Skill UI

This comprehensive checklist guides developers through the pre-release verification process for Obsidian community plugins. It covers critical technical areas such as validating manifest structure, ensuring clean production builds (e.g., removing sourcemaps), identifying potential memory leaks, and verifying cross-platform readiness, ensuring a high-quality user experience upon submission.

This skill provides advanced patterns for handling high-volume Obsidian file operations, such as debouncing keystrokes, batching file reads, and throttling UI updates. It addresses single-threaded performance bottlenecks, ensuring plugins remain responsive and prevent UI freezes during bulk processing or rapid user interactions.

This skill automates the setup and configuration of Ollama, allowing users to deploy Large Language Models (LLMs) locally. It performs system assessment (OS, GPU, RAM), selects optimal models based on hardware availability, and handles installation across macOS, Linux, and Docker. Use this when you require free, self-hosted AI alternatives, need offline inference capabilities, or wish to eliminate reliance on costly hosted API services.

This skill provides comprehensive patterns for creating, maintaining, and validating OpenAPI 3.1 specifications for RESTful APIs. Use it for design-first API contract definition, generating specs from existing code, creating detailed API documentation, validating implementation conformance against the contract, and generating client SDKs. It ensures consistency across the entire API lifecycle.

Automates the Continuous Integration and Continuous Deployment (CI/CD) process for OpenEvidence clinical decision support integrations. This setup ensures that unit tests (using mocked evidence queries and citations) and live API connectivity tests are run automatically on every pull request and merge to main. It validates critical functions like query formatting, evidence parsing, citation extraction, and response quality scoring, maintaining high reliability for medical AI 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 runbook serves as a comprehensive guide for responding to incidents within the OpenEvidence platform. It outlines severity levels (P1-P3), step-by-step triage procedures, and mitigation actions, ensuring rapid and structured resolution of API downtime or degradation issues in a healthcare context.

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.

Provides comprehensive observability for OpenEvidence, a system delivering critical clinical evidence queries. It monitors key metrics such as query response latency (p95), evidence freshness, citation accuracy, and compliance audit logs (HIPAA). Essential for ensuring timely, accurate, and regulatory-compliant patient care information retrieval.

This guide provides comprehensive strategies for optimizing API interactions with OpenEvidence. It covers implementing robust caching mechanisms (TTL management), efficient batch retrieval of citations, connection pooling, and handling rate limits. By applying these techniques, users can significantly reduce query latency, ensuring smooth system performance even when handling complex multi-condition queries or large citation batches.

This comprehensive checklist guides the secure and compliant deployment of OpenEvidence, a clinical decision support system. It covers critical requirements for handling Protected Health Information (PHI), including HIPAA-grade security, robust API integration best practices, mandatory monitoring setup, and resilience testing. Use this guide to ensure your healthcare-adjacent system meets the highest standards of compliance and reliability.