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feat(migrate): Sync migrate/ from GitFarm
Source: Startups-Migrate@a3ce687
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‎migrate/README.md‎

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# Migrate
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# Migration to AWS
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AI agent plugins and tools for migrating workloads to AWS.
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AI agent skills for migrating workloads to AWS, built for [Claude Code](https://docs.anthropic.com/en/docs/agents-and-tools/claude-code/overview) and [Cursor](https://www.cursor.com/).
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## Plugins
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## What This Does
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| Plugin | Description | Status |
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| -------------------- | --------------------------------------------------------------------------------------------------------------------------------------------- | --------- |
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| **migration-to-aws** | Migrate GCP/Azure infrastructure and AI workloads to AWS with resource discovery, architecture mapping, cost analysis, and execution planning | Available |
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Point this plugin at your codebase, Terraform files, or GCP billing data. It runs a structured 6-phase migration assessment — discovering what you have, asking the right questions, designing the AWS architecture, estimating costs with real pricing data, and generating runnable migration artifacts.
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**For AI-focused startups**, it goes further:
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- **Detects your entire AI stack** — not just "you use GPT-4o" but your agents, tools, orchestration patterns, memory layers, and multi-model pipelines
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- **Recommends three migration paths** for agentic workloads: retarget (keep your framework, swap models), AgentCore Harness (config-based managed agents), or Strands Agents (AWS-native multi-agent SDK)
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- **Surfaces options you wouldn't find on your own** — like Strands Agents (open-source, powers AgentCore internally) and AgentCore Harness (declare an agent in 3 API calls)
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- **Generates runnable artifacts** — `harness.json`, deployment scripts, incremental migration scripts, provider adapters — tailored to your specific models, tools, and architecture
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- **Gives honest pricing comparisons** — finds you the best Bedrock option for your workload with current pricing data, including side-by-side cost comparisons against your existing OpenAI/Gemini spend
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## Installation
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### Claude Code
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```bash
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/plugin marketplace add awslabs/startups --sparse migrate
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/plugin install migration-to-aws@startups-migrate
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# Add the marketplace
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/plugin marketplace add awslabs/startups
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# Install the plugin
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/plugin install migration-to-aws@startups-for-aws
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```
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### Cursor
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> **Coming soon** — This plugin is not yet published on the Cursor Marketplace. In the meantime, you can use it locally by cloning this repository and pointing Cursor to the plugin directory at `migrate/plugins/migration-to-aws`.
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## Quick Start
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```bash
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# 1. Install the plugin (see above)
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# 2. cd into a project with Terraform, app code, or GCP billing exports
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cd path/to/your/gcp-project
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# 3. Trigger the skill
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"migrate from GCP to AWS"
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```
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### Kiro
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The skill creates a `.migration/<MMDD-HHMM>/` directory in the current working directory and writes all artifacts there.
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## Workflow
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Powers are available under `powers/` for direct use with Kiro.
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1. **Discover** — Scan Terraform files, application code, and/or billing data. Detects GCP resources, AI models, agentic frameworks, tools, and orchestration patterns.
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2. **Clarify** — Ask targeted questions about migration preferences, AI priorities, agentic migration approach, memory requirements, and timeline. **Mandatory** — cannot be skipped.
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3. **Design** — Map GCP services to AWS equivalents. For AI workloads: select Bedrock models with honest pricing comparison. For agentic workloads: design AgentCore Harness config or Strands architecture.
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4. **Estimate** — Calculate monthly AWS costs using real-time pricing data. Compare to current GCP/OpenAI spend.
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5. **Generate** — Create migration artifacts: Terraform, provider adapters, `harness.json`, deployment scripts, incremental migration scripts, and documentation.
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6. **Feedback** _(optional)_ — Collect anonymized feedback to improve the tool.
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## What It Detects
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| Category | Examples |
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| -------------------- | ----------------------------------------------------------------------------------------------------- |
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| Infrastructure | Cloud Run, Cloud SQL, GKE, Cloud Functions, Pub/Sub, Cloud Storage, VPC, DNS |
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| AI Models | OpenAI (GPT-4o, GPT-5.4, o-series, embeddings, image, speech), Gemini (Pro, Flash), Anthropic, Cohere |
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| Agentic Frameworks | LangGraph, CrewAI, AutoGen, OpenAI Agents SDK, Strands, custom agent loops |
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| Integration Patterns | Direct SDK, LangChain, LlamaIndex, LiteLLM, OpenRouter, MCP servers |
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| Agent Architecture | Single agent, hierarchical, swarm, graph, sequential orchestration |
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| Tools & Memory | Tool definitions with transport/auth classification, memory backends (Redis, Postgres, vector stores) |
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## What You Get That a Base LLM Can't
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| Capability | Base LLM | This Plugin |
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| ------------------------ | --------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------- |
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| Model recommendation | Generic "use Bedrock" | Your specific models mapped with pricing, honest assessment per model, stay-or-migrate recommendation |
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| Agentic migration | "Swap ChatOpenAI for ChatBedrock" | Detects your framework, agents, tools, orchestration pattern. Recommends retarget vs Harness vs Strands with effort ranges. |
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| Multi-model coordination | Generic advice | Warns about re-embedding requirements, cascade pair testing, tiered strategies — based on your actual model usage |
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| Framework gotchas | Not covered | Documents real issues: LangGraph checkpointer incompatibility, CrewAI hierarchical process failures with smaller models, async thread pool exhaustion |
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| Regional validation | Outdated region lists | Live `get_regional_availability` MCP call — catches "AgentCore Harness isn't in your target region" before you commit |
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| Cost estimation | Stale pricing | Three-tier pricing: cached current rates, live AWS Pricing API, fallback. Shows ±5-10% accuracy. |
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| Generated code | Generic templates | Your model IDs, your tool names, your system prompts, your region — in runnable scripts |
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| Incremental migration | Not suggested | Run existing OpenAI models on AgentCore infrastructure today, A/B test with Bedrock per-invocation, swap when confident |
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## Agent Skill Triggers
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| Agent Skill | Triggers |
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| -------------- | ------------------------------------------------------------------------------------------------------------- |
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| **gcp-to-aws** | "migrate GCP to AWS", "move from GCP", "GCP migration plan", "estimate AWS costs", "migrate my AI app to AWS" |
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## MCP Servers
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| Server | Purpose |
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| ---------------- | --------------------------------------------------------------- |
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| **awsknowledge** | AWS documentation, regional availability, architecture guidance |
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| **awspricing** | Real-time AWS service pricing for cost estimates |
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## Requirements
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- Claude Code >=2.1.29 or [Cursor >= 2.5](https://cursor.com/changelog/2-5)
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- AWS CLI configured with appropriate credentials
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- At least one input source: Terraform files, application code, or GCP billing data
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- **For AI/agentic migration:** Application source code is required (billing/IaC alone cannot detect agent architecture)
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## Structure
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```
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migrate/
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├── plugins/ # Claude Code and Cursor plugins
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├── powers/ # Kiro powers (generated from plugin source)
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├── docs/ # Documentation
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├── tests/ # Evaluation fixtures and invariants
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└── tools/ # Evaluation tooling and generators
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└── docs/ # Documentation
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```
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## Ownership
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This folder is maintained by the **Startups-Migrate** team. See `OWNERS.yaml` for routing details.
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## License
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This project is licensed under the Apache-2.0 License.

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