Skill UI

This skill guides authors on assessing if their cardiovascular manuscript meets the rigorous standards of Circulation, the AHA's flagship journal. It covers the journal's specific scope, ideal research methods (e.g., RCTs, biomarker studies, epidemiology), structural requirements, and how to effectively frame the work to impact AHA guidelines and clinical practice.

This comprehensive skill guides authors on assessing the suitability of cardiovascular manuscripts for the European Heart Journal (EHJ). It provides detailed criteria covering journal positioning, required clinical relevance, advanced methodology standards (e.g., RCT and registry design), and structural adherence to ESC guidelines. Use it to reframe your research and ensure your work meets the high academic bar of EHJ.

A comprehensive guide to preparing accountability artifacts for AI research papers, particularly for venues like FAccT. It covers the necessary documentation genres—including datasheets for datasets, model cards for models, and impact assessments—to ensure transparency, verifiability, and accountability regarding claims of fairness and harm mitigation in AI systems. The focus is on making research outputs rigorously traceable and ethically responsible.

This comprehensive guide outlines the critical steps required for authors preparing an accepted paper for ACM FAccT proceedings. It details the transition from anonymous drafts to the final, published 'camera-ready' format, covering mandatory de-anonymization (restoring author details and adding Positionality statements), switching to the specific ACM metadata standards (ORCID, CCS concepts), completing required documentation (datasheets/model cards), and adhering to complex publication timelines and rights requirements.

A comprehensive guide for establishing academic rigor in AI and data science research. It outlines best practices for achieving reproducibility, emphasizing the mandatory release and documentation of raw data, trained models, and qualitative findings. Key tools include datasheets, model cards, and detailed provenance logging, ensuring that research claims are fully auditable and consistent.

This skill provides a comprehensive guide for authors targeting JAMA Cardiology. It helps authors evaluate the suitability of cardiovascular trials, outcomes, and registry studies against the journal's high standards. It covers required endpoints, reporting guidelines (e.g., CONSORT, STROBE), house style, and submission prerequisites, aiding in optimizing manuscript fit before submission.

This skill provides comprehensive guidance for authors targeting the Journal of the American College of Cardiology (JACC). It covers journal positioning, scope fit, rigorous methodology standards (e.g., CONSORT, TRIPOD), house style requirements, and official submission checklists for high-impact cardiovascular research. Use this tool when aiming for publication in JACC or determining if your manuscript fits this leading venue.

This guide details the rigorous process of selecting and structuring academic content for venues with extremely strict page limits, such as major computer science conferences. It teaches authors how to 'triage' material, classifying information into critical, supportive, or discardable categories, ensuring the core argument remains visible and complete even when traditional supplementary files are prohibited.

This skill automates the creation of professional, multi-element Excel dashboards from raw CSV or tabular data. It structures the output with a dedicated 'Dashboard sheet' that serves as the primary landing view. Features include formula-driven KPI cards, linked charts, sparklines, and semantic conditional formatting. It is ideal for generating executive, analytics, or performance dashboards, ensuring that the user sees immediate, actionable insights without needing to navigate raw data sheets.

A critical pre-submission tool that provides a comprehensive go/no-go scorecard for academic manuscripts. It evaluates nine dimensions—including fit, robustness, and venue mechanics—to catch avoidable desk-reject risks before the author submits the paper. It scores the manuscript against the target venue's specific requirements and provides actionable feedback on gaps and necessary revisions.

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.

This skill provides an in-depth, entity-level scorecard for clinical or biomedical Named Entity Recognition (NER) models. It calculates precision, recall, and F1 scores (both strict and partial match modes) using a user-supplied gold corpus. Key features include per-label error breakdown, a confusion matrix, and detailed False Negative/False Positive analysis, essential for debugging model performance in highly sensitive medical domains.