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Aws Agentic Ai

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zxkane
aws-agentic-ai

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 tool integration, credential management, agent discovery, governance workflows, and automated quality assessment. Essential when user mentions AgentCore, agent runtime, agent registry, agent evaluation, MCP gateway, deploy agent, register MCP server, discover agents, evaluate agent quality, agent credentials, or wants to build, deploy, catalog, or monitor AI agents on AWS.

Overview

Publisherzxkane
Repositoryaws-skills
Skill nameaws-agentic-ai
Stars
370
Forks
39
Bundled files
22
LicenseMIT
Links
Skill sourceRepositoryHomepage
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 22 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by zxkane on GitHub. Read the source before you install it.

Installation

Install the Aws Agentic Ai AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/zxkane/aws-skills/tree/main/plugins/aws-agentic-ai/skills/aws-agentic-ai
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/zxkane/aws-skills.git /tmp/aws-skills
mkdir -p .claude/skills
cp -r /tmp/aws-skills/plugins/aws-agentic-ai/skills/aws-agentic-ai .claude/skills/aws-agentic-ai
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Aws Agentic Ai in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Aws Agentic Ai on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Aws Agentic Ai is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

AWS Bedrock AgentCore

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

ServiceUse ForDocumentation
GatewayConverting REST APIs to MCP toolsservices/gateway/README.md
RuntimeDeploying and scaling agentsservices/runtime/README.md
MemoryManaging conversation stateservices/memory/README.md
IdentityCredential and access managementservices/identity/README.md
Code InterpreterSecure code execution in sandboxesservices/code-interpreter/README.md
BrowserWeb automation and scrapingservices/browser/README.md
ObservabilityTracing and monitoringservices/observability/README.md
Agent RegistryCatalog, discover, and govern agents/tools (Preview)services/registry/README.md
EvaluationsAutomated 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.

  1. Upload OpenAPI schema to S3
  2. (API Key auth only) Create credential provider and store API key
  3. Create gateway target linking schema (and credentials if using API key)
  4. 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

Read cross-service/credential-management.md first — credential patterns differ across services and getting them wrong causes hard-to-debug auth failures.

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

Discovering Agents and Tools (Agent Registry)

Read services/registry/README.md first — the registry has governance workflows, MCP endpoint options, and sync modes that affect how records become discoverable.

  1. Create a registry to catalog your organization's AI resources
  2. Register resources (MCP servers, agents, skills, custom) with descriptive metadata
  3. Submit records for approval (auto-approve for dev, manual for production)
  4. Search and discover approved resources via CLI or MCP endpoint

Agent Registry is in Preview. Available in us-east-1, us-west-2, eu-west-1, ap-northeast-1, ap-southeast-2.

Evaluating Agent Quality

Read services/evaluations/README.md first — evaluators, scoring modes, and IAM setup vary between online monitoring and on-demand testing.

  1. Instrument the agent with OpenTelemetry (ADOT) for trace collection
  2. Create evaluators (use built-in like Builtin.Helpfulness or create custom)
  3. Set up online evaluation with sampling rate and data source
  4. Monitor scores in CloudWatch dashboards; investigate low-scoring sessions

Monitoring Agents

Read services/observability/README.md for the full monitoring setup — observability configuration depends on your Runtime protocol and framework choice.

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

Deep-Dive References

Each service README (linked in the table above) contains sub-links to getting-started guides, troubleshooting, and advanced topics. Start with the service README and follow pointers from there.

Advanced Runtime & OAuth References

Deep-dive reference documentation for Runtime internals, deployment, OAuth integration, and communication protocols. Read these when building production Runtime deployments or configuring OAuth authentication:

  • OAuth Integration: references/agentcore-oauth-integration.md - Three-layer OAuth architecture (Inbound JWT, Outbound Credential Provider, Gateway OAuth), Cognito configuration, supported IdPs, end-to-end CDK examples
  • Runtime Core Mechanisms: references/agentcore-runtime-core.md - Container contract, MicroVM Session model, Agent lifecycle (per-request vs per-session), tool integration (MCP/HTTP), startup flow
  • Runtime Deployment & Operations: references/agentcore-runtime-deploy.md - CDK deployment (L1/L2 constructs), multi-Runtime architecture, security model, observability (OTel/CloudWatch), BedrockAgentCoreApp vs FastAPI comparison
  • Runtime Protocol Reference: references/agentcore-runtime-protocols.md - HTTP, MCP, A2A, AG-UI protocol specifications with container contracts, endpoint specs, and selection guide

Runnable Script Templates

Production-ready templates in scripts/ for common deployment patterns:

ScriptProtocolDescription
Dockerfile.runtime-template—ARM64 multi-stage Docker build for AgentCore Runtime
runtime-fastapi-template.pyHTTPFastAPI Runtime with SSE streaming and MCPClient
mcp-server-template.pyMCPMCP Server with Streamable HTTP transport
a2a-server-template.pyA2AA2A Server with Agent Card discovery
agui-server-template.pyAG-UIAG-UI Server with standard AG-UI event stream
gateway-custom-resource-lambda.py—CDK Custom Resource Lambda for Gateway lifecycle

Cross-Service Resources

For patterns and best practices that span multiple AgentCore services:

Additional Resources

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Aws Agentic Ai AI skill do?

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 tool integration, credential management, agent discovery, governance workflows, and automated quality assessment. Essential when user mentions AgentCore, agent runtime, agent registry, agent evaluation, MCP gateway, deploy agent, register MCP server, discover agents, evaluate agent quality, agent credentials, or...

Why use Aws Agentic Ai on TypingMind?

Because you install it once and use it with any model. Aws Agentic Ai is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Aws Agentic Ai in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/zxkane/aws-skills/tree/main/plugins/aws-agentic-ai/skills/aws-agentic-ai. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Aws Agentic Ai?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Aws Agentic Ai?

As many as you like. As long as a model supports skills, you can use Aws Agentic Ai with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Aws Agentic Ai AI skill free?

Yes. It is published on GitHub by zxkane under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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