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

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:

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.

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

Poll until status is 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

If status is 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)

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

5. Get Full Launch Details & Confirm

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

Starts deployment. Then poll with get-launch-status until status is completed or failed. Sleep at least 30 seconds between polls.

7. Download

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

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

Step B: Prepare the local workspace

Ensure the user's working directory is clean:

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:

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:

1. AWS authentication (aws login --profile aws-migrate --region us-east-1)
2. CDK bootstrap (first-time only): npm install && npx cdk bootstrap
3. Deploy: npx cdk deploy --all --progress events
4. 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):

1. 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."

2. 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).