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Found 261 Skills
Knowledge Base RAG implements the complete Retrieval-Augmented Generation pipeline: document ingestion, intelligent chunking, embedding generation, vector store indexing, semantic retrieval, and grounded response generation.
Build backend AI with Vercel AI SDK v6 stable. Covers Output API (replaces generateObject/streamObject), speech synthesis, transcription, embeddings, MCP tools with security guidance. Includes v4→v5 migration and 15 error solutions with workarounds. Use when: implementing AI SDK v5/v6, migrating versions, troubleshooting AI_APICallError, Workers startup issues, Output API errors, Gemini caching issues, Anthropic tool errors, MCP tools, or stream resumption failures.
Framework for state-of-the-art sentence, text, and image embeddings. Provides 5000+ pre-trained models for semantic similarity, clustering, and retrieval. Supports multilingual, domain-specific, and multimodal models. Use for generating embeddings for RAG, semantic search, or similarity tasks. Best for production embedding generation.
Amazon Bedrock patterns using AWS SDK for Java 2.x. Use when working with foundation models (listing, invoking), text generation, image generation, embeddings, streaming responses, or integrating generative AI with Spring Boot applications.
Build on-device AI into React Native apps using ExecuTorch. Provides hooks for LLMs, computer vision, OCR, audio processing, and embeddings without cloud dependencies. Use when building AI features into mobile apps - AI chatbots, image recognition, speech processing, or text search.
QML and Qt Quick — declarative UI language for modern Qt applications. Use when building a QML-based UI, embedding QML in a Python/C++ app, exposing Python/C++ objects to QML, creating QML components, or choosing between QML and widgets. Trigger phrases: "QML", "Qt Quick", "declarative UI", "QQmlApplicationEngine", "expose to QML", "QML component", "QML signal", "pyqtProperty", "QML vs widgets", "QtQuick.Controls", "Item", "Rectangle"
Create production-grade motion graphics and videos using Remotion (React). Use whenever the user wants branded video content, product demos, data-driven video generation, or motion graphics with audio sync, web fonts, TailwindCSS styling, or media embedding. Covers: marketing videos, product launches, data visualizations, social media content, personalized video at scale, explainer videos with voiceover, animated charts, 3D scenes via Three.js. Requires Node.js and Claude Code environment. Trigger on: "create a Remotion video", "React video", "motion graphics", "branded video", "product demo video", "remotion", "video with audio", "TailwindCSS video", "data-driven video generation", "personalized video at scale", "video with voiceover". For mathematical animations, algorithm visualizations, or headless container rendering, use concept-to-video (Manim) instead.
Arquitecto de soluciones digitales basadas en IA. Dos modos: (1) ANALIZAR repositorios o código existente y explicar su arquitectura para cualquier audiencia, incluyendo personas sin conocimiento técnico. (2) DISEÑAR la arquitectura completa de sistemas nuevos que usan LLMs, RAG, agentes o fine-tuning. Usa este skill cuando el usuario mencione: arquitectura de IA, diseño de sistema con LLM, capas arquitectónicas, RAG architecture, tech stack para IA, vector database, diagrama de arquitectura, componentes del sistema, embedding, retrieval, pipeline de datos, MLOps, LLMOps, evaluar enfoques, RAG vs fine-tuning, diseñar solución de inteligencia artificial, explicar repositorio, explicar código, analizar proyecto, qué hace este repo, cómo funciona este sistema, explícame este proyecto, o cualquier variación de "qué componentes necesito" o "explícame cómo funciona esto". Actívalo cuando el usuario pegue código, README, estructura de archivos, o mencione un repositorio de GitHub para analizar. También cuando quiera diseñar arquitectura nueva.
Retrieval-Augmented Generation patterns including chunking, embeddings, vector stores, and retrieval optimization Use when: rag, retrieval augmented, vector search, embeddings, semantic search.
Configure OpenAI as embedding provider for GrepAI. Use this skill for high-quality cloud embeddings.
Semantic code search using Phase 1 vector embeddings and Phase 2 hybrid search.
Apply Convex database best practices for cost optimization, performance, security, and architecture. Use when: building Convex backends, optimizing queries, handling embeddings/vector search, reviewing Convex code, designing schemas, planning migrations, or discussing Convex architecture. Keywords: Convex, real-time database, queries, mutations, actions, indexes, pagination, vector search, embeddings, schema, migrations, ctx.auth, convex-helpers, bandwidth.