Which vector databases are supported?

The skill provides patterns and best practices for popular vector stores including Pinecone, Weaviate, pgvector, and ChromaDB.

What is the AI Engineer skill for Claude Code?

It is a specialized capability that provides expert guidance and implementation patterns for building production-grade LLM applications, RAG pipelines, and agent-based systems.

Does this skill support local LLMs?

Yes, it includes expertise for integrating local models using tools like Ollama and llama.cpp, alongside major providers like OpenAI and Anthropic.

How does it handle agent orchestration?

It provides architectures for multi-agent systems, including tool use patterns, memory management, and robust error handling for production environments.

Can this help with complex RAG strategies?

Absolutely. It offers specific strategies for fixed-size, semantic, hierarchical, and sliding window chunking to optimize retrieval quality.

AI Engineer

Name: AI Engineer
Author: kriscard

bykriscard

Data Science & ML

Architects and implements production-grade LLM applications, RAG pipelines, and intelligent agent orchestrations.

About

The AI Engineer skill empowers Claude with expert-level knowledge in building sophisticated AI-powered applications and retrieval-augmented generation systems. It provides deep technical guidance on document chunking strategies, vector database selection, and multi-agent orchestration patterns. Whether you are integrating specialized LLMs like GPT-4 and Anthropic, optimizing similarity search, or managing complex tool-use workflows, this skill ensures your AI features are performant, scalable, and follow industry best practices for memory management and production stability.

Key Features

Multi-agent orchestration and complex tool-use pattern implementation
Vector database integration for Pinecone, Weaviate, pgvector, and ChromaDB
Model selection guidance for OpenAI, Anthropic, and local LLMs like Ollama
Production-ready best practices for monitoring, caching, and rate limiting
Expert RAG pipeline design including chunking and embedding optimization
0 GitHub stars

Use Cases

Developing multi-agent systems for automated data analysis and task execution
Implementing hybrid search systems that combine vector similarity and keyword matching
Building conversational Q&A agents over large, unstructured document sets

About

Key Features

Multi-agent orchestration and complex tool-use pattern implementation
Vector database integration for Pinecone, Weaviate, pgvector, and ChromaDB
Model selection guidance for OpenAI, Anthropic, and local LLMs like Ollama
Production-ready best practices for monitoring, caching, and rate limiting
Expert RAG pipeline design including chunking and embedding optimization
0 GitHub stars

Use Cases

Developing multi-agent systems for automated data analysis and task execution
Implementing hybrid search systems that combine vector similarity and keyword matching
Building conversational Q&A agents over large, unstructured document sets