Skill UI

A comprehensive guide for structuring a multi-study manuscript, particularly for high-tier journals. This skill focuses on building a coherent set of related studies—rather than a single experiment—to robustly test a major theory. It guides users through sequencing studies, determining necessary power, selecting appropriate designs (longitudinal, dyadic, experimental), and implementing preregistration to ensure convergence and internal validity.

This skill provides comprehensive guidance for authors targeting *Modern Economic Research*, a top-tier journal focused on comprehensive economic theory. It outlines the journal's academic scope, including industrial policy, digital economy, and regional reform. The guide emphasizes the need for manuscripts to possess strong institutional logic, verifiable evidence, and theoretical contribution, ensuring the work transcends mere policy reports.

A comprehensive guide to the rigorous academic peer review process, exemplified by top-tier Recommender Systems conferences (RecSys). It details the double-blind review mechanics, key evaluation criteria (novelty, validity, reproducibility), the critical rebuttal phase, and the requirements for final publication in ACM Digital Library. Essential for researchers and students aiming to improve academic submission quality.

A comprehensive guide for researchers on assessing the suitability of digital health, AI/ML, or health informatics manuscripts for The Lancet Digital Health. It outlines the journal's strict standards for clinical rigor, external validation, reporting guidelines (TRIPOD, CONSORT-AI), and evidence required for publication.

This specialized skill is designed for Digital Forensics and Incident Response (DFIR) scenarios. It correlates evidence from multiple disparate sources—including EVTX, PCAP, registry, memory, and filesystem artifacts—to build a comprehensive, ordered, and replayable forensic timeline. Use it when the goal is to map the full sequence of attacker actions, establishing initial access, lateral movement, and persistence chains based on cross-source evidence correlation.

Implements GDPR-compliant pseudonymization for sensitive personal or clinical data from EU residents. Unlike full anonymization, this method creates a reversible mapping key, which must be stored separately and securely. This process allows for controlled re-linkage (e.g., for Subject Access Requests or longitudinal care) while maintaining GDPR Article 4(5) compliance by ensuring the linkage key is kept separate from the working data.

This skill performs deep 'git archaeology' by cross-referencing commit history, changelogs, and initial summaries. It extracts verified metrics like commit velocity, contributor rankings, and release timelines, generating multiple detailed reports. Use this when building a REX talk and needing factual, data-backed evidence from a source git repository.

This skill guides users through a comprehensive Legitimate Interest Assessment (LIA) required under GDPR Article 6(1)(f). Utilizing the official EDPB three-step test, it helps users systematically determine if processing personal data based on legitimate interest is legal, proportionate, and necessary. It is crucial for documenting compliance records, especially concerning AI training, web scraping, and direct marketing.

A specialized skill for conducting authorized Open Source Intelligence (OSINT) and cyber threat intelligence gathering. It enriches Indicators of Compromise (IOCs), tracks malicious campaigns, and analyzes impersonation or scamming narratives from public platforms like X/Twitter. The process mandates rigorous evidence corroboration, source preservation, and structured handoff to digital forensics or threat-hunting pipelines.

DataLad manages scientific datasets using Git and git-annex, handling large files efficiently by storing content remotely. It captures computational provenance for reproducible analysis, allowing users to clone, fetch, and publish datasets from repositories like OpenNeuro. Ideal for managing large data files and ensuring analysis reproducibility.