidence (mediation, moderation, effect sizes) for behavioral experiments, as well as methods for establishing trustworthiness in interpretive (CCT) research. It emphasizes transparency, replication code, and adherence to modern research reporting mandates.

This guide provides a rigorous methodological checklist for conducting data analysis suitable for top-tier academic journals, such as JMIS. It covers advanced techniques across econometrics (addressing identification and endogeneity), behavioral modeling (SEM/PLS, common-method bias), and ML/analytics, ensuring that research findings meet the highest standards of robustness and theoretical grounding.

This guide assists researchers in aligning their study design—whether behavioral (lab/field experiments) or modeling-econometric (IV/DiD/RDD)—with the rigorous standards required by the Journal of Marketing Research (JMR). It covers best practices for causal identification, power analysis, structural estimation, and meeting journal-level expectations like replication and Web Appendix planning.

This guide provides comprehensive methodology for advanced empirical analysis in Public Economics, particularly for manuscripts using large administrative or register microdata. It covers advanced topics like estimating behavioral elasticities, calculating welfare metrics (MVPF, DWL), and ensuring robust causal identification strategies. It emphasizes translating raw coefficients into policy-relevant quantities with rigorous uncertainty quantification.

This guide provides rigorous standards for transforming complex behavioral and risk data into clear, statistically sound, and academically rigorous tables and figures. It ensures that key decision-theoretic content, such as choice patterns, parameter estimates, and uncertainty, are visibly and interpretably presented for academic publication.

A comprehensive guide for executing, evaluating, and reporting the empirical core of MIS Quarterly manuscripts. It covers specialized methods for Behavioral IS (PLS-SEM), Economics of IS (causal identification), Design Science (artifact evaluation), and qualitative studies, ensuring full transparency and replicability for top-tier academic submissions.

A comprehensive guide for conducting and reporting rigorous experimental research in social and behavioral sciences. It covers various design types (vignette, lab, field), statistical requirements (effect sizes, power analysis), quality control measures (manipulation checks, confound control), and advanced techniques for establishing causality, ensuring high internal validity for academic publication.