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Hcls Deploy Agent

  • 1 installs
  • 265 repo stars
  • Updated August 5, 2026
  • aws-samples/amazon-bedrock-agents-healthcare-lifesciences

HCLS Deploy Agent is a Claude Code skill that guides deploying a healthcare/life-sciences agent to Amazon Bedrock AgentCore with Gateway, Memory, Cognito auth, and Registry.

About

HCLS Deploy Agent is a deployment guide for putting a healthcare or life-sciences agent onto Amazon Bedrock AgentCore. It covers configuring the AgentCore Runtime, Gateway (tools exposed as MCP endpoints via Lambda targets), Memory, Cognito OAuth2 Identity, and the Registry. A developer uses it to run prerequisite CloudFormation, launch the agent container to ECR, verify it, and optionally register it for multi-agent discovery. It also lists the AWS MCP servers that assist during deployment.

  • Deploys HCLS agents to Amazon Bedrock AgentCore: Runtime, Gateway, Memory, Identity, Registry
  • Exposes tools as MCP endpoints via AgentCore Gateway (Lambda targets)
  • Sets up Cognito OAuth2 authentication and conversation-memory persistence

Hcls Deploy Agent by the numbers

  • 1 all-time installs (skills.sh)
  • Ranked #14,102 of 16,546 AI & Agent Building skills by installs in the Skillselion catalog
  • Data as of Aug 5, 2026 (Skillselion catalog sync)
At a glance

hcls-deploy-agent capabilities & compatibility

Capabilities
agent deployment · mcp gateway · auth setup
Works with
aws · docker
Use cases
devops · ci cd · orchestration
Pricing
Free
From the docs

What hcls-deploy-agent says it does

Gateway — Exposes tools as MCP endpoints (Lambda targets)
SKILL.md
Launch (builds container, pushes to ECR, deploys to Runtime)
SKILL.md
npx skills add https://github.com/aws-samples/amazon-bedrock-agents-healthcare-lifesciences --skill hcls-deploy-agent

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Listed on Skillselion
Installs1
repo stars265
Last updatedAugust 5, 2026
Repositoryaws-samples/amazon-bedrock-agents-healthcare-lifesciences

What it does

Deploy a healthcare/life-sciences agent to Bedrock AgentCore with Gateway MCP tools, Cognito auth, and memory.

Who is it for?

Developers deploying an HCLS agent to Bedrock AgentCore and exposing tools as MCP endpoints.

Skip if: Building agent logic from scratch; it assumes an agent to deploy already exists.

When should I use this skill?

A developer wants to deploy an HCLS agent, configure AgentCore Gateway/Runtime/Memory/Identity, or register in the Registry.

What you get

An HCLS agent deployed and verified on Bedrock AgentCore, optionally registered for multi-agent discovery.

  • Deployed AgentCore Runtime, Gateway, Memory, and Identity
  • Optional AgentCore Registry record

By the numbers

  • 5 AgentCore deployment components
  • 5-step deployment sequence

Files

SKILL.mdMarkdownGitHub ↗

Deploying an HCLS Agent

When to use this skill

  • Developer asks "how do I deploy this agent?"
  • Developer needs to configure AgentCore Gateway, Runtime, Memory, or Identity
  • Developer wants to expose tools as MCP endpoints
  • Developer wants to register an agent in the AgentCore Registry

Deployment Components

AgentCore Deployment
├── Runtime       — Hosts the agent container
├── Gateway       — Exposes tools as MCP endpoints (Lambda targets)
├── Memory        — Conversation persistence (semantic, summary, preferences)
├── Identity      — Cognito OAuth2 authentication
└── Registry      — Agent discovery for multi-agent workflows

Steps

1. Prerequisites

# Install AgentCore CLI
pip install bedrock-agentcore

# Configure
agentcore configure --entrypoint main.py \
  -rf agent/requirements.txt \
  -er <IAM_ROLE_ARN> \
  --name <agent-name>

2. Deploy Infrastructure

Run the prerequisite script (creates Lambda tools, Cognito, Gateway):

./scripts/prereq.sh

This deploys CloudFormation stacks for:

  • Lambda functions (tool handlers)
  • IAM roles (agent execution, Gateway invocation)
  • Cognito user pool (OAuth2 authentication)
  • AgentCore Gateway (MCP endpoint with tool targets)
  • AgentCore Memory (conversation persistence)

3. Deploy Agent Runtime

# Remove stale config
rm -f .agentcore.yaml

# Launch (builds container, pushes to ECR, deploys to Runtime)
agentcore launch

4. Verify

# Invoke the deployed agent
agentcore invoke '{"prompt": "Hello, can you help me?"}'

# Test Gateway tools independently
python tests/test_gateway.py --prompt "Test query"

# Test memory
python tests/test_memory.py load-conversation

5. Register in AgentCore Registry (optional)

For multi-agent discovery, register the agent:

import boto3
client = boto3.client('bedrock-agentcore')

client.create_registry_record(
    registryName="hcls-registry",
    recordName="<agent-name>",
    description="<rich description for semantic search>",
    descriptorType="MCP",  # or "A2A" for agent-to-agent
    descriptors={...}
)

Deployment Templates

TemplateWhat it deploys
agentcore_template/Backend: Runtime + Gateway + Memory + Streamlit UI
FASTFull-stack: React/Amplify + Cognito + AgentCore + CDK

AWS MCP Servers Used

When deploying, the following AWS MCP servers help:

  • agentcore-docs — API reference for Gateway/Runtime/Memory/Registry
  • aws-mcp — create IAM roles, manage CloudFormation stacks, configure S3
  • strands-docs — framework patterns for agent code

References

  • Deployment scripts: agentcore_template/scripts/
  • Full deployment example: agents_catalog/28-Research-agent-biomni-gateway-tools/scripts/prereq.sh
  • FAST template deployment: agents_catalog/35-Terminology-agent/

Related skills

FAQ

What components does an AgentCore deployment include?

Runtime (container host), Gateway (tools as MCP endpoints), Memory (conversation persistence), Identity (Cognito OAuth2), and Registry (agent discovery).

How is the agent launched?

agentcore launch builds the container, pushes to ECR, and deploys it to Runtime after prereq.sh provisions the infrastructure.

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