Agents48k · #2237

aws-agentic-ai

Helps create and manage many AI agents at once on AWS cloud. Used for services like Gateway, Runtime, and Memory.

What it does

This tool helps manage and configure artificial intelligence agents on the AWS Bedrock platform. It covers everything from deployment to monitoring, using the various AWS AgentCore services.

When to use it

✓When deploying a new AI agent on AWS.
✓When you need to configure how agents remember conversations.
✓When monitoring the performance and security of your agents.

Example of what to ask your AI

“How do I set up the Gateway service for my AI agent on AWS Bedrock?”

Want more? Open the sections below.

How to use

The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.

Skill link

https://github.com/sickn33/agentic-awesome-skills/blob/main/plugins/agentic-awesome-skills-claude/skills/aws-agentic-ai/SKILL.md

Ready-to-paste message

Use this skill to help me: https://github.com/sickn33/agentic-awesome-skills/blob/main/plugins/agentic-awesome-skills-claude/skills/aws-agentic-ai/SKILL.md Read the SKILL.md file at that link and follow its instructions for my task.

View on GitHub ↗Raw file

How it works

The tool works by consulting specific documentation for AWS Bedrock AgentCore and other AWS services. It uses search tools to find the necessary information and guide you in configuring and managing your agents.

Installation

Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.

In the terminal (Claude Code, Codex, Cursor), in your project folder:

npx skills add sickn33/agentic-awesome-skills --skill aws-agentic-ai

Or download just the file:

curl -L https://raw.githubusercontent.com/sickn33/agentic-awesome-skills/main/plugins/agentic-awesome-skills-claude/skills/aws-agentic-ai/SKILL.md -o SKILL.md

Technical content (original SKILL.md)

Original text by the author, straight from GitHub.

AWS Bedrock AgentCore

When to Use

Use this skill when you need aWS Bedrock AgentCore comprehensive expert for deploying and managing AI agents at scale. Use when working with any AgentCore service including Gateway, Runtime, Memory, Identity, Code Interpreter, Browser, Observability, Agent Registry, or Evaluations. Covers agent deployment, MCP...

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

How to use this skill: Identify the service(s) the user needs from the table below, then read the corresponding service README before responding. For cross-service patterns (credentials, security, registry integration), check the Cross-Service Resources section. Verify AWS-specific details using the MCP documentation tools.

AWS Documentation Requirement

Always verify AWS facts using MCP tools before answering. Two documentation sources are available:

AgentCore-specific docs (mcp__acdocs__*) — bundled with this plugin, provides search_agentcore_docs and fetch_agentcore_doc for AgentCore documentation
General AWS docs (mcp__aws-mcp__* or mcp__*awsdocs*__*) — loaded via the aws-mcp-setup dependency for broader AWS documentation

Prefer the AgentCore docs MCP for AgentCore-specific questions. If MCP tools are unavailable, guide the user through the aws-mcp-setup skill's setup flow.

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`
Agent Registry	Catalog, discover, and govern agents/tools (Preview)	`services/registry/README.md`
Evaluations	Automated agent quality assessment (LLM-as-a-Judge)	`services/evaluations/README.md`

Common Workflows

Deploying a Gateway Target

Read services/gateway/README.md before implementing — Gateway setup involves deployment strategies, IAM, and auth choices that vary significantly by use case.

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

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

Managing Credentials

…

FAQ

What are 'AI agents'?

They are computer programs that use artificial intelligence to perform tasks, such as answering questions, automating processes, or interacting with systems.

What is AWS Bedrock AgentCore?

It is a set of Amazon Web Services that facilitates the creation, deployment, and management of AI agents at scale.

What does 'manage at scale' mean?

It means being able to handle many AI agents simultaneously, ensuring they all function well, are secure, and can be easily updated.

Details

Original description by the author

AWS Bedrock AgentCore comprehensive expert for deploying and managing AI agents at scale. Use when working with any AgentCore service including Gateway, Runtime, Memory, Identity, Code Interpreter, Browser, Observability, Agent Registry, or Evaluations.

repo: sickn33/agentic-awesome-skills

Popularity

GitHub stars: 48k
Ranking position: #2237
License: MIT
Updated: 10/15/2026

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