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migration-to-aws

Skill

Migrate workloads from Google Cloud Platform to AWS — including AI and agentic workloads regardless of cloud provider. Triggers on: migrate from GCP, GCP to AWS, move off Google Cloud, migrate Terraform to AWS, migrate Cloud SQL to RDS, migrate GKE to EKS, migrate Cloud Run to Fargate, Google Cloud

0Cloud

Install

git clone https://github.com/awslabs/startups.git ~/.claude/skills/migration-to-aws

What is migration-to-aws?

Migrate workloads from Google Cloud Platform to AWS — including AI and agentic workloads regardless of cloud provider. Triggers on: migrate from GCP, GCP to AWS, move off Google Cloud, migrate Terraform to AWS, migrate Cloud SQL to RDS, migrate GKE to EKS, migrate Cloud Run to Fargate, Google Cloud migration, migrate from OpenAI to Bedrock, move off OpenAI, switch from ChatGPT API to AWS, migrate from Gemini to Bedrock, migrate LangChain to Bedrock, migrate LangGraph to AWS, migrate agentic workloads to AWS, move AI workloads to AWS, migrate my AI app to AWS. Runs a 6-phase process: discover GCP resources from Terraform files, app code, or billing exports, clarify migration requirements, design AWS architecture, estimate costs, generate migration artifacts, and collect optional feedback. Clarify must finish before Design, Estimate, or Generate. Includes AI provider migration guidance (for example, OpenAI to Amazon Bedrock) by selecting closest-fit Bedrock model families for required modality, latency/quality targets, context windows, and cost constraints. Model mapping is compatibility-guided, not 1:1 parity; validate prompts, tool-calling behavior, and eval metrics before cutover. Do not use for: Azure or on-premises migrations to AWS, AWS-to-GCP reverse migration, general AWS architecture advice without migration intent, GCP-to-GCP refactoring, or multi-cloud deployments that do not involve migrating off GCP.

What this can do

Capabilities declared in this component's own frontmatter — not inferred.

Inherit all session tools

Declares no tool restrictions — inherits every session tool

~359 tokens of context used while enabled, before you invoke anything

Documentation

README · ~13 min read

Migration to AWS Skill

Philosophy

  • Re-platform by default: Select AWS services that match GCP workload types (e.g., Cloud Run → Fargate, Cloud SQL → RDS).
  • Dev sizing unless specified: Default to development-tier capacity (e.g., db.t4g.micro, single AZ). Upgrade only on user direction.
  • Cost informed decisions: Always estimate costs before generating infrastructure code.
  • No human one-time migration costs: Do not present human labor, professional services, or people-time work as dollar estimates or "one-time migration cost" budget categories. Vendor charges grounded in data (for example GCP data transfer egress in the infra estimate when billing exists) are allowed.
  • Multi-signal approach: Design phase adapts based on available inputs — Terraform IaC for infrastructure, billing data for service mapping, and app code for AI workload detection.
  • Terraform generation preferred: Use Terraform as the default IaC tool for migration artifacts.
  • Architecture choice preservation: Preserve existing application architecture patterns during migration.
  • BigQuery / google_bigquery_*: The skill does not recommend a specific AWS analytics or warehouse service. During Clarify, if discovery shows BigQuery (IaC google_bigquery_* and/or billing rows for BigQuery), you must surface the specialist advisory before Design (see references/phases/clarify/clarify.md). Design output uses Deferred — specialist engagement; keep directing the user to their AWS account team and/or a data analytics migration partner through Design, Estimate, and docs (see references/phases/design/design-infra.md BigQuery specialist gate).

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