Skill

aws-ai-assistant

Building AI Solutions on AWS - Bedrock AgentCore agents, MCP server development and deployment, Knowledge Bases RAG with S3 Vectors, generative AI architecture patterns, CDK/CloudFormation infrastructure, and AWS AI service integration. Use when designing, building, deploying, or reviewing any AI system on Amazon Web Services including chatbots, copilots, multi-agent orchestration, content generation pipelines, and enterprise knowledge systems. Do NOT use for general backend development (use backend-assistant), frontend work (use frontend-assistant), or non-AI AWS infrastructure.

npx claudepluginhub diegouis/provectus-marketplace --plugin proagent-aws-ai

Tool Access

This skill uses the workspace's default tool permissions.

Preview

Comprehensive AWS AI skill covering the full lifecycle of AI solution development on Amazon Web Services — from architecture design through agent building, MCP server creation, knowledge base setup, and production deployment.

Supporting Assets

references/agentcore-patterns.mdreferences/architecture-patterns.mdreferences/cdk-iac-patterns.mdreferences/knowledge-bases-rag.mdreferences/mcp-server-patterns.md

SKILL.md

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Last CommitMar 12, 2026

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Building AI Solutions on AWS

When to Use This Skill

Designing AI architectures on AWS (chatbots, agents, copilots, content generators)
Building AI agents with Amazon Bedrock AgentCore
Creating and deploying Model Context Protocol (MCP) servers on AWS
Setting up RAG systems with Amazon Bedrock Knowledge Bases
Deploying AI infrastructure with CDK, CloudFormation, or Terraform
Integrating AWS AI services (Bedrock, SageMaker, Comprehend, Rekognition, etc.)
Reviewing AWS AI architectures for Well-Architected compliance
Implementing responsible AI with Bedrock Guardrails
Optimizing AI workload costs on AWS
Selecting foundation models (Claude, Nova, Llama, Mistral) for specific use cases
Configuring Cedar policies for agent access control
Setting up agent evaluation and observability

CRITICAL: Ask First, Load Later

DO NOT read reference files, run environment detection commands, or load mode files until the user has told you what they want to do.

MANDATORY: You MUST call the AskUserQuestion tool — do NOT render these options as text:

AskUserQuestion( header: "AWS AI", question: "What AWS AI topic would you like help with?", options: [ { label: "Bedrock AgentCore", description: "Build agents with AgentCore, Cedar policies, and evaluations" }, { label: "MCP Servers on AWS", description: "Create and deploy Model Context Protocol servers on AWS" }, { label: "RAG / Knowledge Bases", description: "RAG systems, Knowledge Bases, chunking, vector storage" }, { label: "AI Architecture", description: "AI architecture design, service and model selection, Well-Architected" } ] )

If the user selects "Other", offer CDK/IaC infrastructure for AI workloads.

Reference Routing

CONTEXT GUARD: Load reference files only when the user's request matches a specific topic below. Do NOT load all references upfront.

User Intent	Reference File
Building agents with Bedrock AgentCore, Cedar policies, evaluations	`references/agentcore-patterns.md`
Creating or deploying MCP servers on AWS	`references/mcp-server-patterns.md`
RAG systems, Knowledge Bases, chunking, vector storage	`references/knowledge-bases-rag.md`
AI architecture design, service selection, model selection	`references/architecture-patterns.md`
CDK infrastructure, CloudFormation, IaC for AI workloads	`references/cdk-iac-patterns.md`

Core Principles

Follow the AWS Well-Architected Generative AI Lens (six pillars)
Use AgentCore for production agent deployments with session isolation
Prefer S3 Vectors for cost-optimized vector storage (90% savings)
Always apply Bedrock Guardrails and Cedar policies for safety
Deploy infrastructure as code — never ClickOps

Visual Diagramming

Use Excalidraw MCP for AWS AI architecture diagrams, agent flow diagrams, RAG pipeline topology, and multi-agent orchestration visualizations.

Reference Assets

See AWS Bedrock AgentCore Docs, AWS MCP Servers, Gen AI CDK Constructs, and the Well-Architected Generative AI Lens.