Skill UI

A comprehensive, multi-phase workflow designed for building sophisticated AI systems. This guide covers everything from designing autonomous agents and implementing single-agent logic, to orchestrating complex multi-agent systems and integrating advanced tools and memory mechanisms. Ideal for developers building production-grade AI applications using frameworks like CrewAI and LangGraph.

A comprehensive, multi-phase workflow designed for building sophisticated AI systems. This guide covers everything from designing autonomous agents and implementing single-agent logic, to orchestrating complex multi-agent systems and integrating advanced tools and memory mechanisms. Ideal for developers building production-grade AI applications using frameworks like CrewAI and LangGraph.

Helps teams ship reliable AI-powered products by detailing LLM integration patterns, RAG architecture, scalable prompt engineering, UX resilience, and cost-aware design.

Designs and runs structured evaluation frameworks for agent systems, balancing token budgets, tool usage, and LLM scoring to compare configurations, guard production, and track regressions across complex workflows.

Provides production-ready reference architectures for designing Lindy AI systems, covering basic integrations, event-driven queues, multi-agent orchestration, and high-availability failover.

This skill provides expert guidance in designing and developing advanced AI assistants, including sophisticated chatbots and intelligent conversational interfaces. It helps users build comprehensive, production-ready solutions by focusing on natural language understanding, context management, and seamless integrations. Ideal for AI development workflows and needing best practices for intelligent applications.

Document describes structured prompts, chain-of-thought guidance, few-shot examples, system prompt design, and tooling advice for building reliable prompt engineering workflows in LLM-augmented products.

Qdrant is a high-performance, Rust-powered vector database designed for production-grade RAG (Retrieval-Augmented Generation) and semantic search applications. It provides fast nearest neighbor search, supports complex hybrid search using metadata filtering, and offers horizontal scalability through sharding and replication. Ideal for building real-time recommendation systems and enterprise-level knowledge retrieval.

Designs and implements production-grade RAG systems with document chunking, embeddings, vector stores, hybrid search, reranking, and evaluation, ideal for knowledge-grounded AI apps that need semantic retrieval, similarity search, and context augmentation.

Provides automated guidance for designing, validating, and deploying Vertex AI pipelines within Google Cloud, covering compute, storage, BigQuery, and GCP-specific services for production readiness.

This tool is designed for defining and scaffolding production-grade, multi-agent workflows. It allows developers to move beyond single prompts by structuring complex tasks into explicit steps. Core capabilities include selecting workflow patterns (e.g., sequential, parallel, orchestrator), defining handoff contracts, implementing error recovery strategies, and enforcing context and cost discipline across long-running AI processes.