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 skill provides a complete guide to deploying a containerized OpenEvidence clinical evidence integration service using Docker. It establishes a HIPAA-conscious production environment, enabling secure connections to the OpenEvidence API. Users can query clinical evidence, retrieve summarized medical literature, and validate treatment recommendations, all while maintaining audit logging and data-at-rest encryption compliance. Includes setup for health checks and rolling updates.

This system implements Role-Based Access Control (RBAC) for OpenEvidence, an AI-powered clinical decision support platform. It strictly manages access to sensitive data, including PHI-adjacent datasets and medical literature, ensuring compliance with regulations like HIPAA. The system defines distinct roles—Admin, Clinician, Researcher, Student, and Auditor—with granular permissions for querying, data export, and institutional configuration, maintaining a secure and auditable data environment.

This SDK provides comprehensive, production-ready patterns for interacting with the OpenEvidence clinical evidence API. It enables developers to efficiently query medical literature, retrieve clinical guidelines, and generate evidence-based recommendations. Key features include a singleton client for consistent authentication, robust error wrappers (handling retries, invalid keys), and structured data models designed to ensure audit compliance and detailed citation provenance in complex healthcare applications.

Integrate with the DNAnexus cloud genomics platform to build apps/applets, manage FASTQ/BAM/VCF data, run workflows, and script automations via dxpy for biomedical pipelines.

Conduct rigorous, multi-database literature reviews with systematic workflows, verified citations in styles like APA or Nature, and AI-enhanced schematics for publication-ready markdown and PDF outputs in biomedical and technical domains.

Pydicom is a Python toolkit for reading, writing, and manipulating DICOM medical imaging files, helping teams extract pixel data, manage metadata, anonymize records, convert to standard formats, and integrate with PACS workflows in healthcare research and radiology applications.

PyHealth delivers a full-stack Python toolkit for building, evaluating, and deploying clinical machine learning models, covering EHR datasets, medical coding systems, physiological signals, and healthcare-tailored architectures such as RETAIN, SafeDrug, and Transformer-based pipelines.

TorchDrug is a PyTorch-native toolbox for building graph neural networks tailored to molecules, proteins, and biomedical knowledge graphs, covering property prediction, generation, retrosynthesis, and knowledge graph reasoning with curated datasets and models.

An AI-driven comprehensive health analysis system that integrates multi-dimensional datasets, including biometrics, lifestyle, and medical history. It excels at anomaly detection, predicting various health risks (e.g., CVD, Diabetes), providing personalized, evidence-based recommendations, and generating detailed, interactive health reports.

This skill generates a comprehensive, structured medical summary card for quick access during emergencies, travel, or hospital visits. It extracts critical user data, including severe allergies, active medications, chronic conditions, implants, and emergency contacts. Supports multi-format output (HTML, JSON, PDF) and prioritizes information by medical urgency (P0-P2) for rapid deployment in critical situations.

This skill assesses Bluetooth Low Energy (BLE) device security by scanning nearby devices, enumerating GATT services, and analyzing characteristics. It detects critical vulnerabilities such as unencrypted sensitive data exposure, weak pairing configurations, and known vulnerable device fingerprints, making it essential for IoT and medical device security auditing.