Claude-skill-registry deploy-agentcore

Deploy Python agents to AWS Bedrock AgentCore. Use when deploying agents to AWS, setting up serverless agent hosting, configuring AgentCore components (Runtime, Gateway, Memory, Identity, Policy), or troubleshooting deployment errors.

install
source · Clone the upstream repo
git clone https://github.com/majiayu000/claude-skill-registry
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/majiayu000/claude-skill-registry "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/data/deploy-agentcore" ~/.claude/skills/majiayu000-claude-skill-registry-deploy-agentcore && rm -rf "$T"
manifest: skills/data/deploy-agentcore/SKILL.md
source content

<essential_principles> AWS Bedrock AgentCore is a serverless platform for AI agents at scale.

Architecture

AgentCore has 6 modular components:

  • Runtime - Serverless hosting (direct_code_deploy or container)
  • Gateway - Tool access via MCP (Lambda, OpenAPI, Smithy targets)
  • Memory - STM (session) and LTM (persistent) storage
  • Identity - Auth via IAM, Cognito, AWS JWT, external OAuth
  • Observability - CloudWatch + OpenTelemetry tracing
  • Policy - Cedar-based governance and authorization

Entry Point Pattern

All agents use

BedrockAgentCoreApp
with
@app.entrypoint
decorator:

from bedrock_agentcore import BedrockAgentCoreApp

app = BedrockAgentCoreApp()

@app.entrypoint
def invoke(payload: dict) -> dict:
    prompt = payload.get("prompt", "")
    result = your_agent_logic(prompt)
    return {"result": result}

if __name__ == "__main__":
    app.run()

Key CLI Commands

All commands:

uv run agentcore [command]

Runtime: configure, deploy, invoke, status, destroy, stop-session Gateway: gateway create-mcp-gateway, gateway create-mcp-gateway-target Memory: memory create, memory list, memory status Identity: identity setup-cognito, identity setup-aws-jwt Policy: policy create-policy-engine, policy create-policy

See references/cli-reference.md for full command list.

Rules

  • Agent names: underscores only (
    my_agent
    not
    my-agent
    )
  • Never hardcode API keys - use Secrets Manager
  • Windows: prefix with
    PYTHONIOENCODING=utf-8
  • Memory mode order:
    STM_AND_LTM
    (not LTM_AND_STM) </essential_principles>
<intake> What would you like to do?
  1. Deploy a new agent
  2. Update existing deployment
  3. Add Google OAuth
  4. Create chat UI
  5. Set up Gateway (MCP tools)
  6. Configure Memory
  7. Set up Identity/Auth
  8. View logs/observability
  9. Troubleshoot errors
  10. Something else

Wait for response before proceeding. </intake>

<routing> | Response | Workflow | |----------|----------| | 1, "deploy", "new" | workflows/deploy-agent.md | | 2, "update", "redeploy" | workflows/update-deployment.md | | 3, "oauth", "google" | workflows/add-oauth.md | | 4, "ui", "chat", "streamlit" | workflows/create-chat-ui.md | | 5, "gateway", "mcp", "tools" | workflows/setup-gateway.md | | 6, "memory", "stm", "ltm" | workflows/setup-memory.md | | 7, "identity", "auth", "cognito", "jwt" | workflows/setup-identity.md | | 8, "logs", "observability", "cloudwatch" | workflows/view-logs.md | | 9, "error", "troubleshoot", "fix" | workflows/troubleshoot.md | | 10, other | Clarify, then select |

After reading the workflow, follow it exactly. </routing>

<reference_index> All domain knowledge in

references/
:

  • architecture.md - All AgentCore components explained
  • cli-reference.md - Complete CLI command reference
  • prerequisites.md - AWS setup, Python, uv requirements
  • memory-modes.md - Memory configuration details
  • common-errors.md - Error messages and fixes
  • iam-policies.md - IAM role configuration </reference_index>

<workflows_index>

WorkflowPurpose
deploy-agent.mdDeploy Python agent to AgentCore
update-deployment.mdRedeploy with code changes
add-oauth.mdAdd Google OAuth for cloud environment
create-chat-ui.mdCreate Streamlit chat interface
setup-gateway.mdCreate MCP gateway with targets
setup-memory.mdConfigure memory modes
setup-identity.mdSet up auth (Cognito, JWT, OAuth)
view-logs.mdAccess CloudWatch logs and metrics
troubleshoot.mdFix common deployment errors
</workflows_index>

<templates_index>

TemplatePurpose
entry_claude_sdk.pyEntry point for Claude SDK agents
entry_langchain.pyEntry point for LangChain agents
entry_custom.pyEntry point for custom Python agents
entry_minimal.pyBare minimum entry point
policy_minimal.jsonIAM policy for Secrets Manager only
policy_oauth.jsonIAM policy for OAuth (Secrets + S3)
policy_full.jsonIAM policy with all common permissions
chat_ui.pyStreamlit chat interface
</templates_index>

<success_criteria> Deployment successful when:

  • uv run agentcore deploy
    completes without errors
  • uv run agentcore invoke
    returns expected response
  • Agent handles sessions correctly
  • External API keys work via Secrets Manager </success_criteria>