Skill UI

This skill provides comprehensive tools for implementing granular cost controls for OpenRouter API usage. It allows users to set hard spending caps via per-key limits, track real-time credit balances, and implement client-side middleware for budget enforcement. Use cases include preventing unexpected overspending, managing multi-key environments, and optimizing API calls using cost-saving model variants.

This skill provides comprehensive reference architectures for building production-grade AI applications using OpenRouter as a unified LLM gateway. It covers three scaling patterns: simple single-service, standard microservice (with caching and budget enforcement), and enterprise event-driven systems (with message queues and workers). Use this when planning system design, reviewing complex AI pipelines, or ensuring scalability and cost control in your LLM integration.

This skill provides comprehensive guides on implementing real-time streaming responses using OpenRouter's Server-Sent Events (SSE) capability. It covers various languages (Python, TypeScript, FastAPI, JavaScript), enabling developers to build modern, fast-feeling chat interfaces. By streaming tokens as they are generated, it significantly reduces the time-to-first-token (TTFT) and improves user experience for long completions and chat applications.

This skill enables the development of robust, event-driven pipelines interacting with the Perplexity Sonar API. Since Perplexity lacks native webhooks, this system simulates event patterns using advanced techniques: Streaming Server-Sent Events (SSE) for real-time data, job queues (like BullMQ/SQS) for asynchronous batch research, and cron-triggered monitoring for scheduled news and trend alerts. It provides patterns for building complex, reliable, and scalable AI applications.

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.

Automates the collection of comprehensive diagnostic evidence for PostHog support tickets. This bundle gathers runtime environment details (Node, Python versions), SDK versions, API connectivity status to US/EU clouds, feature flag settings, event flow testing results, and redacts sensitive environment variables. It provides a structured, support-ready archive for rapid troubleshooting.

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.

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.