Skill UI

This skill provides best practices for structuring complex, production-ready applications built with Makepad. It details robust asynchronous communication patterns, showing how to integrate the Tokio runtime, manage state synchronization, and handle data flow between the main UI thread and background worker tasks using MPSC channels and SignalToUI. Essential for developing advanced chat clients or data streaming services.

A comprehensive guide to product analytics, covering essential concepts like event tracking (PostHog, Mixpanel), funnels, cohort analysis, retention metrics, North Star Metrics, and OKR setting. Use this skill to define data-driven product strategies, optimize user journeys, and build robust performance dashboards.

A framework for building production-ready, low-latency, multimodal AI agents. It allows developers to orchestrate complex pipelines that seamlessly handle real-time audio, video, and text data. Key capabilities include integrating multiple AI services (LLMs, STT, TTS), managing conversation context, and enabling tool use and function calling for advanced, interactive AI applications.

Comprehensive guide to LLM Operations (LLMOps). Covers advanced topics like Retrieval-Augmented Generation (RAG), vector database management (Pinecone, Chroma), embeddings, fine-tuning, prompt engineering, and cost optimization. Essential for bridging the gap between AI prototypes and reliable, scalable, production-grade AI systems.

An expert guide for developing production-grade AI applications, chatbots, and generative UIs using the Vercel AI SDK, Next.js, and React. Covers core concepts including streaming text (`streamText`), managing chat state (`useChat`), implementing complex tool/function calling, and ensuring robust structured data generation (JSON schemas) across multiple LLM providers.

This comprehensive index provides access to 32 production-ready, specialized engineering skills. It covers the entire development lifecycle, including system architecture, frontend, backend, DevOps, security operations, AI/ML, and data engineering. It is designed to guide users in selecting and loading only the specific module needed, ensuring focused expertise for complex tasks.

A comprehensive guide for defining, tracking, and interpreting product metrics across all stages—from discovery to maturity. This skill covers selecting metric frameworks (AARRR, HEART), defining stage-appropriate KPIs, designing multi-layered dashboards, and executing deep analyses like cohort and retention modeling. It helps teams transform raw data into actionable product strategies, moving beyond vanity metrics.

A comprehensive guide to building robust database layers in Kotlin using the Exposed ORM framework. It covers advanced patterns including DSL queries, DAO entity management, transaction handling, connection pooling with HikariCP, schema migration via Flyway, and implementing the Repository pattern for clean, production-grade data access.

This skill provides methods for reliably extracting typed, validated, and structured data from Large Language Models (LLMs), moving beyond simple text parsing. It covers best practices across major APIs (OpenAI's response_format, Anthropic's tool_use, Google's responseSchema) and local models. Key workflows include defining schemas (Pydantic/Zod), implementing constrained decoding, and building robust retry loops for production-grade data pipelines.

Guides borrowers through refinance evaluation by collecting loan data, extracting mortgage statement fields, validating inputs against underwriting rules, and recommending the best products with clear, consumer-friendly explanations.

A comprehensive strategy for regression testing in AI-assisted development. It focuses on identifying systematic blind spots, such as schema inconsistencies between sandbox and production paths, and integrating automated bug checks into the development workflow. This guide teaches how to build robust, database-free API tests to ensure code quality when AI models contribute to the codebase.

Build a production-ready, AI-powered data collection agent for any public source (job boards, prices, news, etc.). This agent automatically scrapes data, enriches it using free LLMs (like Gemini Flash), and stores the results in databases such as Notion, Sheets, or Supabase. It runs on a free schedule using GitHub Actions and learns from user feedback, making it ideal for continuous data monitoring and tracking.