Skill UI

This skill provides a comprehensive guide and implementation for deploying a production-ready, containerized integration service using Docker. It connects to the Juicebox API to manage datasets, execute advanced AI-powered analyses, and retrieve structured business insights. The service includes health check mechanisms and supports zero-downtime rolling updates, ensuring reliable real-time data processing and analysis.

Guides senior data scientists through designing controlled experiments, engineering structured features, training ML models, and translating results into decisions using Python, R, SQL, and tools like Scikit-learn, XGBoost, and MLflow.

KAPE (Kroll Artifact Parser and Extractor) is a specialized, rapid triage tool for incident response. It collects specific, volatile forensic artifacts (like registry hives, event logs, and browser data) from compromised Windows systems. It is designed for remote, at-scale artifact collection when full disk imaging is impractical, enabling analysts to quickly build a defensible, parseable dataset for initial investigation and containment.

This skill integrates with DeepAPI to facilitate advanced web scraping, market research, and controlled email workflows. It handles scraping data from various sources like websites, LinkedIn, GitHub, and Twitter, while managing costs and ensuring explicit user approval for sensitive actions like sending emails. Use it for robust data extraction and automated reporting.

This comprehensive guide details the methodological requirements for building a robust robustness section in accounting and empirical research manuscripts. It covers four critical pillars: substituting accounting measures, controlling for institutional/governance variables, executing placebo tests, and advanced endogeneity follow-ups. It helps authors ensure their primary findings withstand stringent academic scrutiny.

This guide provides stringent methodological standards for establishing credible causal claims in empirical policy manuscripts. It covers advanced econometric techniques—including Difference-in-Differences (DiD), Instrumental Variables (IV), Regression Discontinuity Design (RDD), and Randomized Controlled Trials (RCT)—required to move beyond mere correlation and prove that the observed effect is genuinely caused by the policy intervention.

This skill guides the design of real-data validation for academic manuscripts, specifically for forecasting and financial engineering strands. It covers data provenance, rolling out-of-sample tests, benchmark batteries, and statistical significance of improvements. It ensures robustness against look-ahead bias and parameter sensitivity, helping authors meet rigorous external review standards for empirical studies in journals like Chinese Journal of Management Science.

A comprehensive project management guide for submitting database systems papers to EDBT. It details the complexities of the multiple-cycle rolling publication model, covering everything from initial evidence lock and cycle selection to the five-step review arc (Submission, Author Feedback, Revision, etc.). Use this to backward-plan your research timeline and manage the entire academic publishing process.

A comprehensive methodology for designing scientific and reproducible storage evaluations, particularly for high-stakes submissions. This guide covers controlling device state (aging, preconditioning, TRIM), selecting credible workloads (fio, YCSB), matching metrics (P99.9 latency, bytes-written) to claims, and rigorously testing durability and crash consistency on real hardware.

A comprehensive toolkit designed for international macroeconomics research, ensuring that headline estimates are stable and reliable. It systematically tests the robustness of findings against common methodological threats, such as leaving out individual countries, changing sample periods, controlling for global common shocks, or varying estimation specifications. Essential for submitting high-impact academic papers.

A guide for integrating behavioral theory into rigorous empirical studies. It outlines the necessary steps to move beyond descriptive findings of irrationality by constructing a statistically identifiable behavioral model (e.g., incorporating loss aversion parameters) and testing it against rational baselines using controlled, incentive-compatible experimental designs. Essential for submitting papers requiring model explanation of behavioral biases.

This guide details best practices for rigorous statistical reporting in clinical trials, following guidelines from top medical journals like NEJM. It emphasizes using confidence intervals (CIs) over bare P-values, defining primary analysis populations (ITT), controlling for multiplicity, and reporting absolute risks (NNT). It helps researchers ensure their findings are statistically robust, transparent, and defensible.