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:

bash

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.

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 (AppSync Events)
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 or GitHub URL:

If the user provides a local path: pass that path directly.
If the user provides a GitHub URL: pass it directly (the service clones it server-side).
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

bash

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

false

, immediately display the
signInUrl
to the user (so they can open it in their browser) and call

auth-wait

in the same response:

bash

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.

2. Create Launch

For a local directory, present this confirmation and wait for explicit approval:

Your source code will be uploaded to the Launch with AWS service 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 source code is encrypted at rest, automatically deleted after 7 days, and 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?

Do NOT call

create-launch

for a local directory until the user explicitly confirms. A missing or ambiguous response means no.

bash

python3 scripts/launch_with_aws.py create-launch <source-path-or-github-url> [name]

Creates a launch from a local directory (zips, uploads, then creates) or a GitHub URL (passes directly). Returns JSON with the full

launch

object including

launch.launchId

The launch starts in

analyzing

status and automatically progresses through analysis and planning.

3. Poll Launch Status

bash

python3 scripts/launch_with_aws.py get-launch-status <launch-id>

Poll until

status

planned

(ready for execution),

awaiting_input

(needs context answers — see step 4), or

failed

. Key status progression:

```
analyzing
```
→ detecting app type and dependencies
```
awaiting_input
```
→ needs context answers (see
```
refine-plan
```
)
```
planning
```
→ generating migration plan
```
planned
```
→ ready for execution
```
executing
```
→ deployment in progress
```
completed
```
→ done
```
failed
```
→ check
```
failureReason
```

status

awaiting_input

, check

contextInputs

for the questions that need answering. Inputs with

required: true

must be answered before the launch can proceed; others are optional enrichment.

4. Refine Plan (if awaiting_input)

bash

python3 scripts/launch_with_aws.py refine-plan <launch-id> key1=value1 key2=value2

Provide context answers to refine the plan. Triggers re-planning.

5. Get Full Launch Details & Confirm

bash

python3 scripts/launch_with_aws.py get-launch <launch-id> plan,cost_estimate

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

Region: us-east-1

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.

6. Start Execution

bash

python3 scripts/launch_with_aws.py start-launch-execution <launch-id>

Starts deployment. Then poll with

get-launch-status

until

status

completed

failed

. Sleep at least 30 seconds between polls.

7. Download

bash

python3 scripts/launch_with_aws.py get-launch-download-url <launch-id>

Always present the full download URL to the user — they may need it to download the migrated snapshot directly or for reference.

8. List or Delete Launches

bash

python3 scripts/launch_with_aws.py list-launches
python3 scripts/launch_with_aws.py delete-launch <launch-id>

9. 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

bash

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:

bash

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:

bash

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 definition
```
DEPLOY.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

databases-on-aws

deploy-on-aws

aws-cdk

sagemaker-ai

launch-with-aws

NPX Install

Tags

SKILL.md Content

Launch with AWS

Script Invocation

Supported Application Types

What Gets Migrated vs. What Stays

Input Resolution

Flow

1. Authentication

2. Create Launch

3. Poll Launch Status

4. Refine Plan (if awaiting_input)

5. Get Full Launch Details & Confirm

6. Start Execution

7. Download

8. List or Delete Launches

9. Post-Migration: Apply Migrated Code Locally

Step A: Download and unpack

Step B: Prepare the local workspace

Step C: Apply migration (3-way merge)

Step D: Follow DEPLOY.md

Unsupported Application Handling