Skill UI

Expert guide for Odoo eCommerce and Website: product catalog, payment providers, shipping methods, SEO, and order-to-fulfillment workflow.

Deploy a local LLM stack with Ollama, Open WebUI, and LiteLLM for offline, privacy-first AI workloads. Covers installation, model selection, Docker Compose setup, API usage, and custom Modelfiles for development environments.

Guides choosing the right output format (plain, markdown, djot, HTML, JSON, DocTags) and token-reduction level for Xberg document extraction, based on downstream consumers like LLMs, RAG systems, parsers, or human review.

This blueprint guides the creation of F.R.I.D.A.Y., a tactical, low-latency voice assistant inspired by Iron Man. It utilizes the specialized Pipecat framework to orchestrate a comprehensive, real-time audio pipeline: STT (OpenAI Whisper/GPT-4o) -> LLM (Gemini) -> TTS (OpenAI Nova). Ideal for developers needing to build advanced, multi-provider, conversational voice applications.

This skill covers principles for identifying tasks suited to LLM processing, designing effective project architectures, and iterating rapidly using agent-assisted development. It applies to batch pipelines, multi-agent systems, and interactive applications. Key concepts include task-model fit recognition, manual prototyping, pipeline architecture, file system state management, and structured output design for reliable parsing.

Caching strategies for LLM prompts, including Anthropic prompt caching, response caching, cache invalidation, and Cache Augmented Generation (CAG) to reduce latency and cost.

Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability. Learn core capabilities including few-shot learning, chain-of-thought prompting, prompt optimization, template systems, and system prompt design. This skill helps design complex prompts for production applications, optimize consistency, and implement structured reasoning patterns for specialized AI assistants.

Protect AI systems from prompt injection attacks by implementing input sanitization, tool permissioning, and output validation. Essential for securing LLM applications, RAG pipelines, and agentic workflows against indirect and direct attacks.

PydanticAI is a robust Python framework that integrates Pydantic's type-checking and validation capabilities into Large Language Models (LLMs). It allows developers to build production-ready AI agents that guarantee structured, validated outputs, moving beyond simple raw strings. Key features include supported tool calling, dependency injection for testability, and multi-model compatibility across major providers like OpenAI, Anthropic, and Gemini. Use this when reliability and correct data structure are paramount.

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.