Skill UI

A specialized guide for researchers assessing manuscript suitability for securities and capital market journals. It analyzes the journal's positioning, preferred topics (e.g., market regulation, corporate governance, asset pricing), required methodologies (e.g., DID, event studies), and common submission pitfalls. Use this skill to structure your paper and improve its fit before formal submission.

This specialized skill guides authors through the rigorous academic requirements for submitting manuscripts to the Shanghai Journal of Economics. It helps users assess whether their work aligns with the journal's focus areas—such as urban governance, industrial transformation, and the Yangtze River Delta economy—and provides heuristics for structuring, framing, and enhancing the manuscript's academic contribution.

This skill assesses whether a manuscript is suitable for World Development, a leading multidisciplinary development-studies journal. It guides authors on re-framing their work to focus on real-world development problems, policy relevance, and global issues (e.g., poverty, climate, governance). It clarifies the required balance between rigorous methodology and multidisciplinary, actionable insights for a broad academic readership.

This guide provides a comprehensive framework for researchers to assess if their development-related work is suitable for World Development (WD). It moves beyond mere methodological rigor, focusing instead on the paper's multidisciplinary relevance, real-world policy stakes, and ability to advance global understanding of development problems (e.g., poverty, governance, inequality). It helps authors ensure their findings appeal to a broad academic and policy audience.

Designed for regulated environments (GxP/GCP), this skill generates and verifies audit trails compliant with 21 CFR Part 11 standards. It ensures every data processing step leaves an attributable, time-stamped, and tamper-evident record, providing electronic signatures and robust data integrity checks (ALCOA+). It is essential for clinical trials and regulated data governance.

This skill audits OpenMed NER or de-identification models to detect performance disparities across sensitive demographic subgroups (e.g., sex, age, race). It calculates metrics like per-group recall and leakage disparity, helping users identify instances of 'under-protection' before deployment, ensuring equitable AI governance and compliance.

This skill automates the creation of comprehensive Model Cards for OpenMed clinical NER or de-identification models. It gathers data from various OpenMed evaluation reports (e.g., `GateReport`, `fairness_report`, `error_report`) to structure critical governance information, including intended use, quantitative performance metrics, subgroup analysis, limitations, and required medical-device disclaimers. It ensures documentation is reproducible and compliant for clinical AI deployment.