Prompt Library For Startups

AWSOfficialPublished by AWS

Skill
Tool integration
Cloud and DevOps
Apache-2.0
Low risk

From the author

AWS-curated copy-paste prompts for AI coding agents (MVP scaffolding, RAG chatbot with Claude on Bedrock, security baseline evaluation, cost anomaly detection, GPU quota requests, EKS deployment, Well-Architected review, etc.) plus downloadable installable agents (Multi-Account Transition Advisor, Bill Shock Preventer, Service Quota Agent, Bedrock Model Availability Agent, AWS DB Advisor). Use when the user asks for a prompt to do X on AWS, wants an installable agent for multi-account / cost monitoring / quota management / Bedrock model availability / database selection, or asks how to use AWS prompts. For migration intent (GCP to AWS, Azure to AWS, OpenAI/Gemini to Bedrock), route to the migration skills in this plugin (`gcp-to-aws`, `azure-to-aws`, `heroku-to-aws`, `llm-to-bedrock`). Do not use for: factual AWS Activate / programs / credits questions, learn articles, sample architectures, or for prompts that are not in the bundled `references/prompt-library/` tree.

— the description by AWS

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

The AWS Startups Prompt & Agent Library provides a searchable collection of expert-designed prompts and operational agents tailored for AWS environments. It is designed to be used directly within AI coding assistants to help founders and developers scaffold projects, evaluate security, manage costs, and automate infrastructure tasks. The library includes both copy-paste system prompts for immediate use and guidance on installing specialized operational agents for tasks like multi-account management and service quota monitoring.

What it does

Provides a searchable index of AWS-curated prompts for AI coding tools.
Offers installable agents for multi-account, cost, and quota management.
Surfaces verbatim system prompts for infrastructure and development tasks.
Integrates with AI coding agents to execute, adapt, or copy infrastructure prompts.
Provides guidance on AWS foundation setup and security baselines.
Routes complex migration requests to specialized sibling skills.

When to use it

When you need a proven prompt to scaffold an AWS-based MVP or RAG chatbot.
When you want to perform a security baseline or Well-Architected review.
When you need to manage AWS service quotas or detect cost anomalies.
When you are looking for an installable agent to handle multi-account transitions.
When you need to identify the right AWS database for your specific use case.

Advantages

Reduces planning time by using AWS-curated best practices.
Ensures consistency by using engineered system prompts.
Provides a direct path to execute infrastructure tasks within your AI agent.
Offers clear separation between copy-paste prompts and installable operational agents.
Maintains a structured, searchable library for rapid development.

Usage examples

“Give me a prompt for an MVP.”: The agent will reference the awsome-mvp-builder.md file and offer to run, adapt, or copy the prompt for your project.
“How do I prevent bill shock on AWS?”: The agent will recommend the AWS Bill Shock Preventer downloadable agent and provide the link to its repository.
“I need to set up a RAG chatbot on Bedrock.”: The agent will surface the rag-chatbot-with-claude.md prompt and offer to execute it against your current setup.
“How do I get a GPU instance quota raised?”: The agent will provide the gpu-instance-quota-assistant.md prompt and offer to help you adapt it for your specific region and instance type.

Install

Download the skill as a ZIP:
https://autogineer.com/agent-skills/aws/prompt-library-for-startups/download

Unzip it into the cross-client skills folder of your project:

mkdir -p .agents/skills && unzip prompt-library-for-startups.zip -d .agents/skills

Documentation by AWS

AWS Startups Prompt & Agent Library

Searchable index of AWS-curated prompts for AI coding tools (Kiro, Claude Code, Cursor, etc.) plus downloadable installable agents. Content is verbatim from aws.amazon.com/startups/prompt-library.

Last updated: 2026-09-24

Where to start

Open references/prompt-library.md — it's the index. Filter by the Keywords column (e.g., RAG, Security & Compliance, Cost Optimization, EKS, Beginner, Bedrock), then open the linked detail file under references/prompt-library/<slug>.md. Each detail file carries the full verbatim System Prompt plus a How to use? section where available.

The index has three sections:

Prompts — searchable index — copy-paste prompts.
Downloadable agents — installable agents that clone from a GitHub repo.
Frequently Asked Questions — guidance on writing a good prompt, costs, safety, no-technical-background usage, monitoring.

Handing over a prompt — match the host context

You ARE already inside an AI coding agent (Claude Code / Kiro / Cursor / etc.). Don't tell the user to "paste this into your AI tool" — you ARE the AI tool.

When the user asks for a prompt:

Read the index in references/prompt-library.md, filter by keyword, identify the matching slug.
Open references/prompt-library/<slug>.md and read the full System Prompt.
Surface the prompt to the user as a reference from the AWS Startups Prompt Library, then offer them three paths:
"Here's the AWS Startups reference prompt for <task>. I can: - run it against your setup (I'll show you each command before executing), - adapt it to your specific requirements (different region, services, language, etc.), or - you can copy it as a starting point.
How would you like to proceed?"
Don't assume intent. Wait for the user's call (execute / adapt / copy) before acting.

Downloadable agents — different surface from prompts

The Downloadable agents section of references/prompt-library.md lists installable agents, not copy-paste prompts:

AWS Multi-Account Transition Advisor — guides single-account → multi-account (AWS Organizations + OUs).
AWS Bill Shock Preventer — proactive cost-spike detection and alerting.
AWS Service Quota Agent — auditing and requesting quota increases.
Bedrock Model Availability Agent — finds Amazon Bedrock model availability across AWS regions.
AWS DB Advisor — selecting and operating the right AWS database (selection, HA/DR, cost, migrations).

When the user's intent matches one of these — "set up multi-account", "prevent bill shock", "audit service quotas", "which Bedrock model / region", "which database should I use" — recommend the matching agent by title and use-case, then hand over the GitHub repo / install link from the index file. Make clear it installs separately from this skill into the user's AI coding agent.

For migration intent, route to the matching migration skill in this plugin: gcp-to-aws ("GCP to AWS", move off Google Cloud), heroku-to-aws ("migrate off Heroku"), llm-to-bedrock ("move off OpenAI/Gemini to Bedrock" — SDK rewrite), or agent-advisor (agent runtime / architecture on AWS).

Routing hints — common queries → which entry

Query	Entry
"Give me a prompt for an MVP"	`awsome-mvp-builder.md`
"Prompt for a RAG chatbot on Bedrock"	`rag-chatbot-with-claude.md`
"How do I set up AWS for the first time?"	`day-one-aws-foundation-setup.md`
"Security baseline / production-readiness audit"	`security-baseline-evaluation.md`, `aws-security-baseline-terraform-deployment-kit.md`
"How do I get a GPU instance quota raised?"	`gpu-instance-quota-assistant.md`
"Bedrock model quota / TPM / RPM"	`bedrock-quota-manager.md`
"Cost anomaly / spend monitoring"	`cost-anomaly-detection.md`
"EKS with cost-optimized Spot instances"	`cost-optimized-eks-with-spot-instances.md`
"Open-source LLM inference"	`open-source-llm-inference.md`
"Well-Architected Review"	`well-architecture-review.md`
"Multi-region security assessment"	`multi-region-assessment.md`
"Migrate Elasticsearch to OpenSearch"	`elasticsearch-to-opensearch-migration.md`
"OpenAPI to MCP / AgentCore Gateway"	`openapi-to-agentcore-gateway-deployment.md`
"Deploy a GitHub repo to AWS"	`deploy-github-repo.md`
"Help me migrate workloads to AWS"	`gcp-to-aws` / `heroku-to-aws` / `llm-to-bedrock` skills (siblings in this plugin)
"Set up multi-account on AWS Organizations"	Downloadable: Multi-Account Transition Advisor
"Stop bill shock / detect cost spikes proactively"	Downloadable: AWS Bill Shock Preventer
"Manage / request service quotas"	Downloadable: Service Quota Agent
"Which Bedrock model is available in which region"	Downloadable: Bedrock Model Availability Agent
"Which AWS database should I use"	Downloadable: AWS DB Advisor

For anything else, filter references/prompt-library.md by keyword.

Companion skills — when to defer

This skill is prompts and installable agents only. Two sibling skills cover adjacent jobs:

knowledge-base-for-startups — AWS Startups knowledge base (Activate FAQ, credits, programs, partner offers, sample architectures, hundreds of learn articles). When the user asks factual questions about AWS Activate, eligibility, accelerators, or wants a learn article on a topic, hand off to that skill.
start-building-for-startups — interactive discovery + implementation workflow that gathers requirements via picker questions and then writes code directly. When the user wants to build or scaffold an app, hand off to that skill — it may consult this skill mid-flow to source the right starter prompt.

Boundary cases — invoke both. Example: "how do I start with RAG on Bedrock?" → this skill for the starter prompt (rag-chatbot-with-claude.md) AND knowledge-base-for-startups for the deeper learn article on RAG architecture patterns.

Answer style

MUST read the detail file before quoting a System Prompt. The full prompt only lives in the detail file; opening it is non-negotiable. Do not quote a System Prompt from memory or from the index alone.
MUST quote the System Prompt verbatim. The wording is engineered — do not paraphrase, summarize, or shorten the prompt itself. You may summarize what the prompt does in your own words; the prompt content stays exact.
MUST cite source_url. Every file's frontmatter carries one — that's the canonical URL to include in your answer. Never construct or guess a URL.
MUST NOT tell the user to paste the prompt elsewhere. You ARE the AI coding agent (see "Handing over a prompt" above). Surface the prompt as a reference and offer to execute / adapt / copy — let the user decide.
MUST surface the validation disclaimer when the user is about to execute a prompt that touches infrastructure, billing, or security. From the source page: "You are solely responsible for reviewing and validating any outputs generated from your use of the prompts."

Context loading rule

Open references/prompt-library.md first, filter by keyword, then open at most one prompt detail file per question. MUST NOT speculatively load multiple detail files. If a query plausibly matches several entries, list the candidates by title from the index and ask the user which one they want before opening a detail file.

Scope notes

This skill cannot:

Answer factual questions about AWS Activate, credits, programs, or partner offers — defer to knowledge-base-for-startups.
Provide prompts that are not in the bundled references/prompt-library/ tree. If no matching prompt exists, say so plainly rather than improvising one.
Install the downloadable agents on the user's behalf. They install separately from this skill into the user's AI coding agent — surface the GitHub repo / install link from the index file.
Override the source-page disclaimer. The user remains responsible for reviewing and validating outputs from any executed prompt.

Freshness check — surface after every answer

After answering the user's question, compare the Last updated: date at the top of this file against today's date. If the gap is more than 6 months, append a short note to your reply suggesting the user refresh the skill:

"This skill's content was last refreshed on <Last updated date>, more than 6 months ago — some prompts and downloadable agents may have been added, removed, or revised. To pull the latest content, run:
npx skills update prompt-library-for-startups — update the installed copy to the latest version
Then restart your AI agent so the new content is picked up."

Do not show this note when the skill is fresh (≤6 months). Do not repeat it within a single conversation; once is enough.

Files

ATTRIBUTION.mdAdded by AutoGineer935 B
LICENSELicence file added by AutoGineer9.9 KiB
NOTICELicence file added by AutoGineer67 B
SKILL.md11.4 KiB
references/prompt-library.md27.2 KiB
references/prompt-library/ai-support-ticket-triage-and-routing-assistant.md5.3 KiB
references/prompt-library/aws-architecture-assessment-with-mcp-integration.md22.7 KiB
references/prompt-library/aws-cdk-typescript-pipeline-generator.md3.1 KiB
references/prompt-library/aws-ecs-express-deployment-assistant.md4.5 KiB
references/prompt-library/aws-guardduty.md4.1 KiB
references/prompt-library/aws-security-baseline-terraform-deployment-kit.md3.8 KiB
references/prompt-library/awsome-mvp-builder.md5.3 KiB
references/prompt-library/bedrock-quota-manager.md24.2 KiB
references/prompt-library/container-orchestration.md3.7 KiB
references/prompt-library/cost-anomaly-detection.md21.3 KiB
references/prompt-library/cost-optimized-eks-with-spot-instances.md2.4 KiB
references/prompt-library/day-one-aws-foundation-setup.md5.4 KiB
references/prompt-library/deploy-github-repo.md3.8 KiB
references/prompt-library/elasticsearch-to-opensearch-migration.md13.4 KiB
references/prompt-library/full-aws-deployment-agent.md4.6 KiB
references/prompt-library/gpu-instance-quota-assistant.md16.5 KiB
references/prompt-library/kiro-project-init.md23.1 KiB
references/prompt-library/local-code-to-cloud.md3.4 KiB
references/prompt-library/multi-region-assessment.md49.3 KiB
references/prompt-library/mvsp.md12.9 KiB
references/prompt-library/open-source-llm-inference.md10.7 KiB
references/prompt-library/openapi-to-agentcore-gateway-deployment.md10.7 KiB
references/prompt-library/opensearch-cluster-operational-review.md3.1 KiB
references/prompt-library/rag-chatbot-with-claude.md2.9 KiB
references/prompt-library/resiliency-baseline-evaluation.md28.9 KiB
references/prompt-library/security-baseline-evaluation.md29.4 KiB
references/prompt-library/startup-landing-page-deployment.md4.0 KiB
references/prompt-library/startup-pitch.md2.6 KiB
references/prompt-library/well-architecture-review.md4.7 KiB

references/prompt-library.md

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	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	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	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	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	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	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	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	Turn any startup pitch or product description into a production-ready AWS architecture recommendation.	Architecture, Prototyping, Beginner
Amazon GenAI Powered - Well Architecture Review	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	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	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	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	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	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	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	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	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	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	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	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	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	Get help deploying your local dev environment to AWS with this prompt.	Deployment, Migration, Architecture, Full-Stack, Beginner
MVSP-Compliant AWS Infrastructure Builder	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	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	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	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	Have a GitHub repo? This prompt will help you deploy it to AWS.	GitHub Integration, Deployment, Beginner
Container Orchestration	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	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

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

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

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

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

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) 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?

Choose a template from our prompt library that matches your needs (e.g., landing page, API, database).
Click “copy” to copy the prompt.
Test the prompt with Kiro CLI or your chosen AI tool
Review the generated infrastructure plan and cost estimates
Deploy to a development environment first
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.

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs Node.js

Security

What our scanner noticed· Low risk

Instructions that use the network

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Published by

AWS

Publisher

AWS (Official)

Source

aws/agent-toolkit-for-aws · plugins/aws-startup-advisor/skills/prompt-library-for-startups · commit 0a218f8

Licence

Apache License 2.0 (redistribution allowed)

LICENSE (added by AutoGineer from LICENSE)
NOTICE (added by AutoGineer from NOTICE)
ATTRIBUTION.md (written by AutoGineer)

Synced

2026-11-09

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The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download prompt-library-for-startups.zip