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.
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Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionaws-agentic-aiExecute the skills CLI command in your project's root directory to begin installation:
Fetches aws-agentic-ai from zxkane/aws-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate aws-agentic-ai. Access via /aws-agentic-ai in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
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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.
Always verify AWS facts using MCP tools (mcp__aws-mcp__* or mcp__*awsdocs*__*) before answering. The aws-mcp-setup dependency is auto-loaded — if MCP tools are unavailable, guide the user through that skill's setup flow.
Use this skill when you need to:
| 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 |
MANDATORY - READ DETAILED DOCUMENTATION: See services/gateway/README.md for complete Gateway setup guide including deployment strategies, troubleshooting, and IAM configuration.
Quick Workflow:
Note: Credential provider is only needed for API key authentication. Lambda targets use IAM roles, and MCP servers use OAuth.
MANDATORY - READ DETAILED DOCUMENTATION: See cross-service/credential-management.md for unified credential management patterns across all services.
Quick Workflow:
MANDATORY - READ DETAILED DOCUMENTATION: See services/observability/README.md for comprehensive monitoring setup.
Quick Workflow:
For detailed documentation on each AgentCore service, see the following resources:
services/gateway/README.mdservices/gateway/deployment-strategies.mdservices/gateway/troubleshooting-guide.mdEach service has comprehensive documentation in its respective directory:
services/runtime/README.mdservices/memory/README.mdservices/identity/README.mdservices/code-interpreter/README.mdservices/browser/README.mdservices/observability/README.mdFor patterns and best practices that span multiple AgentCore services:
cross-service/credential-management.md - Unified credential patterns, security practices, rotation proceduresPrerequisites
Time Estimate
15-45 minutes depending on use case complexity
Steps
Common Pitfalls
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💡 Pro Tips
✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid when
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
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I recommend aws-agentic-ai for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
aws-agentic-ai has been reliable in day-to-day use. Documentation quality is above average for community skills.
aws-agentic-ai reduced setup friction for our internal harness; good balance of opinion and flexibility.
aws-agentic-ai reduced setup friction for our internal harness; good balance of opinion and flexibility.
aws-agentic-ai fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
I recommend aws-agentic-ai for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
aws-agentic-ai fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
aws-agentic-ai is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Useful defaults in aws-agentic-ai — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
aws-agentic-ai fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
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