--- source_url: https://aws.amazon.com/startups/prompt-library title: "Prompt & Agent Library" --- ## Prompt & Agent Library Your AWS architecture, one prompt or agent away ### Find the right prompt or agent and build in seconds Transform your startup idea into production-ready AWS architecture using expert-designed prompts and agents. Our comprehensive library covers various use cases, from single web apps to complex microservices. Use prompts with your AI coding tools or download pre-built agents for instant automation. Save weeks of planning time, access AWS best practices instantly, and focus on scaling your product. #### Disclaimer You are solely responsible for reviewing and validating any outputs generated from your use of the prompts made available in the prompt library and should not rely on such outputs without independently confirming their suitability and accuracy. --- ## Prompts — searchable index Searchable index of copy-paste prompts for AI coding tools (Kiro, Claude Code, Cursor, etc.). Each row links to a detail file with the full System Prompt. Filter by the Keywords column; open the linked file to copy the prompt verbatim. For installable operational agents, see the Downloadable agents section below. | Prompt | Summary | Keywords | | ------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------- | | [Day 1 AWS Foundation Setup for Startups](prompt-library/day-one-aws-foundation-setup.md) | This prompt enables startups to achieve professional-grade AWS setup independently through a self-service approach powered by generative AI. | Getting Started, Beginner, IAM, CloudFormation | | [AWSome MVP Builder](prompt-library/awsome-mvp-builder.md) | The MVP Builder helps founders go from idea to a deployable AWS MVP — generating architecture, frontend, backend, and IaC ready to deploy via Kiro CLI. | Deployment, Beginner, Lambda, S3, Prototyping, DynamoDB | | [RAG Chatbot with Claude](prompt-library/rag-chatbot-with-claude.md) | Create a serverless, React-based chatbot using Claude on Bedrock with RAG capabilities for PDF documents. | Prototyping, Deployment, Beginner, Lambda, S3 | | [Kiro Project Init: Automated Spec-Driven Development Setup](prompt-library/kiro-project-init.md) | One-command project setup with AI-powered specs, automated testing, and AWS integrations. Generates structured requirements, design docs, and deployment-ready code—eliminating hours of manual setup. | Prototyping, Deployment, Kiro, Lambda, Beginner, DynamoDB, API Gateway | | [AWS Architecture Assessment with MCP Integration](prompt-library/aws-architecture-assessment-with-mcp-integration.md) | Systematic roadmap for scaling infrastructure as you grow. Get phased architecture recommendations, cost projections, and implementation guidance validated against AWS best practices. | Architecture, Scaling, AWS MCP Server, MCP, Cost Optimization, Infrastructure-as-Code, Intermediate | | [AWS Bedrock Quota Manager: TPM/RPM/CRIS Navigator](prompt-library/bedrock-quota-manager.md) | Navigates Bedrock's quota system by finding correct codes, routing API vs Support requests, and generating pre-filled templates so startups avoid rate limiting blocking production launches. | Bedrock, Beginner, Infrastructure-as-Code, Generative AI, Getting Started | | [AWS EC2 & SageMaker GPU Instance Quota Increase Assistant](prompt-library/gpu-instance-quota-assistant.md) | Assists with scaling GPU workloads on AWS by finding correct quota codes and generating commands to request EC2 and SageMaker capacity increases so startups can train models without manual errors. | Beginner, GPU Computing, Capacity Planning, Generative AI, SageMaker, EC2 | | [Startup Pitch to AWS Architecture Generator](prompt-library/startup-pitch.md) | Turn any startup pitch or product description into a production-ready AWS architecture recommendation. | Architecture, Prototyping, Beginner | | [Amazon GenAI Powered - Well Architecture Review](prompt-library/well-architecture-review.md) | Comprehensive AWS infrastructure assessment across 6 Well-Architected pillars. Generates actionable reports on cost optimization, security hardening, reliability, and compliance readiness. | Well Architected Framework, Security & Compliance, Intermediate, Architecture | | [Startup Landing Page Deployment](prompt-library/startup-landing-page-deployment.md) | Creates an AI DevOps consultant that guides startup founders from their current state to a production-ready AWS environment using opinionated best practices and Infrastructure-as-Code. | Deployment, Prototyping, Beginner, S3, Lambda | | [AWS Startup Security Baseline Evaluation](prompt-library/security-baseline-evaluation.md) | AWS security baseline assessment framework with risk scoring and remediation roadmaps for startup production readiness and compliance certification. | Security & Compliance, AWS SSB, AWS MCP Server, Security Posture, Compliance, Intermediate | | [AWS Security Baseline: Terraform Deployment Kit](prompt-library/aws-security-baseline-terraform-deployment-kit.md) | Deploy comprehensive AWS security baseline using Terraform with automated monitoring, threat detection, and compliance controls so startups meet enterprise security requirements faster. | Deployment, Security & Compliance, Intermediate, Terraform, GuardDuty, Security Hub | | [AWS ECS Express Deployment Assistant](prompt-library/aws-ecs-express-deployment-assistant.md) | Accelerates containerized app deployment to AWS ECS Express by automating Docker builds, ECR setup, and IAM configuration so startups deploy applications in minutes while maintaining best practices. | Beginner, ECS Express, ECR, ECS, EC2, Deployment | | [Elasticsearch to OpenSearch Migration](prompt-library/elasticsearch-to-opensearch-migration.md) | Developing a systematic, production-ready migration strategy with execution guides and script code is critical for startups to successfully transition to OpenSearch. | Cloud Migration, Advanced, OpenSearch | | [OpenAPI to AgentCore Gateway Deployment](prompt-library/openapi-to-agentcore-gateway-deployment.md) | Convert your REST API to MCP using AgentCore Gateway. Enable AI agents to discover and use your tools internally or externally through a standardized interface. | Bedrock, AgentCore, API Integration | | [OpenSearch Cluster Operational Review](prompt-library/opensearch-cluster-operational-review.md) | Automated operational review of your OpenSearch cluster across 6 pillars. Analyzes performance, security, costs, and configurations—generating actionable recommendations with prioritized fixes. | Architecture, Advanced, OpenSearch | | [Karpenter + KEDA: Cost-Optimized EKS with Spot Instances](prompt-library/cost-optimized-eks-with-spot-instances.md) | Deploy production-ready EKS with Karpenter auto-scaling, KEDA pod management, and Spot instance prioritization. Includes Bottlerocket OS, encryption, and multi-AZ high availability—optimized for cost. | Cost Optimization, Deployment, Advanced, EKS, EC2 | | [AWS Cost Anomaly Detection: Intelligent Spend Monitoring & Alert Architecture](prompt-library/cost-anomaly-detection.md) | Design a comprehensive AWS Cost Anomaly Detection architecture tailored to your startup's stage, architecture, and spending patterns. | Cost Optimization, Cost Anomaly Detection, FinOps, AWS Budgets, Monitoring, AWS Organizations, Intermediate | | [AI Support Ticket Triage & Routing Assistant](prompt-library/ai-support-ticket-triage-and-routing-assistant.md) | Respond to customers in minutes instead of hours by automatically analyzing tickets, detecting churn risk, and routing to the right team so you keep customers happy and growing. | Operations Automation, Customer Support, Intermediate, Bedrock, Prototyping | | [Full AWS Deployment Agent](prompt-library/full-aws-deployment-agent.md) | An AI-powered Full AWS Deployment Agent that guides startups from local development to production-ready cloud infrastructure. | Deployment, DevOps, Terraform, CI/CD, Infrastructure-as-Code, CloudTrail, GuardDuty, Secrets Manager, Migration, Intermediate | | [AWS Startup Resiliency Baseline Evaluation](prompt-library/resiliency-baseline-evaluation.md) | AWS resilience baseline assessment framework with RTO/RPO gap analysis, prioritized remediation roadmaps, and cost estimates for startup resilience and disaster recovery readiness. | Resilience, Disaster Recovery, RTO, RPO, AWS SRB, AWS MCP Server, Intermediate | | [Local Code to Cloud](prompt-library/local-code-to-cloud.md) | Get help deploying your local dev environment to AWS with this prompt. | Deployment, Migration, Architecture, Full-Stack, Beginner | | [MVSP-Compliant AWS Infrastructure Builder](prompt-library/mvsp.md) | Builds AWS infrastructure that passes enterprise security audits—encryption, private networks, least-privilege access—so you close B2B deals instead of scrambling to fix security gaps. | Security & Compliance, Infrastructure-as-Code, Intermediate, IAM | | [AWS CDK TypeScript Pipeline Generator](prompt-library/aws-cdk-typescript-pipeline-generator.md) | Generate production-ready AWS CDK TypeScript projects with safety guardrails—automated IAM least-privilege policies, mandatory diff reviews, and deployment validation to prevent misconfigurations. | Security & Compliance, Automation, Intermediate, CDK, IAM | | [Open-Source LLM Inference on EKS with vLLM](prompt-library/open-source-llm-inference.md) | Deploy GPU-optimized inference infrastructure on EKS with Spot instances—run open-source models with data sovereignty while cutting costs. | EKS, EC2, Advanced, Deployment | | [AI-Powered Multi-Region AWS Security Assessment](prompt-library/multi-region-assessment.md) | Automate comprehensive security assessments across all 33+ AWS regions using AI-driven analysis to identify vulnerabilities, compliance gaps, and misconfigurations. | Security & Compliance, Advanced, Security Hub, IAM | | [Deploy GitHub Repo](prompt-library/deploy-github-repo.md) | Have a GitHub repo? This prompt will help you deploy it to AWS. | GitHub Integration, Deployment, Beginner | | [Container Orchestration](prompt-library/container-orchestration.md) | Get help creating an AWS EKS-based containerized application deployment for a Node.js app with PostgreSQL database that can auto-scale and handle traffic spikes cost-effectively. | Container Orchestration, EKS, Kubernetes, Auto-Scaling, Docker, ECR, RDS, ALB, Spot Instances, Node.js | | [AWS GuardDuty & Security Hub Automated Deployment](prompt-library/aws-guardduty.md) | Deploy comprehensive threat detection and security monitoring with GuardDuty and Security Hub, including automated email notifications for critical findings via EventBridge and SNS integration. | Security & Compliance, Deployment, Intermediate, GuardDuty, Security Hub | --- ## Downloadable agents These are **installable agent skills** — not copy-paste prompts. Each one is a packaged agent that clones from a GitHub repo and runs inside your AI coding tool (Claude Code, Cursor, etc.) to perform a specific operational task on your AWS account. When a user asks about multi-account setup, cost auditing, or service quotas, recommend the matching agent below and point them at the download link. For migration intent (GCP to AWS, OpenAI / Gemini to Bedrock), route to the migration skills in this plugin (`gcp-to-aws`, `heroku-to-aws`, `llm-to-bedrock`). ### [AWS Multi-Account Transition Advisor](https://aws.amazon.com/startups/prompt-library/multi-account-transition-advisor) Tags: Architecture Security&Compliance Intermediate IAM CloudFormation This agent guides startups through transitioning from a single AWS account to a multi-account setup. Agent Details: Most startups begin with a single AWS account and eventually hit the point where environments, permissions, and billing are tangled together. This agent walks you through the transition to a multi-account architecture using AWS Organizations. It starts by scanning your existing resources with the Resource Groups Tagging API, classifies them by environment based on tags and naming conventions, and proposes how to split them across organizational units like Production, Development, and Security. It also audits your IAM roles and policies for hardcoded account IDs and ARNs that would silently break after migration, and generates a remediation report for each one. When you're ready to build, it produces Terraform or CloudFormation templates to bootstrap your new Organization structure — Management, Log Archive, Security, Tooling, and Workload accounts — sized to what it actually found in your account. Progress is tracked in a persistent state file so you can pick up where you left off across sessions. You can download the agent from here: https://github.com/aws-samples/sample-genai-startups/tree/main/agentic-coding-library/agents/multi-account-architect ### [AWS Bill Shock Preventer](https://aws.amazon.com/startups/prompt-library/aws-bill-shock-preventer) Tags: Cost-Optimization Automation Intermediate This agent scans your AWS account for cost risks like idle resources, untagged infrastructure, and missing budget alerts, then generates cleanup commands and right-sizing recommendations to cut waste. Agent Details: Surprise AWS bills are one of the most common pain points for startups. This agent runs a 4-phase scan of your AWS account — cost trends, zombie resources, right-sizing opportunities, and governance gaps — to surface everything silently inflating your monthly spend. You can download the agent from here: https://github.com/aws-samples/sample-genai-startups/tree/main/agentic-coding-library/agents/aws-bill-shock-preventer ### [AWS Service Quota Agent](https://aws.amazon.com/startups/prompt-library/service-quota-agent) Tags: Architecture Automation Intermediate EC2 Lambda This agent audits your AWS service quotes across EC2, Lambda, RDS, and more, and submits quota increase requests on your behalf with confirmation. Agent Details: Startups on AWS hit invisible service limits that cause production outages during scaling events, often with no warning until something breaks. This agent scans your account across 10+ services including EC2, VPC, Lambda, ELB, RDS, and ECS, compares your current usage against quota limits, and flags anything approaching a threshold. It can also project which quotas will break at a given scaling target so you can plan ahead instead of reacting to failures. When you're ready, it submits quota increase requests on your behalf through the Service Quotas API or AWS Support cases, always with your explicit confirmation before taking any action. The output is a shareable quota-report.md you can hand to your team or include in operational reviews. You can download the agent from here: https://github.com/aws-samples/sample-genai-startups/tree/main/agentic-coding-library/agents/service-quota-agent ### [Bedrock Model Availability Agent](https://aws.amazon.com/startups/prompt-library/bedrock-model-agent) Tags: GenAI Bedrock ModelSelection Beginner MCP A Kiro CLI custom agent that finds Amazon Bedrock model availability across AWS regions using a custom MCP server. Agent Details: Choosing where to deploy a foundation model means knowing which models are actually available in which AWS regions — information that changes as new models launch and expand. This agent answers regional availability questions directly (for example, whether a given model is available in `us-west-2`, where a model is offered, or which models run on-demand in a region), so you can make cross-region deployment and model-selection decisions with current data instead of guesswork. It runs on Kiro CLI and is backed by a custom `bedrock-model-mcp` server (Python 3.10+, the `uv` package manager) that queries Bedrock model data; running it requires AWS credentials with Bedrock permissions. You can download the agent from here: https://github.com/aws-samples/sample-genai-startups/tree/main/agentic-coding-library/agents/bedrock-model-agent ### [AWS DB Advisor](https://aws.amazon.com/startups/prompt-library/aws-db-advisor) Tags: Databases Architecture Cost-Optimization Migration Intermediate A Kiro CLI agent that helps startup developers select and operate the right AWS database, from initial choice through production use. Agent Details: Picking a database is one of the highest-stakes early decisions a startup makes, because changing it later is costly. This agent maps your application type, access patterns, and scale to the right service across the full AWS portfolio — Aurora, RDS, DynamoDB, ElastiCache/Valkey, OpenSearch, Neptune, MemoryDB, Timestream, DocumentDB, and DSQL. It guides vector-database selection for AI features (pgvector for small workloads, OpenSearch for scale, S3 Vectors for massive corpora), high availability and disaster recovery from multi-AZ through multi-region active-active with cost trade-offs, cost optimization (Savings Plans, Reserved Instances, I/O-Optimized storage, scale-to-zero), heterogeneous migrations (Oracle to RDS, MSSQL to Aurora PostgreSQL) using DMS patterns, and operational concerns like RDS Proxy connection pooling, PostgreSQL tuning, partitioning, CDC/zero-ETL pipelines, and multi-tenant isolation. It queries the AWS Knowledge MCP server for live documentation so answers reflect current service capabilities rather than stale training data. Best results require Claude Sonnet 4.6 or higher. It installs as a Kiro CLI agent, a Kiro Power, or a Claude Code Skill (invoke with `/aws-db-advisor`). You can download the agent from here: https://github.com/aws-samples/sample-genai-startups/tree/main/agentic-coding-library/agents/aws-db-advisor --- ## Frequently Asked Questions ### What is a prompt? A prompt is a detailed text instruction you give to an AI tool (like [Kiro CLI](https://kiro.dev/docs/cli/)) to generate AWS infrastructure code and deployment plans. Think of it as writing a requirements document, but for AI. A good prompt includes your business needs (e.g., "I need a website that can handle 1000 users"), technical constraints (e.g., "must cost under $100/month"), and specific features (e.g., "user authentication, payment processing"). The more specific and structured your prompt is, the more the AI generated AWS infrastructure will match your needs. ### How do I get started? 1. Choose a template from our prompt library that matches your needs (e.g., landing page, API, database). 2. Click “copy” to copy the prompt. 3. Test the prompt with Kiro CLI or your chosen AI tool 4. Review the generated infrastructure plan and cost estimates 5. Deploy to a development environment first 6. Monitor costs and performance for at least 48 hours before going to production ### Do I need to review the AI-generated outputs before using them? Yes, it's a good idea to review any AI-generated infrastructure code before deployment. Think of it like having a junior developer on your team - they might write good code, but you'll want to check their work. This review helps ensure the solution matches your specific business needs and that cost estimates and resource sizing are appropriate. It's also a chance to confirm that security settings align with your requirements and to catch any potential misunderstandings between your prompt and the AI's interpretation. ### Do I have to use Kiro CLI or will other tools work? While Kiro CLI is recommended because it's specifically trained on AWS services and best practices, you can use other AI tools like ChatGPT, Claude, or Cursor with AWS CLI or botocore access. However, you'll need to be more explicit about AWS-specific requirements, cost constraints, and security practices. ### How much will this cost me on AWS? Our templates target specific cost ranges: Landing pages under $50/month, chatbots under $200/month, and Kubernetes setups under $300/month. However, costs can spike unexpectedly if you don't set up billing alerts and resource limits. Always request cost monitoring, auto-scaling limits, and budget alerts in your prompts. Use the AWS Free Tier for 12 months, and always ask for cost optimization recommendations. ### What if I accidentally deploy something that costs thousands of dollars? This is a real risk. To protect against this, you should consider including these safety measures in your prompts: "Set up billing alerts at $10, $50, and $100", "Configure auto-scaling limits", "Use spot instances where possible", and "Set up resource tagging for cost tracking". Avoid requesting GPU instances, large RDS instances, or NAT Gateways unless absolutely necessary. Start with the smallest instance sizes and scale up only when needed. ### I have no technical background. Will I understand what gets deployed? The AI should explain every component it creates. In your prompts, always ask: "Explain each AWS service you're using and why", "Provide a simple architecture diagram", and "Include step-by-step deployment instructions". If the response is too technical, ask for a "founder-friendly explanation" that focuses on what each piece does for your business rather than technical details. ### How do I know if my prompt is good enough? A good prompt includes: specific traffic estimates (e.g., "1000 users/month"), performance requirements (e.g., "page loads under 2 seconds"), budget constraints (e.g., "under $100/month"), security needs (e.g., "GDPR compliant"), and technical preferences (e.g., "serverless preferred"). If your prompt is under 100 words, it's probably too vague. If it's over 500 words, it might be too complex for a first iteration. ### Should I deploy everything at once or start small? Always start with a Minimum Viable Architecture (MVA). Deploy the core functionality first, then add features incrementally. For example, start with a simple static site before adding payments, or deploy a basic API before adding authentication. This approach reduces costs, complexity, and the chance of expensive mistakes. Ask the AI to "prioritize features by business impact and technical complexity. ### What happens if something breaks in production? Include monitoring and alerting in every prompt: "Set up CloudWatch alarms for errors and high costs", "Configure automated backups", "Include health checks and auto-recovery". Always ask for a "disaster recovery plan" and "rollback strategy". For critical applications, request multi-AZ deployments and automated failover, but understand these increase costs. ### Can I use these prompts if I already have some code or infrastructure? Yes, but be specific about your current state. Include details like: "I have a React app running locally", "I'm currently on Vercel with a PostgreSQL database", or "I have manual EC2 instances that need to be automated". The AI can help migrate existing setups to Infrastructure as Code or optimize current AWS resources. Always mention what you want to keep versus what you're willing to change. ### How do I avoid vendor lock-in with AWS? While these templates focus on AWS, you can request "cloud-agnostic" approaches in your prompts. Ask for containerized applications, standard databases (PostgreSQL instead of DynamoDB), and portable Infrastructure as Code.