Can I use this for RAG development?

Absolutely. It covers the full RAG lifecycle including chunking strategies, Bedrock Knowledge Base setup, and S3 vector storage optimization.

Should I use this for general AWS backend work?

No, this skill is focused strictly on AI services. For general AWS backend tasks, you should use the backend-assistant skill.

What AWS AI services are supported?

It supports Amazon Bedrock, SageMaker, Knowledge Bases, Rekognition, Comprehend, and foundation models like Claude, Nova, and Llama.

Does it support Model Context Protocol (MCP)?

Yes, it includes specialized patterns for creating, configuring, and deploying MCP servers specifically on AWS infrastructure.

Does this skill help with AWS infrastructure?

Yes, it provides specific CDK, CloudFormation, and Terraform patterns for deploying AI-specific workloads like Bedrock agents and vector databases.

AWS AI Assistant

Name: AWS AI Assistant
Author: diegouis

bydiegouis

•

Cloud Infrastructure

Architects, builds, and deploys production-grade AI solutions on Amazon Web Services using Bedrock, MCP, and RAG patterns.

This skill provides a comprehensive toolkit for developing AI systems on AWS, guiding developers through the entire lifecycle from architectural design to production deployment. It specializes in building autonomous agents with Bedrock AgentCore, implementing Retrieval-Augmented Generation (RAG) using Bedrock Knowledge Bases, and deploying Model Context Protocol (MCP) servers. By integrating infrastructure-as-code patterns via CDK and following the AWS Well-Architected Generative AI Lens, it ensures that your AI workloads are cost-optimized, secure, and scalable.

Key Features

01AI infrastructure automation using CDK, CloudFormation, and Terraform

02Bedrock AgentCore orchestration and multi-agent system development

032 GitHub stars

04Well-Architected Generative AI compliance and Bedrock Guardrails setup

05RAG implementation with Bedrock Knowledge Bases and S3 Vector storage

06Model Context Protocol (MCP) server creation and AWS deployment

Use Cases

01Building enterprise-grade AI agents with session isolation and Cedar access control

02Architecting and deploying Model Context Protocol (MCP) servers within AWS VPCs

03Setting up cost-effective RAG systems using S3 Vectors for 90% storage savings

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add diegouis/provectus-marketplace aws-ai-assistant

For use in Claude.ai and ChatGPT

Download Skill