Skill UI

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.

Automates random winner selection from lists, CSV/Excel, or Google Sheets for giveaways, raffles, contests, and team assignments while logging transparent, cryptographically fair draws and preventing duplicate wins.

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.

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.

Automate Ragic operations through Composio’s Rube MCP toolkit by always discovering current tool schemas, ensuring active connections, and executing schema-compliant workflows via RUBE_SEARCH_TOOLS, RUBE_MANAGE_CONNECTIONS, and RUBE_MULTI_EXECUTE_TOOL calls.

Ray Data offers scalable distributed data processing for ML workloads, streaming across CPU/GPU clusters and integrating with PyTorch, TensorFlow, and Ray Train. Use it for batch inference, ETL, and multi-modal preprocessing from a laptop up to hundreds of nodes.

Ray Train orchestrates distributed PyTorch, TensorFlow, and HuggingFace workloads from laptops to clusters, automating GPU placement, fault tolerance, checkpointing, and hyperparameter sweeps so teams can scale massive model training with minimal code changes.

Automate Re Amaze operations through Composio’s Rube MCP toolkit by discovering current tool schemas, verifying connections, and executing schema-compliant workflows via managed tool calls.

A comprehensive optimization guide maintained by Vercel, covering 45 rules for React and Next.js. Use this resource when building new components, implementing complex data fetching (client or server-side), refactoring code, or systematically improving bundle size and overall load times for high-performance web applications.

A senior specialist for building modern, production-grade React applications using React 19, Server Components, and Next.js App Router. Expertise includes implementing complex state management (Redux, Zustand), optimizing performance with memoization, handling data fetching with TanStack Query, and ensuring strict TypeScript typing and accessibility standards. Ideal for architecting large-scale frontend systems.

A comprehensive architectural guide for building production-grade flowcharts using ReactFlow. This pattern set covers key challenges including hierarchical tree navigation (expandable/collapsible nodes), performance optimization via incremental rendering and memoization, and advanced state management using Redux-like reducer patterns with history tracking (undo/redo).

This skill provides production-ready architectural patterns for building robust mobile applications using React Native and Expo. It covers complex topics like state management, sophisticated navigation flow, integrating native modules, and implementing offline-first capabilities. It is ideal for starting new projects or optimizing existing large-scale React Native applications.