Skill UI

A comprehensive workflow for systematically reviewing medical evidence. It allows users to search databases for clinical studies, build curated citation collections, grade evidence using established frameworks like GRADE, and generate professionally formatted, structured reports in academic citation styles (e.g., AMA). Ideal for conducting literature reviews and supporting clinical research. This tool helps transform raw medical data into actionable, structured knowledge.

This comprehensive guide defines the secure data lifecycle management for OpenEvidence, an AI-powered platform for clinical evidence synthesis. It outlines data classification (e.g., clinical queries, patient responses), strict adherence to global regulations (HIPAA, GDPR, FDA), and technical requirements for handling Protected Health Information (PHI). Key processes include PHI de-identification, data validation, secure API integration, and compliant data export, ensuring the system maintains the highest level of patient privacy and regulatory compliance.

This guide demonstrates how to implement robust rate limiting and retry logic for critical healthcare APIs using a token bucket pattern. It covers managing specific service limits (e.g., clinical queries, evidence synthesis) and implementing strategies like exponential backoff and batch processing to ensure graceful degradation and reliable data retrieval in clinical decision support systems.

BGPT Paper Search queries the remote MCP server to retrieve structured experimental data from full-text studies, returning methods, results, sample sizes, quality scores, and conclusions for efficient literature reviews and evidence synthesis.

Guides structured hypothesis generation by combining literature searches, evidence synthesis, competing explanations, and experimental design, with mandatory AI-generated scientific schematics for complete reports.

A powerful tool designed for systematic literature reviews and meta-analyses. It searches scientific papers and retrieves highly structured experimental data directly from the full text, going beyond mere abstracts. It extracts over 25 fields, including detailed methods, quantitative results, sample sizes, quality scores, and conclusions, enabling comprehensive evidence synthesis for academic research.

A comprehensive mentor guiding users through the entire lifecycle of academic literature reviews. It teaches systematic search strategies (PICO/PCC), critical appraisal methods, and advanced synthesis techniques. The skill covers searching major English and Chinese databases (e.g., PubMed, Web of Science, CNKI), organizing findings, and effectively utilizing AI research tools like Elicit and Semantic Scholar to build robust, evidence-based research papers.

This meta-skill orchestrates a comprehensive workflow for analyzing PDF documents. It handles complex tasks such as summarizing, cross-document comparison, and targeted Q&A. It ensures findings are traceable by indexing evidence from various sources (full PDFs, pasted excerpts, etc.) and synthesizing structured insights across multiple files.

This skill synthesizes raw user interview transcripts into structured research findings. It identifies core themes, pain points, workflow insights, and actionable product implications. Use this when analyzing qualitative research notes to derive evidence-based product recommendations and next steps for development teams.

This skill analyzes raw user research data—including transcripts, survey results, and feedback—and synthesizes it into a structured, actionable report. It identifies key themes, quantifies prevalence, pinpoints critical pain points, prioritizes feature requests, and provides strategic, evidence-backed recommendations for product improvement and development.

This tool synthesizes multiple, scattered research findings retrieved from memory into a single, structured, and actionable report. It automatically grades evidence quality (High/Medium/Low), resolves conflicting claims, and provides concrete, recommendation-based insights, making complex data digestible for decision-making.

A comprehensive guide for conducting rigorous academic literature reviews. It teaches authors how to critically appraise and synthesize conflicting empirical evidence by weighing findings based on credibility, study design, and contextual differences. The framework ensures balance, maintains objectivity, and elevates the quality of academic scholarship.