Skill UI

Expert in building Retrieval-Augmented Generation systems for LLM applications. Masters embedding models, vector databases, chunking strategies, and retrieval optimization. Capable of designing retrieval pipelines, implementing hybrid search, and optimizing context windows to ensure high-quality generation from retrieved documents.

This comprehensive workflow guides the end-to-end implementation of Retrieval-Augmented Generation (RAG) systems. It covers critical stages, including selecting optimal embedding models, setting up vector databases, designing chunking strategies, implementing hybrid retrieval, integrating Large Language Models (LLMs), and performing rigorous evaluation. Use this when building knowledge-grounded AI applications or advanced semantic search systems.

Build and operate Retrieval-Augmented Generation (RAG) systems using vector stores, embedding pipelines, and hybrid search. This skill covers ingesting documents, generating embeddings, storing in vector databases, and serving grounded LLM responses. It includes setting up embedding pipelines, vector store infrastructure, and deploying hybrid search (dense + sparse) to reduce hallucinations and improve semantic search over large document collections.

Monitor and evaluate Retrieval-Augmented Generation (RAG) systems using measurable metrics. This skill covers retrieval quality checks like Recall@k, hallucination detection, and groundedness scoring. It enables continuous regression testing and production monitoring for AI pipelines using Python libraries like RAGAS and OpenTelemetry.

This guide provides comprehensive best practices for creating high-quality videos using Remotion within a React environment. It covers advanced techniques for dynamic content generation, including integrating 3D models (Three.js), managing diverse assets like audio, video, and fonts, implementing complex animations, data visualization, and generating precise captions. Use this skill when developing professional, robust video generation workflows in React.

The RunAPI CLI serves as the dedicated execution layer for interacting with RunAPI's suite of advanced AI models. It allows developers to programmatically generate multimodal content, including images, videos, and music/audio, directly from the terminal or CI/CD pipeline. Users can submit one-off jobs, manage asynchronous tasks, and pass complex JSON request bodies to various services like Suno, Claude, and Gemini, making it essential for robust AI automation workflows.

This skill provides expert patterns for Salesforce platform development, covering Lightning Web Components, Apex triggers, REST/Bulk APIs, and External Client Apps. It supports Salesforce DX with scratch orgs and 2nd generation packages. Ideal for developers building scalable solutions on the Salesforce ecosystem.

Automates the generation of professional, high-resolution marketing screenshots for your web application using Playwright. This skill guides you through the entire process, from gathering user requirements (product hunt, social media, documentation) to deep code analysis (routes, components). It ensures comprehensive coverage of key features, providing marketing-ready assets for your product launch or documentation.

This skill enables AI agents to search, import, and analyze video content using Memories.ai's Large Visual Memory Model. It provides persistent video intelligence, allowing videos to be indexed once and queried repeatedly. Supports social media import from TikTok, YouTube, and Instagram, meeting summarization, knowledge base building, and cross-video Q&A via Memory Augmented Generation. Ideal for analyzing video URLs, researching social media trends, and summarizing recordings.

Automate software version management and changelog generation based on Semantic Versioning principles and Conventional Commits. Supports tools like semantic-release and standard-version for CI/CD integration, ensuring consistent release workflows and version precedence.

This pattern provides established best practices for implementing efficient similarity search in production systems. It is crucial for building semantic search engines, implementing Retrieval-Augmented Generation (RAG), creating advanced recommendation systems, and optimizing retrieval performance when scaling to millions of vectors.

This skill enforces a high standard for all generated code, prioritizing correctness, clarity, and idiomatic patterns across all languages. It helps minimize code bloat by removing unnecessary abstractions and verbose patterns, while also optimizing the agent's output by using targeted diffs and eliminating excessive conversational overhead. Use this for all code generation, refactoring, or review tasks.