Skill UI

This skill provides comprehensive guidance for building scalable, maintainable, and production-ready backend applications using Node.js. It covers modern architectural patterns, best practices, and implementation details for REST APIs, GraphQL servers, microservices, authentication, real-time WebSockets, and complex database integrations (SQL/NoSQL).

A comprehensive guide covering essential security practices for developing Obsidian plugins. Learn how to safely handle sensitive credentials (avoiding data.json), implement robust input validation, prevent Cross-Site Scripting (XSS), secure URI handlers, and adhere to modern Electron and Obsidian guidelines for data privacy and system integrity.

A comprehensive guide for developers migrating and upgrading Obsidian plugins across major API versions. It covers critical steps such as auditing plugin compatibility, implementing version-aware settings migration logic (for user data persistence), replacing deprecated API calls (e.g., Vault, Editor, FileManager), and updating configuration files like manifest.json to ensure stability on new Obsidian releases.

This guide provides comprehensive error handling and debugging steps for the OpenEvidence API. It covers common failure points—including authentication issues (401, 403), query validation errors (422), rate limiting (429), and service timeouts (408). Users can learn how to diagnose the root cause of connection failures and refine their clinical queries to maximize data retrieval accuracy.

This skill provides comprehensive guidelines and optimization strategies for managing API usage and reducing costs when interacting with OpenEvidence. It covers best practices such as implementing caching, batching requests, selecting appropriate API tiers, and monitoring usage dashboards to ensure efficient and cost-effective data retrieval in healthcare applications.

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 bundle collects comprehensive diagnostic evidence from the OpenEvidence clinical decision support API. It validates API credentials, checks query endpoint health, retrieves evidence citations, and captures model metadata. Use it to diagnose complex issues like query failures, missing references, or high latency without needing direct dashboard access.

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 provides a local development loop for integrating the OpenEvidence clinical decision support API. Developers can build health-tech tools and quickly iterate using mock evidence queries, citation responses, and clinical summaries. It supports toggling between mock mode for rapid development and sandbox mode for validation against the real platform, ensuring development uses de-identified data.

This deep dive guide provides comprehensive strategies and detailed checklists for migrating to the OpenEvidence platform. It covers various migration methods, such as parallel run, Strangler fig, and Big bang approaches. The guide assists technical teams in planning the transition by addressing key areas including API mapping, data migration plans, rollback procedures, and performance baselines, ensuring a secure and smooth transition for healthcare IT systems.

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.

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.