Skill UI

Best practices for building AI chat interfaces with Makepad using MolyKit, covering cross-platform async, OpenAI/LLM integration, SSE streaming, chat widgets, and bot clients.

Design secure multi-tenant LLM hosting platforms on shared GPU inference infrastructure, covering tenant isolation, per-tenant quotas and rate limits, billing attribution, noisy-neighbor protection, and per-tenant policy controls for vLLM on Kubernetes.

Unified LLM inference layer built into Neon branches: one credential and one OpenAI-compatible API for frontier and open-source models from seven providers. Switch models without rewiring, wire it into OpenAI/Anthropic SDKs, and keep AI requests branch-scoped.

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.