Skill UI

This skill provides a comprehensive guide to configuring the Perplexity Sonar API across distinct operational environments (development, staging, and production). It details how to manage environment-specific API keys, enforce tailored rate limits, select appropriate models (e.g., sonar vs. sonar-pro), and implement cost optimization strategies. Use this when migrating or integrating Perplexity search functionality into a multi-tier, production-grade application architecture.

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.

This guide details the standardized directory structure and best practices for building robust and maintainable plugins for Claude Code. It covers critical components including the plugin manifest (plugin.json), commands, agents, skills, and hooks. Use this skill when you need to scaffold a new plugin, understand the required component organization, or configure the plugin's architecture according to best practices.

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.

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.

Blueprint skill describing the architecture, stack, folder layout, API patterns, and testing/deployment workflows used by the Zenith AI customer discovery platform clade. Use it as a reference when working on this dedicated project to understand expectations for frontend, backend, AI integration, and QA.

A guide to building robust projections and read models from continuous event streams. This pattern is essential for implementing CQRS read sides, creating materialized views, optimizing query performance, and building complex search indexes in event-sourced architectures. Use it when data needs to be transformed and aggregated from raw events into optimized query structures.

This skill acts as an expert architect to generate complete, production-ready Python project structures. It supports various modern architectures, including FastAPI, Django (full-stack), standalone libraries, and CLI tools. It ensures adherence to best practices, includes dependency management (uv, pip), type hinting, and comprehensive testing skeletons.

Automated quickstart guide generator that kicks in whenever you mention the trigger phrase, offering step-by-step technical documentation guidance, code samples, and validation to ensure API docs, user guides, and architecture notes stay production-ready.

Expert in designing and implementing advanced Retrieval-Augmented Generation (RAG) pipelines. This role focuses on optimizing the entire information retrieval process—from document chunking and embedding model selection to vector database architectures and hybrid search strategies. The goal is to bridge the gap between raw knowledge documents and accurate LLM understanding, drastically reducing hallucination and enhancing context relevance for enterprise applications.

Guides backend engineers through planning, implementing, and validating rate limit middleware across Node.js, Python, Go, and microservice architectures.

This guide outlines best practices for building highly flexible and maintainable React components. It addresses common architectural pitfalls, such as 'boolean prop proliferation,' by recommending advanced patterns like compound components, state lifting, and context providers. Use this when refactoring large component libraries, designing robust APIs, or reviewing component architecture, including considerations for React 19 APIs.