Skill UI

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.

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 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 skill provides a comprehensive pattern for optimizing bulk API interactions. It enables efficient processing of large arrays of operations by supporting both synchronous (for small batches) and asynchronous (for large batches) execution. Key features include concurrency control, robust partial failure handling, progress tracking via background workers, and dedicated validation steps, ensuring reliable data throughput for high-volume requests.

This skill performs deep, line-by-line scanning of an entire codebase to understand its architecture, patterns, and dependencies. It systematically identifies critical issues across security vulnerabilities (SQL injection, secrets), performance bottlenecks (N+1 queries, caching), architectural flaws, and code quality gaps. It then automatically refactors and fixes all identified issues, transforming the code into professional, production-grade, enterprise-level quality.

Profiles application performance by analyzing CPU usage, memory allocation, and execution time with the application-profiler plugin, then identifies bottlenecks and suggests code-level optimizations for faster execution.

Provides prioritized performance optimization recommendations across frontend, backend, and infrastructure, offering bottleneck analysis, impact estimates, and phased implementation roadmaps when users request speed improvements or reviews.

A comprehensive guide for profiling, analyzing, and optimizing Python code. It addresses identifying performance bottlenecks related to CPU usage, excessive memory consumption, I/O operations, and slow data processing. Use this skill to improve application speed and efficiency in production environments.

Guides adding PyTorch FSDP2’s fully_shard integration to training loops, covering correct distributed init, bottom-up sharding, mixed precision/offload policies, DTensor-aware optimizers, and distributed checkpointing so oversized models or DeviceMesh sharding goals work reliably.

A practical decision framework for determining whether to use regular expressions or Large Language Models (LLMs) when parsing structured text (e.g., forms, quizzes, invoices). It recommends starting with deterministic, low-cost regex for 95-98% of structured data, and only invoking expensive LLM calls for identifying and validating ambiguous edge cases, optimizing both cost and accuracy.

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.

This comprehensive guide demonstrates how to build and test a voice agent using the Retell AI SDK in TypeScript. It walks through configuring the underlying LLM with a custom persona, creating the voice agent instance, and finally initiating both outbound phone calls and web calls through the API. This is essential for integrating AI-powered voice communication into customer service platforms, call centers, or telephony automation systems.