Launch With AWS

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.

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).

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):

The app continues to call Supabase for database, auth, storage, and realtime from the AWS-hosted application.

Replit apps (Express.js + PostgreSQL):

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

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 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 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?

Do NOT call create-launch for a local directory until the user explicitly confirms. A missing or ambiguous response means no.

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.