Skill UI

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 module provides minimal working examples for the OpenEvidence API, designed for clinical intelligence applications. Users can perform complex clinical queries to get treatment recommendations, check potential drug interactions based on patient context, and search for authoritative medical guidelines from major health organizations (e.g., ACC/AHA). Ideal for developing evidence-based healthcare decision support systems.

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 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.

This skill demonstrates advanced techniques for managing high-volume API calls to LLM services via OpenRouter. It implements multi-key round-robin strategies, incorporating health checks and circuit breakers to distribute load across multiple API keys. It also covers concurrent request processing using asyncio and provider-level failover, ensuring high availability and maximizing throughput when facing rate limits.

This skill provides comprehensive tools to monitor the real-time availability, status, and performance of various large language models (LLMs) accessed via OpenRouter. It supports active health probes, catalog checks, and scheduled monitoring via Python and Bash, ensuring robust fault detection and failover logic for production systems that rely on external AI APIs.

Monitor the operational health of PostHog integrations by tracking critical metrics. This skill checks event ingestion rates, feature flag evaluation latency, and monitors billing volume consumption. It also provides rules for Prometheus/Grafana alerting, ensuring data pipelines remain stable and alerting on potential performance bottlenecks or exceeding API limits.

A comprehensive checklist and guide for ensuring robust PostHog integration in a production environment. It covers crucial topics like SDK configuration hardening, implementing graceful degradation, setting up health check endpoints for monitoring, and handling serverless shutdown hooks to maximize reliability during deployment and operation.

This comprehensive guide details how to configure a complete Continuous Integration and Continuous Deployment (CI/CD) pipeline for Replit applications using GitHub Actions. Learn to connect your GitHub repository, set up automated testing on pull requests, perform post-deployment health checks, and ensure reliable, automated deployment whenever code is pushed.

This comprehensive skill manages the full lifecycle deployment of applications on the Replit platform. It supports three key deployment types: Static (for frontends), Autoscale (for variable APIs), and Reserved VM (for always-on services). Use this for configuring production environments, setting up custom domains, implementing health checks, managing environment secrets, and optimizing scaling strategies.

This comprehensive guide provides methods for implementing deep observability for Replit deployments. It covers building detailed health check endpoints (tracking uptime, memory, and DB status), setting up structured JSON logging, detecting cold start events, and configuring external alerting mechanisms (e.g., UptimeRobot, Slack). Use this when building robust, production-grade applications that require continuous performance monitoring and reliability tracking.