launch-with-aws
Migrates vibe-coded web applications to AWS. Handles the full workflow from analysis through migration to deployment, producing deployable AWS Blocks infrastructure code. Supports full-stack apps built with vibe-coding platforms (Lovable, Bolt.new, Replit) and frontend web applications and websites: React, Vue, Angular, Next.js, Nuxt, Astro, SvelteKit, Gatsby, Vite, Svelte, Solid, Docusaurus, and others (static sites, SPAs, and SSR frameworks with static export). Triggers on: launch with AWS, launch on AWS, deploy to AWS, migrate to AWS, host my app on AWS, move my app to AWS, transfer my app to AWS. Activates when the user wants to migrate a vibe-coded app or frontend web app to AWS, even if they don't say 'migrate' explicitly.
- Installs
- 4.7K
- Last 24h
- +44
- Trending rank
- #3648
- Security
- Warn
Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/aws/agent-toolkit-for-aws --skill launch-with-aws- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads launch-with-aws automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
Security audits
Independent scans of the skill's instructions and bundled files.
SKILL.md
The instructions your agent reads when the skill is triggered.
Launch with AWS
Drives an AWS migration end-to-end using CLI scripts. Takes a user's web application, analyzes it, generates a migration plan with cost estimate, and delivers deployable AWS Blocks infrastructure code.
The AWS MCP server is recommended but is not required. This skill works standalone via its CLI scripts in any agent environment.
Script Invocation
All commands are run via:
python3 scripts/launch_with_aws.py <command> [args...]
where scripts/ is relative to this skill directory. The agent MUST set the working directory to the skill root before invoking commands.
Required files: launch_with_aws.py, launch_config.py, auth.py, auth_callback_server.py, launch_api_client.py, archive.py, service model. When loaded via MCP, fetch all and write to a temp directory preserving structure before invoking.
Each command outputs JSON to stdout on success, or exits non-zero with a JSON error on stderr.
Two global flags may be added to any command: --region <region> selects the region the service runs the migration in, and --aws-mcp-url <url> passes the URL of the AWS MCP server the agent is connected to. See step 2.
Dependencies: Python 3.10+ and boto3. The script checks both on startup and exits with a clear error if either is missing.
Supported Application Types
Full-stack apps built with vibe-coding platforms, and frontend web applications and websites (static sites, SPAs, and SSR frameworks with static export).
| Origin Platform | What it covers |
|---|---|
| Lovable | Lovable-generated full-stack apps (React + Supabase) |
| Bolt.new | Bolt.new-generated full-stack apps (React + Supabase) |
| Replit | Replit-hosted full-stack apps (React + Express.js + PostgreSQL) |
| Framework | Examples |
|---|---|
| React ecosystem | React, CRA, Vite + React, Gatsby, Docusaurus |
| Vue ecosystem | Vue, Nuxt (static export), VitePress |
| Angular | Angular |
| Svelte ecosystem | Svelte, SvelteKit (static export) |
| SSR with static export | Next.js, Nuxt, Astro, SvelteKit |
| Other modern frameworks | Astro, Solid, Preact, Lit, Eleventy |
| Vite (generic) | Any Vite-based app |
Other frameworks may also work. If the user's app doesn't match these, see Unsupported Application Handling below.
What Gets Migrated vs. What Stays
Lovable / Bolt.new apps (Supabase-backed):
| Component | What happens |
|---|---|
| Frontend & hosting | Migrated to AWS (S3 + CloudFront + Lambda) |
| Edge functions / server functions | Migrated to AWS Lambda |
| AI calls (e.g. Lovable AI Gateway) | Migrated to Amazon Bedrock |
| Database (Supabase DB) | Stays on Supabase — not migrated |
| Auth (Supabase Auth) | Stays on Supabase — not migrated |
| Storage & Realtime | Stays on Supabase — not migrated |
The app continues to call Supabase for database, auth, storage, and realtime from the AWS-hosted application.
Replit apps (Express.js + PostgreSQL):
| Component | What happens |
|---|---|
| Frontend & hosting | Migrated to AWS (S3 + CloudFront + Lambda) |
| Server logic (Express.js) | Migrated to AWS Lambda (API Gateway) |
| Database (PostgreSQL) | Schema and code migrated to AWS (Aurora Serverless / DynamoDB). Existing data is NOT migrated — customers must export and import their data separately. |
| Auth (Replit Auth) | Code migrated to AWS (Cognito). Existing user accounts are NOT migrated — customers must re-create or invite users in Cognito. |
| Realtime (WebSockets) | Migrated to AWS (API Gateway WebSocket) |
| File storage | Migrated to AWS (S3). Existing files are NOT migrated. |
Replit app infrastructure and code are migrated to AWS-native services, but existing data, user accounts, and files must be migrated separately by the customer.
Input Resolution
Resolve the user's input to a local directory path:
- If the user provides a local path: pass that path directly.
- If the user provides a GitHub URL: clone the repository locally and use that directory.
- If neither is provided: use the current working directory. If it doesn't look like an app directory, ask the user for the path.
Flow
Run the script commands in order, surfacing results to the user at each step:
1. Authentication
python3 scripts/launch_with_aws.py auth-start
Always run first. Returns immediately with JSON:
- If already authenticated:
{"authenticated": true, "reusedCachedSession": true, "baseUrl": "..."} - If silent refresh succeeded:
{"authenticated": true, "reusedCachedSession": false, "baseUrl": "..."} - If interactive sign-in is needed:
{"authenticated": false, "signInUrl": "https://...", "pid": 12345, "port": 54321, "baseUrl": "..."}
When authenticated is false, immediately display the signInUrl to the user (so they can open it in their browser) and call auth-wait in the same response:
python3 scripts/launch_with_aws.py auth-wait <pid>
where <pid> is the pid value from the auth-start response. This blocks until the user completes browser sign-in (or times out after 600s). Returns {"authenticated": true, "baseUrl": "..."} on success.
Sessions are capped at 90 days even if the identity provider does not set an expiration; after that the interactive flow is required again.
To check the current session without authenticating, or to sign out:
python3 scripts/launch_with_aws.py session-status
python3 scripts/launch_with_aws.py sign-out
session-status reports whether a session exists and how long until the token and overall session expire. sign-out best-effort revokes the refresh token and deletes the local ~/.launch-with-aws/session.json. On shared or untrusted workstations, run sign-out when finished.
2. Create Launch
For a local directory, present this data-handling confirmation verbatim and wait for explicit approval:
Your source code will be uploaded to the Launch with AWS service in [regionName], [region] ([sourceLabel]) to analyze your application and generate a migration plan. If you later approve execution, an AWS-hosted agent will modify a copy of your source code according to the plan and produce a migrated snapshot for you to download. Your uploaded source code and associated launch data are encrypted in transit and at rest and retained for up to 48 hours for recovery. Your data is never used to train AI models. We exclude Git history, Git-ignored files, and files matching common sensitive-file patterns. Sensitive-file filtering is best effort; review your project for secrets. Continue? Or reply with [otherRegions regionName] to run the migration there instead.
Fill in the region fields from resolve-region, verbatim, and offer each otherRegions entry as an alternative. If the agent is connected to an AWS MCP server, pass that server's URL verbatim. The script reads the region from it and decides whether it counts as a signal.
python3 scripts/launch_with_aws.py resolve-region [--aws-mcp-url https://aws-mcp.<region>.api.aws/mcp]
Returns {"region": ..., "regionName": ..., "source": ..., "sourceLabel": ..., "baseUrl": ..., "otherRegions": [{"region": ..., "regionName": ...}]}.
Do NOT call create-launch until the user explicitly confirms; a missing or ambiguous response means no. Naming one of otherRegions is both a region choice and approval — pass its region as --region and do not ask again.
python3 scripts/launch_with_aws.py create-launch <app-directory> [name] --region <region>
Zips the directory, uploads it, then creates the launch. Returns the full launch object including launch.launchId and the region it was created in, which becomes the user's saved default. A launch exists only in its own region — pass its --region on every later command.
The launch starts in analyzing status and automatically progresses through analysis and planning.
3. Poll Launch Status
python3 scripts/launch_with_aws.py get-launch-status <launch-id> --region <region>
Poll until status is planned (ready for execution) or failed; report any other status to the user. Key status progression:
analyzing→ detecting app type and dependenciesplanning→ generating migration planplanned→ ready for executionexecuting→ deployment in progresscompleted→ donefailed→ checkfailureReason
4. Get Full Launch Details & Confirm
python3 scripts/launch_with_aws.py get-launch <launch-id> plan,cost_estimate --region <region>
Get full launch details. Optional second argument is a comma-separated include list: analysis, plan, execution, cost_estimate, download_url.
Present the cost estimate and plan to the user. The costEstimate field in the response contains estimatedMonthlyCost, region, and a services breakdown with per-service costs.
Confirmation Gate — present and wait for explicit approval:
Migration Summary
- App type: [detected type from analysis]
- Architecture: [target architecture from plan]
- Estimated monthly cost: $X.XX/month ([costEstimate.region] pricing)
- Migration runs in: [regionName], [region]
Ready to proceed? This will execute the migration in an AWS-managed environment (no cost to you) and produce the migrated snapshot for you to download.
Do NOT call start-launch-execution until the user explicitly confirms.
5. Start Execution
python3 scripts/launch_with_aws.py start-launch-execution <launch-id> --region <region>
Starts deployment. Then poll with get-launch-status until status is completed or failed. Sleep at least 30 seconds between polls.
6. Download
python3 scripts/launch_with_aws.py get-launch-download-url <launch-id> --region <region>
Always present the full download URL to the user — they may need it to download the migrated snapshot directly or for reference.
7. List or Delete Launches
python3 scripts/launch_with_aws.py list-launches
python3 scripts/launch_with_aws.py delete-launch <launch-id> --region <region>
list-launches queries both regions and returns items with a region on each launch; add --region to query one. A region that cannot be reached is reported under errors instead of failing the command, unless every queried region fails, in which case the command exits with that error.
8. Post-Migration: Apply Migrated Code Locally
After obtaining the download URL (adapt commands for the user's platform if not POSIX):
Step A: Download and unpack
curl -L -o /tmp/migration-snapshot.zip "<download_url>"
mkdir -p /tmp/migration-output
unzip -o /tmp/migration-snapshot.zip -d /tmp/migration-output
Step B: Prepare the local workspace
Ensure the user's working directory is clean:
cd <user-app-directory>
git status
If there are uncommitted changes, ask the user to commit or stash first. Do NOT proceed with a dirty working tree.
Step C: Apply migration (3-way merge)
Create a migration branch and overlay the migrated files:
cd <user-app-directory>
git checkout -b aws-migration
rsync -a /tmp/migration-output/ .
git status
git diff --stat
Review the changes with the user. Key additions to highlight:
aws-blocks/— AWS Blocks infrastructure definitionDEPLOY.md— deployment instructions- Any modified config files
If there are conflicts with the user's existing files, present them and ask how to resolve.
Step D: Follow DEPLOY.md
Read the DEPLOY.md file in the project root and follow its instructions to deploy the app to the user's AWS account. Typical steps:
- AWS authentication (
aws login --profile aws-migrate --region us-east-1) - CDK bootstrap (first-time only):
npm install && npx cdk bootstrap - Deploy:
npx cdk deploy --all --progress events - Verify the CloudFront URL that CDK prints on completion.
Important: Always read DEPLOY.md from the migrated output — it is generated specifically for this app and architecture. Do not assume deployment steps from memory.
Unsupported Application Handling
If a launch fails during analysis with a failureReason indicating an unsupported app type (or the user's stack doesn't match the supported list):
-
Tell the user: "This app type isn't directly supported by Launch with AWS yet. Let me search for other skills that can help deploy this kind of application."
-
Search for relevant skills based on the app type (e.g.
aws-serverless,aws-containers,aws-database,aws-deployment,aws-cdk,aws-ai-ml).
FAQ
What does the launch-with-aws skill do?
Migrates vibe-coded web applications to AWS. Handles the full workflow from analysis through migration to deployment, producing deployable AWS Blocks infrastructure code. Supports full-stack apps built with vibe-coding platforms (Lovable, Bolt.new, Replit) and frontend web applications and websites: React, Vue, Angular, Next.js, Nuxt, Astro, SvelteKit, Gatsby, Vite, Svelte, Solid, Docusaurus, and others (static sites, SPAs, and SSR frameworks with static export). Triggers on: launch with AWS, launch on AWS, deploy to AWS, migrate to AWS, host my app on AWS, move my app to AWS, transfer my app to AWS. Activates when the user wants to migrate a vibe-coded app or frontend web app to AWS, even if they don't say 'migrate' explicitly.
How do I install launch-with-aws?
Run npx skills add https://github.com/aws/agent-toolkit-for-aws --skill launch-with-aws in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/launch-with-aws/ (personal) or .claude/skills/launch-with-aws/ (project).
Which AI agents can use launch-with-aws?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is launch-with-aws safe to install?
Security partners have reviewed it: Gen Agent Trust Hub passed; Socket flagged items to review; Snyk passed. As with any skill, read the SKILL.md and bundled scripts before installing.
Who maintains launch-with-aws?
It is published by aws in aws/agent-toolkit-for-aws. It has 4,747 recorded installs.
More from aws/agent-toolkit-for-aws
aws-iam
aws/agent-toolkit-for-aws
Provides verified corrections for IAM behaviors that AI agents frequently get wrong — policy evaluation edge cases, trust policy gotchas, STS session limits, Organizations quirks, and SAML/MFA specifics. Also provides structured workflows for IAM role management and baseline policy generation from application source code or a Terraform plan JSON. Covers condition operator safety (ForAnyValue/ForAllValues with Null checks), bucket policy deny patterns (VPC endpoint restrictions, org paths), confused deputy protection, and service role creation for AWS services (Glue, CloudTrail, Lambda, ECS, etc.) with aws:SourceAccount/aws:SourceArn trust conditions. Applies when creating IAM roles, writing IAM or bucket policies, generating policies from application source code or a Terraform plan JSON, working with STS, Organizations, or condition operators, or any task needing a service or execution role. Does not cover non-IAM authorization like Cognito user-pool policies or app-level RBAC.
aws-serverless
aws/agent-toolkit-for-aws
Routes serverless requests to a compute form factor and the skill that owns it. Applies first to Lambda, API Gateway, EventBridge, Step Functions and event-driven work while the compute, orchestration or integration choice is open — API backends, webhooks, jobs, cron, queue/stream consumers, durable workflows, event routing, Step Functions vs Durable Functions, and Pipes vs native event source mappings for queues and streams. Decides Event Functions on-demand vs Managed Instances, Web Functions, MicroVMs, EventBridge rules/Scheduler/Pipes/bus, HTTP vs REST API. Owns shared Lambda references (cold starts, SnapStart, concurrency, event sources, function URLs, throttling, troubleshooting). Defers to the specialized skill once a request commits to one form factor or service, or describes its defining feature, even unnamed (durable checkpointing and automatic step retries, Firecracker sandbox, managed instances, Node.js server). Not for EC2, ECS/EKS/Fargate or Amplify hosting.
aws-observability
aws/agent-toolkit-for-aws
Builds, configures, debugs, and optimizes AWS observability - operator-symptom questions and detecting Omni vs classic CloudWatch. CloudWatch on an already-reporting service: Log Insights, metric/composite/anomaly alarms, custom metrics/EMF, dashboards, X-Ray/ADOT tracing, canaries, CloudTrail, Dynamic Instrumentation (live breakpoints/snapshots), the Application Signals service map, and fleet health views. CloudWatch Omni on an existing Space: SQL over logs and traces, PromQL over metrics, Omni dashboards, Omni alerts, context graph for root cause, programmatic/IaC access (API/SDK/CLI/CloudFormation), driving Omni from a coding agent or skills, and evaluating AI agent quality from traces - on-demand and online scoring of live traffic, readback, custom evaluators. For first-time setup - creating an Omni Space, granting access, ingestion, or ADOT instrumentation for Application Signals (ServiceEvents, CI/CD metadata) or Omni - use setting-up-cloudwatch-observability. Not for app logging
aws-cdk
aws/agent-toolkit-for-aws
Authors, deploys, and troubleshoots AWS infrastructure using CDK with TypeScript or Python. Covers best practices, stack architecture, and construct patterns. Applies when writing CDK constructs, bootstrapping environments, running cdk deploy/synth/diff, fixing CDK or CloudFormation errors, planning stack structure, importing existing resources, resolving drift, or refactoring stacks without resource replacement.
aws-containers
aws/agent-toolkit-for-aws
Builds and deploys containerized workloads on Elastic Kubernetes Service (EKS), Elastic Container Service (ECS), Fargate, and ECR (Elastic Container Registry). Covers general EKS knowledge, Karpenter, AWS Load Balancer Controller and leveraging various open source Kubernetes projects with EKS. Covers general ECS knowledge, task definitions, Fargate services, ECS Exec, ECS Express Mode and ECS Managed Instances. Covers general Elastic Beanstalk knowledge, Elastic Beanstalk configuration and platforms supported by Elastic Beanstalk. Covers general ECR knowledge, ECR repository setup and lifecycle policies. Includes recommending, enabling, and reading Amazon ECS Action Logs to troubleshoot control-plane failures (deployment rollback/circuit-breaker, task placement, scaling, task replacement). Applies when deploying, debugging, or optimizing containers on AWS. Should be used instead of relying on internal knowledge for these services.
aws-billing-and-cost-management
aws/agent-toolkit-for-aws
Analyze AWS costs, find savings, manage budgets, evaluate Savings Plans and Reserved Instances, right-size EC2/Lambda/RDS/EBS with Compute Optimizer, look up service pricing, query CUR with Athena, detect cost anomalies, scope costs to billing views, and monitor Free Tier usage. Triggers on: AWS bill, cost analysis, reduce spend, savings plan, reserved instance, right-size, budget alert, cost optimization, pricing, free tier, cost anomaly, CUR, cost audit, billing view, billing view ARN.
Popular devops & cloud skills
microsoft-foundry
microsoft/azure-skills
Build, deploy, evaluate, optimize, fine-tune, and manage Microsoft Foundry agents, models, and resources end to end. USE FOR: foundry, azd ai agent, azd provision/deploy, hosted agent scaffold/develop/run/deploy/troubleshoot, prompt agent create, managed harness agent create, GitHub Copilot harness agent, create agent, update agent, add tool to agent, invoke agent, agent insights, evaluate agent, batch eval, continuous eval, continuous monitoring, agent CI/CD, optimize prompt, improve prompt, optimize agent instructions, Agent Optimizer scaffold, dataset curation from traces, deploy model, model fine-tuning (SFT/DPO/RFT), Foundry project, RBAC, role assignment, permissions, quota, capacity, region, deployment failure, AI Services, create Foundry resource, knowledge index, customize deployment, onboard, availability, training-data, grader, distillation, large file upload. DO NOT USE FOR: Azure Functions, App Service, general Azure deploy (use azure-deploy), general Azure prep (use azure
azure-diagnostics
microsoft/azure-skills
Debug Azure production issues on Azure using AppLens, Azure Monitor, resource health, and safe triage. WHEN: debug production issues, troubleshoot app service, app service high CPU, app service deployment failure, troubleshoot container apps, container app 503 or target port mismatch, troubleshoot functions, function telemetry missing or stopped after a config/connection-string change, troubleshoot AKS, VM RDP, Linux SSH, VM black screen, can't connect to VM, reset VM password, NSG or firewall blocking, kubectl cannot connect, kube-system/CoreDNS failures, pod pending, crashloop, node not ready, upgrade failures, analyze logs, KQL, insights, image pull failures, cold start issues, health probe failures, resource health, root cause of errors, troubleshoot event hubs, troubleshoot service bus, messaging SDK error, AMQP connection failure, message lock lost, lock renewal during long-running processing, service bus dead letter.
azure-prepare
microsoft/azure-skills
Prepare azd-based Azure projects for deployment: generates azure.yaml, infrastructure (Bicep/Terraform), and Dockerfiles for the Azure Developer CLI (azd) workflow. USE ONLY when the user explicitly wants to use azd as the deployment tool, or the project already has an azure.yaml file. DO NOT USE FOR: non-azd deployments, Python App Service code-only deploys (use python-appservice-deploy), or cross-cloud migration (use azure-cloud-migrate). WHEN: prepare app for azd, create azure.yaml, set up azd infrastructure, modernize app for Azure with azd, deploy with azd, function app, timer trigger, service bus trigger, event-driven function, managed identity, generate Bicep, generate Terraform, create and deploy to Azure.
azure-ai
microsoft/azure-skills
Use for Azure AI: Search, Speech, OpenAI, Document Intelligence. Helps with search, vector/hybrid search, speech-to-text, text-to-speech, transcription, OCR. WHEN: AI Search, query search, vector search, hybrid search, semantic search, speech-to-text, text-to-speech, transcribe, OCR, convert text to speech.
azure-deploy
microsoft/azure-skills
Execute Azure deployments for ALREADY-PREPARED applications that have existing .azure/deployment-plan.md and infrastructure files. DO NOT use this skill when the user asks to CREATE a new application — use azure-prepare instead. This skill runs azd up, azd deploy, terraform apply, and az deployment commands with built-in error recovery. Requires .azure/deployment-plan.md from azure-prepare and validated status from azure-validate. WHEN: "run azd up", "run azd deploy", "execute deployment", "push to production", "push to cloud", "go live", "ship it", "bicep deploy", "terraform apply", "publish to Azure", "launch on Azure". DO NOT USE WHEN: "create and deploy", "build and deploy", "create a new app", "set up infrastructure", "create and deploy to Azure using Terraform" — use azure-prepare for these.
azure-validate
microsoft/azure-skills
Pre-deployment validation for Azure readiness. Run deep checks on configuration, infrastructure (Bicep or Terraform), RBAC role assignments, managed identity permissions, and prerequisites before deploying. WHEN: validate my app, check deployment readiness, run preflight checks, verify configuration, check if ready to deploy, validate azure.yaml, validate Bicep, test before deploying, troubleshoot deployment errors, validate Azure Functions, validate function app, validate serverless deployment, verify RBAC roles, check role assignments, review managed identity permissions, what-if analysis, validate Container Apps deployment.