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aws-agentic-ai

AWS Bedrock AgentCore comprehensive expert for deploying and managing all AgentCore services. Use when working with Gateway, Runtime, Memory, Identity, or any AgentCore component. Covers MCP target deployment, credential management, schema optimization, runtime configuration, memory management, and identity services.

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npx skills add https://github.com/rafaelcalleja/claude-market-place --skill aws-agentic-ai

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rafaelcalleja/claude-market-place

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Atualizado9 de dezembro de 2025 às 09:53

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Útil paraSOC

Desenvolvedores de softwareInformática e Matemática15-1252L4

name	aws-agentic-ai
aliases	["bedrock-agentcore","aws-agentic-ai"]
description	AWS Bedrock AgentCore comprehensive expert for deploying and managing all AgentCore services. Use when working with Gateway, Runtime, Memory, Identity, or any AgentCore component. Covers MCP target deployment, credential management, schema optimization, runtime configuration, memory management, and identity services.

AWS Bedrock AgentCore

AWS Bedrock AgentCore provides a complete platform for deploying and scaling AI agents with seven core services. This skill guides you through service selection, deployment patterns, and integration workflows using AWS CLI.

AWS Documentation Requirement

CRITICAL: This skill requires AWS MCP tools for accurate, up-to-date AWS information.

Before Answering AWS Questions

Always verify using AWS MCP tools (if available):
- mcp__aws-mcp__aws___search_documentation or mcp__*awsdocs*__aws___search_documentation - Search AWS docs
- mcp__aws-mcp__aws___read_documentation or mcp__*awsdocs*__aws___read_documentation - Read specific pages
- mcp__aws-mcp__aws___get_regional_availability - Check service availability
If AWS MCP tools are unavailable:
- Guide user to configure AWS MCP: See AWS MCP Setup Guide
- Help determine which option fits their environment:
  - Has uvx + AWS credentials → Full AWS MCP Server
  - No Python/credentials → AWS Documentation MCP (no auth)
- If cannot determine → Ask user which option to use

When to Use This Skill

Use this skill when you need to:

Deploy REST APIs as MCP tools for AI agents (Gateway)
Execute agents in serverless runtime (Runtime)
Add conversation memory to agents (Memory)
Manage API credentials and authentication (Identity)
Enable agents to execute code securely (Code Interpreter)
Allow agents to interact with websites (Browser)
Monitor and trace agent performance (Observability)

Available Services

Service	Use For	Documentation
Gateway	Converting REST APIs to MCP tools	`services/gateway/README.md`
Runtime	Deploying and scaling agents	`services/runtime/README.md`
Memory	Managing conversation state	`services/memory/README.md`
Identity	Credential and access management	`services/identity/README.md`
Code Interpreter	Secure code execution in sandboxes	`services/code-interpreter/README.md`
Browser	Web automation and scraping	`services/browser/README.md`
Observability	Tracing and monitoring	`services/observability/README.md`

Common Workflows

Deploying a Gateway Target

MANDATORY - READ DETAILED DOCUMENTATION: See services/gateway/README.md for complete Gateway setup guide including deployment strategies, troubleshooting, and IAM configuration.

Quick Workflow:

Upload OpenAPI schema to S3
(API Key auth only) Create credential provider and store API key
Create gateway target linking schema (and credentials if using API key)
Verify target status and test connectivity

Note: Credential provider is only needed for API key authentication. Lambda targets use IAM roles, and MCP servers use OAuth.

Managing Credentials

MANDATORY - READ DETAILED DOCUMENTATION: See cross-service/credential-management.md for unified credential management patterns across all services.

Quick Workflow:

Use Identity service credential providers for all API keys
Link providers to gateway targets via ARN references
Rotate credentials quarterly through credential provider updates
Monitor usage with CloudWatch metrics

Monitoring Agents

MANDATORY - READ DETAILED DOCUMENTATION: See services/observability/README.md for comprehensive monitoring setup.

Quick Workflow:

Enable observability for agents
Configure CloudWatch dashboards for metrics
Set up alarms for error rates and latency
Use X-Ray for distributed tracing

Service-Specific Documentation

For detailed documentation on each AgentCore service, see the following resources:

Gateway Service

Overview: services/gateway/README.md
Deployment Strategies: services/gateway/deployment-strategies.md
Troubleshooting: services/gateway/troubleshooting-guide.md

Runtime, Memory, Identity, Code Interpreter, Browser, Observability

Each service has comprehensive documentation in its respective directory:

Cross-Service Resources

For patterns and best practices that span multiple AgentCore services:

Credential Management: cross-service/credential-management.md - Unified credential patterns, security practices, rotation procedures

Additional Resources

AWS Documentation: Amazon Bedrock AgentCore
API Reference: Bedrock AgentCore Control Plane API
AWS CLI Reference: bedrock-agentcore-control commands