Skill UI

A comprehensive guide detailing the standards required for academic reproducibility in NLP and LLM research. It covers the 'Responsible NLP checklist' requirements, including detailed reporting for hyperparameters, model versions, exact prompts, decoding parameters, data contamination auditing, and variance reporting to ensure claims are auditable and robust.

A comprehensive guide detailing how to structure and populate the supplementary material section of technical research papers, especially in NLP and Machine Learning. It distinguishes between load-bearing elements (main text) and audit trails (appendix), covering prompt verbatim, decoding configurations, full result grids, and code anonymization protocols to ensure high reproducibility and academic rigor.

A comprehensive guide detailing the ICASSP peer review pipeline, covering single-blind review mechanics, technical committee evaluation criteria, and the strategic importance of evidence, significance, and reproducibility in signal processing papers. Use this to craft submission strategies and decode decision letters.

A detailed guide for authors handling major revisions (R&R) for quantitative economics papers. It structures the entire process, from decoding the coeditor's priorities and classifying referee demands (fundamental, clarification, disagreement), to executing necessary new analyses and drafting a professional, evidence-based response letter that maintains reproducibility and rigor.

This skill facilitates the ingestion of unstructured clinical notes and reports from FHIR R4 EHR APIs (e.g., Epic, Cerner). It handles complex tasks including paginating large search results, decoding base64 attachments (PDF, text), and extracting narrative content. The extracted text is then passed to the OpenMed pipeline for HIPAA-compliant de-identification and subsequent NLP analysis, enabling structured data insights from raw clinical records.