Skill UI

This comprehensive reference architecture guides the integration of OpenEvidence into clinical decision support systems (CDS). It is designed for healthcare platforms needing evidence-based query processing, deterministic citation tracking, and full audit logging. Key considerations include HIPAA compliance, optimizing for sub-second response times in point-of-care workflows, and establishing robust data models and event pipelines.

This comprehensive guide provides solutions for common PostHog integration issues, helping developers diagnose and fix tracking failures. It covers critical topics including event capture problems, feature flag misbehavior, API authentication errors (401/429), and user identity fragmentation. Learn best practices for robust data analytics implementation.

This comprehensive workflow guides developers through implementing full-stack product analytics using PostHog. It covers key areas including defining event taxonomies, capturing user actions (client-side), identifying and updating user properties, and performing B2B group analytics. It also demonstrates server-side event tracking using posthog-node, ensuring a complete, accurate, and traceable view of user behavior and product usage across web, mobile, and API sources.

This skill guides the complete setup and configuration of PostHog SDKs for modern applications. It covers best practices for implementing client-side (browser) and server-side (Node.js/Python) event tracking. Users will learn how to install the necessary SDKs via npm or pip, manage environment variables for both Project API Keys and secure Personal API Keys, and initialize the tracking service correctly to ensure accurate and reliable event capture in PostHog.

This guide provides a comprehensive workflow for setting up a robust local development environment for PostHog integrations. It covers enabling debug mode for real-time event inspection, implementing mocking strategies for stable unit testing, and configuring integration tests using a dedicated development PostHog project. Use this when building or testing analytics tracking features with posthog-js or posthog-node to ensure fast, isolated development cycles.

This comprehensive guide details how to properly set up PostHog for multiple environments (development, staging, production). It emphasizes data isolation by utilizing separate projects, allowing granular control over critical analytics features such as session recording rates, autocapture, and feature flag rollout percentages. This ensures accurate and reliable data tracking across all deployment stages, preventing test data from polluting production analytics.

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.

This guide provides a comprehensive, production-grade architecture for implementing analytics using PostHog. It covers best practices for event taxonomy design, SDK layering (client and server), feature flag management, and integrating the data pipeline (CDP). Ideal for tracking user lifecycle, feature adoption, and revenue in complex web applications.

A comprehensive guide to implementing production-ready patterns for PostHog analytics integration. This includes establishing singleton clients for both server-side (Python/Node) and client-side environments. It provides type-safe event capture utilities, React hooks for handling feature flags (including variants), and the full Next.js App Router setup for robust pageview tracking. Patterns are available for both TypeScript/JavaScript and Python environments.

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.

A comprehensive guide to configuring Sentry error tracking and performance monitoring across modern application architectures. Learn how to implement coherent, end-to-end distributed tracing for monoliths, microservices, serverless functions (Lambda/Cloud Functions), event-driven systems, SPAs, and mobile applications, ensuring all application hops are monitored from a single source.

Master advanced error capture and context enrichment using Sentry SDKs for both TypeScript and Python. This skill teaches how to reliably capture exceptions, track non-exception events (messages), and enrich reports with structured data like user context, tags, breadcrumbs, and custom fingerprints. It is essential for dramatically improving the ability to diagnose complex production issues quickly.