Skill UI

Automate PostHog analytics, events, feature flags, projects, and profiles through Composio’s Rube MCP toolkit, starting each workflow by refreshing schemas.

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 guide provides comprehensive strategies for implementing privacy-safe analytics using PostHog. It addresses critical compliance requirements like GDPR and CCPA, covering explicit consent management (opt-in/opt-out), real-time property sanitization (stripping PII like IP or tokens), and methods for handling Data Subject Access Requests (SAR) and the Right to Erasure. Use this to build highly compliant and privacy-preserving analytics pipelines.

A comprehensive guide for deploying PostHog analytics platform across diverse production environments. Covers advanced configurations for Next.js/Vercel (including reverse proxy for ad blocker bypass and edge functions for server-side capture), self-hosted setups using Docker, and deployment patterns for Google Cloud Run. Ensures robust, reliable user behavior tracking regardless of the hosting platform.

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.

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.

A comprehensive guide to implementing expert customer data tracking across the entire stack. Includes best practices for client-side tracking using Analytics.js (React/Next.js), high-performance server-side tracking via Node.js webhooks, user identity resolution, and data governance strategies for Segment CDP.

Guides automating Segmetrics workflows through Composio's Rube MCP connector, covering tool discovery, connection checks, schema compliance, and execution best practices for marketing analytics teams.

Master advanced techniques for scaling Supabase projects to handle high production load. This skill covers six critical layers: implementing read replicas for analytics, optimizing connectivity using connection pooling (Supavisor), upgrading compute resources, utilizing CDN caching for Storage, deploying Edge Functions regionally, and performing database table partitioning. Ideal for engineers preparing for traffic spikes or optimizing large-scale database architectures.

This guide provides a comprehensive setup for Vercel observability. It covers enabling built-in analytics, collecting detailed runtime logs, implementing structured logging, configuring external log drains (e.g., Datadog, Sentry), and integrating OpenTelemetry tracing. Use this when monitoring production deployments and setting up robust error tracking.