Skill UI

This comprehensive guide details production-grade architectural patterns for integrating MaintainX with large enterprise systems, including ERPs, SCADA, and data warehouses. It covers best practices for event-driven synchronization using webhooks and Pub/Sub, implementing bi-directional sync gateways, and establishing scalable analytics data pipelines.

Handles disaster recovery, PITR, and automated failover for production databases via the database-recovery-manager plugin, streamlining backup validation and recovery testing whenever users ask for database recovery support.

Automated memcached configuration assistant that provides best-practice guidance and production-ready snippets for caching, database, messaging, and microservices patterns in backend services.

Mastering the full lifecycle of machine learning models from data ingestion to deployment. This expertise covers building robust, production-grade MLOps pipelines using orchestrators like Kubeflow, Airflow, or Prefect. Key skills include feature store implementation with Feast, rigorous data validation, experiment tracking (e.g., MLflow), model registry management, and automated deployment workflows. Essential for automating model training and ensuring reproducibility at scale.

This skill provides comprehensive guidance for building production-grade MLOps pipelines. It covers the entire machine learning lifecycle, from initial data ingestion and rigorous feature engineering to model training, advanced validation (A/B testing, performance drift), and robust deployment strategies (Canary, Blue-green). It enables the design and orchestration of scalable, reproducible, and observable ML systems using tools like Airflow and Kubeflow.

Monitors PostgreSQL and MySQL health with real-time metrics, predictive alerts, and automated remediation, letting you preempt bottlenecks and respond to production issues quickly.

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).

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 guide outlines the strict multi-environment setup for the OpenEvidence clinical AI system, crucial for maintaining HIPAA compliance. It details how to configure and validate separate environments (Development, Staging, Production) to manage different data classifications—synthetic, de-identified, and full PHI. The process emphasizes environment validation checks, BAA adherence, and robust audit logging to prevent regulatory violations and PHI exposure.

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.

This skill provides comprehensive, structured audit logging for OpenRouter API interactions. It automatically captures essential metadata like total cost, token usage, model details, latency, and status, while also performing PII redaction. The resulting audit trail is stored in an append-only SQLite database, making it ideal for building compliance-ready systems, detailed cost monitoring, and deep debugging of production environments.

Auto-activating ML training skill that kicks in whenever you mention optuna study creator, then guides you through data prep, model training, hyperparameter tuning, and experiment tracking with production-ready code and validations.