Skill UI

A comprehensive skill designed to assess the suitability of a manuscript for The BMJ (British Medical Journal). It provides expert guidance on journal positioning, required methodological rigor (e.g., CONSORT, PRISMA, STROBE compliance), open data commitments, structured abstract requirements, and the specific standards necessary for submission to a leading, patient-centered, evidence-based medical journal. Use this to re-frame and optimize your research paper before submission.

A comprehensive guide for structuring and positioning computer science manuscripts for The Web Conference (WWW). It advises authors on leveraging open-web data, focusing on web systems, web mining, and responsible AI, while adhering to the latest venue-specific standards and evidence requirements.

This guide assists researchers in assessing the suitability of their manuscripts for the World Economic Papers. It covers the journal's focus on international economics, open macro, and global value chains. It provides detailed checks on formatting requirements (abstract, references), assesses methodological suitability (e.g., GVC decomposition), and identifies potential submission pitfalls, ensuring academic rigor before targeting a high-level academic journal.

This skill provides a comprehensive framework for targeting and revising academic manuscripts for the journal World Economic Studies. It analyzes the journal's scope, focusing on globalization, open strategy, international trade, and regional economics. It guides users on required advanced methodologies (e.g., GVC decomposition, panel data), structural mandates, and common submission pitfalls to ensure manuscripts meet high academic standards.

This guide helps scholars rigorously defend the methodological design of academic papers, particularly in International Relations and Comparative Politics. It covers advanced techniques such as process tracing, case selection logic, quantitative cross-national panel data analysis (DID, IV, RDD), and experimental design. Its core purpose is to strengthen causal claims by explicitly addressing selection bias, defining scope conditions, and ruling out major rival explanations.

This guide outlines the mandatory requirements for authors publishing quantitative or mixed-method research in academic journals. It details the process of creating a complete replication package—including raw data, master scripts, seeds, and extensive README documentation—and depositing it in a trusted data repository (like Dataverse). It ensures scholarly rigor by guaranteeing that published findings are fully reproducible and traceable.

This skill calculates and documents the residual re-identification risk of de-identified datasets. It goes beyond simple direct identifier removal by assessing the risk posed by quasi-identifiers (age, ZIP, dates). It computes structural metrics like k-anonymity and l-diversity, and runs empirical linkage attacks to provide a defensible memo supporting HIPAA Expert Determination for determining if a dataset is safe for release.

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.

A rigorous framework for making evidence-based estimates and forecasts when precise data is unavailable. It guides users to anchor predictions on base rates, articulate uncertainty using confidence intervals, and systematically update beliefs using likelihood ratios (prior to posterior) as new evidence arrives. Ideal for risk sizing, project timelines, and strategic decision-making.

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.

This skill performs pharmacokinetic and pharmacodynamic modelling, including non-compartmental analysis, compartmental fitting, and population PK. It helps derive exposure metrics, evaluate dosing regimens, and predict drug interactions using Python scripts. Suitable for analyzing concentration-time data and supporting clinical pharmacology workflows.

ADHX provides a dedicated API to fetch X/Twitter posts as clean, structured JSON. It extracts comprehensive data, including author profiles, engagement metrics (likes, retweets), and full article content for long-form posts. This method is vastly superior to traditional scraping techniques, making it ideal for LLM analysis, summarization, and structured data workflows.