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GCP To AWS

ClineOfficialPublished by Cline

  • Skill
  • Workflow
  • Cloud and DevOps
  • Apache-2.0
  • Low risk

From the author

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
— the description by Cline

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

The gcp-to-aws skill provides a deterministic, multi-phase process to migrate Google Cloud Platform (GCP) workloads to AWS. It supports infrastructure migration via Terraform analysis and AI workload migration by mapping models to Amazon Bedrock. The skill enforces a strict state machine, requiring discovery, clarification, design, estimation, and generation phases to ensure a reliable migration path. It includes specific logic for handling hybrid stacks, AI provider transitions, and complex infrastructure clustering.

What it does

  • Discovers GCP resources from Terraform, application code, or billing data.
  • Clarifies migration requirements through a structured questionnaire.
  • Designs AWS architecture based on GCP workload types and AI model requirements.
  • Estimates costs for the proposed AWS infrastructure and AI services.
  • Generates migration artifacts including Terraform configurations and AI provider adapters.
  • Provides specialized guidance for migrating AI workloads from Gemini or OpenAI to Amazon Bedrock.

When to use it

  • When you need to migrate existing GCP infrastructure to AWS.
  • When you are looking to move AI-driven applications from GCP or OpenAI to Amazon Bedrock.
  • When you have Terraform files or billing exports and need a structured migration plan.
  • When you require a step-by-step, validated approach to re-platforming cloud services.

Advantages

  • Enforces a rigorous, fail-closed state machine to prevent skipped steps.
  • Provides intelligent mapping of GCP services to AWS equivalents.
  • Includes specific logic for AI workload migration and model compatibility.
  • Supports multi-signal discovery using IaC, code, and billing data.
  • Ensures consistent artifact generation through a structured 6-phase process.

Usage examples

“Migrate my GKE and Cloud SQL setup to AWS.”
The agent initiates the discovery phase, analyzing your Terraform files to identify GKE and Cloud SQL resources, then guides you through the clarification and design phases for EKS and RDS.
“I need to move my LangChain app from OpenAI to Bedrock.”
The agent identifies the AI workload, selects the best-fit Bedrock model family, and generates the necessary provider adapters and migration documentation.

Install

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/cline/gcp-to-aws/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip gcp-to-aws.zip -d .agents/skills

Documentation by Cline

GCP-to-AWS Migration 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.

  • 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.

  • 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).


Definitions

  • "Load" = Read the file using the Read tool and follow its instructions. Do not summarize or skip sections.

  • $MIGRATION_DIR = The run-specific directory under .migration/ (e.g., .migration/0226-1430/). Set during Phase 1 (Discover).


Context Loading Rules

Each phase loads reference files on demand. To keep per-turn context manageable and prevent instruction-following degradation:

  • Budget: Each phase should load no more than ~800 lines of instructions (excluding user artifacts like JSON profiles and MCP tool results).

  • Conditional loading: Reference files with trigger conditions (e.g., agentic_profile.is_agentic == true) MUST NOT be loaded unless the condition is met. Do not speculatively load files.

  • No duplication: Model mapping tables, pricing data, and shared warnings exist in one canonical file. Other files reference them; they do not copy them inline.

  • Progressive depth: Phase orchestrators (design.md, generate.md) contain short routing logic that points to detailed sub-files. Load the sub-file only when its path is selected.

Conditional reference files (load ONLY when condition is true):

FileCondition
design-refs/ai-gemini-to-bedrock.mdai-workload-profile.json exists AND summary.ai_source = "gemini" or "both"
design-refs/ai-openai-to-bedrock.mdai-workload-profile.json exists AND summary.ai_source = "openai" or "both"
design-refs/ai-anthropic-to-bedrock.mdai-workload-profile.json exists AND summary.ai_source = "anthropic"
design-refs/ai.mdai-workload-profile.json exists AND summary.ai_source = "other"
design-refs/design-ref-harness.mdagentic_profile.is_agentic == true AND ai_constraints.agentic.migration_approach == "harness"
design-refs/design-ref-agentic-to-agentcore.mdagentic_profile.is_agentic == true AND ai_constraints.agentic.migration_approach == "strands"
shared/retarget-gotchas.mdagentic_profile.is_agentic == true AND ai_constraints.agentic.migration_approach == "retarget"

When adding new reference files, verify the phase's total loaded instructions remain under budget. If a new file would exceed ~800 lines when combined with other loaded refs, split it or make it conditional.

Hybrid stack budget warning:

When both gcp-resource-inventory.json AND ai-workload-profile.json exist, the combined design refs will approach the ~800-line budget. Output this warning to the user before loading the AI design refs:

"⚠️ This is a large hybrid stack (infrastructure + AI workloads). To ensure complete and accurate recommendations, consider running the migration in two separate passes:

Pass 1 — Infrastructure: Run with only your Terraform files to get infra mapping, Terraform generation, and cost estimates.

Pass 2 — AI workloads: Run with only your application code to get Bedrock model recommendations, provider adapters, and AI migration artifacts.

Continue with the combined run? (Y/N)"

If the user chooses to continue, proceed with the combined run. Load AI refs after infra refs to preserve infra instruction fidelity. If the user declines, stop and instruct them to re-run with a single input source type.

This warning is advisory only — it does not block the run.


Prerequisites

User must provide at least one GCP source:

  • Terraform IaC: .tf files (with optional .tfvars, .tfstate)

  • Application code: Source files with GCP SDK or AI framework imports

  • Billing data: GCP billing/cost/usage export files (CSV or JSON)

If none of the above are found, stop and ask user to provide at least one source type.


State Machine

This is the execution controller. After completing each phase, consult this table to determine the next action.

Current StateConditionNext Action
discoverphases.discover != "completed"Load references/phases/discover/discover.md
clarifyphases.discover == "completed" AND phases.clarify != "completed"Load references/phases/clarify/clarify.md
designphases.clarify == "completed" AND phases.design != "completed"Load references/phases/design/design.md
estimatephases.design == "completed" AND phases.estimate != "completed"Load references/phases/estimate/estimate.md
generatephases.estimate == "completed" AND phases.generate != "completed"Load references/phases/generate/generate.md
completephases.generate == "completed" AND phases.feedback == "pending"Set phases.feedback to "completed" (user had two chances), then migration complete
completephases.generate == "completed" AND phases.feedback == "completed"Migration planning complete

How to determine current state (deterministic):

  1. Read $MIGRATION_DIR/.phase-status.json

  2. If current_phase exists, use it (must match one of: discover, clarify, design, estimate, generate, complete)

  3. Otherwise use ordered phase evaluation: discover → clarify → design → estimate → generate

  4. Pick the first phase in that order where phases.<phase> != "completed"; if none, state is complete

Phase gate checks: If prior phase incomplete, do not advance (e.g., cannot enter estimate without completed design).

Clarify is mandatory: Do not load references/phases/design/design.md, references/phases/estimate/estimate.md, or references/phases/generate/generate.md unless $MIGRATION_DIR/.phase-status.json exists and phases.clarify is exactly "completed". A preferences.json file alone is not sufficient proof that Clarify ran. If the user asks to skip Clarify or jump straight to Design, cost estimate, or artifact generation, refuse briefly, then load references/phases/clarify/clarify.md and run Phase 2. There is no exception for "quick" or "obvious" migrations.

Feedback checkpoints: Feedback is not a sequential phase — it is offered at two interleaved checkpoints (after Discover and after Estimate). See the Feedback Checkpoints section below for details.

Handoff Gate Orchestration (Fail Closed)

Load references/shared/handoff-gates.md when executing any phase completion step.

  1. Single $MIGRATION_DIR: Use one run directory for the entire migration. Do not mix artifacts across .migration/*/ sessions.

  2. Re-read from disk: Before each phase (and before each handoff gate), Read required artifacts from $MIGRATION_DIR/. Do not rely on chat memory.

  3. Advance only on HANDOFF_OK: A phase is complete only when its orchestrator emits HANDOFF_OK | phase=<name> | artifacts=.... Do not load the next phase without it.

  4. On GATE_FAIL: Output the failure line(s) to the user in plain language. Do NOT modify artifacts to pass the gate. Do NOT continue to the next phase. Tell the user which phase to re-run.

  5. Re-entry: Re-running an earlier phase after downstream phases completed requires explicit user confirmation; downstream phases must be reset to "pending". See handoff-gates.md re-entry table.

Generate phase additionally loads references/shared/validate-artifacts.md before writing migration-report.html.


State Validation

When reading $MIGRATION_DIR/.phase-status.json, validate before proceeding:

  1. Multiple sessions: If multiple directories exist under .migration/, list them with their phase status and ask: [A] Resume latest, [B] Start fresh, [C] Cancel.

  2. Invalid JSON: If .phase-status.json fails to parse, STOP. Output: "State file corrupted (invalid JSON). Delete the file and restart the current phase."

  3. Unrecognized phase: If phases object contains a phase not in {discover, clarify, design, estimate, generate, feedback}, STOP. Output: "Unrecognized phase: [value]. Valid phases: discover, clarify, design, estimate, generate, feedback."

  4. Unrecognized status: If any phases.* value is not in {pending, in_progress, completed}, STOP. Output: "Unrecognized status: [value]. Valid values: pending, in_progress, completed."

  5. Invalid current_phase (if present): If current_phase is not in {discover, clarify, design, estimate, generate, complete}, STOP. Output: "Unrecognized current_phase: [value]. Valid values: discover, clarify, design, estimate, generate, complete."

  6. Out-of-order completion: For ordered phases [discover, clarify, design, estimate, generate], if any later phase is "completed" while an earlier phase is not "completed", STOP. Output: "Inconsistent phase ordering detected. Reconcile .phase-status.json before resuming."

  7. Multiple active phases: Across core phases {discover, clarify, design, estimate, generate}, at most one phase may be "in_progress". If >1, STOP. Output: "Multiple phases are in_progress. Keep only one active phase before resuming."


State Management

Migration state lives in $MIGRATION_DIR (.migration/[MMDD-HHMM]/), created by Phase 1 and persisted across invocations.

.phase-status.json schema:

{
  "migration_id": "0226-1430",
  "last_updated": "2026-02-26T15:35:22Z",
  "current_phase": "design",
  "phases": {
    "discover": "completed",
    "clarify": "completed",
    "design": "in_progress",
    "estimate": "pending",
    "generate": "pending",
    "feedback": "pending"
  }
}

Status values: "pending" → "in_progress" → "completed". Never goes backward. For core phases (discover, clarify, design, estimate, generate), at most one phase may be "in_progress" at any time. current_phase is optional but recommended; when present it is authoritative.

The .migration/ directory is automatically protected by a .gitignore file created in Phase 1.

Phase Status Update Protocol

Use read-merge-write updates for .phase-status.json:

  1. Read the current file before every update.

  2. Change only the phase keys being advanced and last_updated.

  3. Keep prior completed phases unchanged.

  4. Set current_phase to the next deterministic phase (or complete after generate).

  5. Write the full file in the same turn as your final phase work message.

Example — after completing the Clarify phase, write $MIGRATION_DIR/.phase-status.json with:

{
  "migration_id": "MMDD-HHMM",
  "last_updated": "2026-02-26T15:35:22Z",
  "current_phase": "design",
  "phases": {
    "discover": "completed",
    "clarify": "completed",
    "design": "pending",
    "estimate": "pending",
    "generate": "pending",
    "feedback": "pending"
  }
}

Replace MMDD-HHMM with the actual migration ID, generate the last_updated ISO 8601 UTC timestamp yourself, and set each phase to its correct status at that point.


Phase Summary Table

PhaseInputsOutputsReference
Discover.tf files, app source code, and/or billing exports (at least one required)gcp-resource-inventory.json, gcp-resource-clusters.json, ai-workload-profile.json, billing-profile.json, .phase-status.json updated (outputs vary by input)references/phases/discover/discover.md
ClarifyDiscovery artifacts (gcp-resource-inventory.json, gcp-resource-clusters.json, ai-workload-profile.json, billing-profile.json — whichever exist)preferences.json, .phase-status.json updatedreferences/phases/clarify/clarify.md
Designpreferences.json + discovery artifactsaws-design.json (infra), aws-design-ai.json (AI), aws-design-billing.json (billing-only)references/phases/design/design.md
Estimateaws-design.json or aws-design-billing.json or aws-design-ai.json, preferences.jsonestimation-infra.json or estimation-ai.json or estimation-billing.json, .phase-status.json updatedreferences/phases/estimate/estimate.md
Generateestimation-infra.json or estimation-ai.json or estimation-billing.json, aws-design.json or aws-design-billing.json or aws-design-ai.json, preferences.jsongeneration-infra.json or generation-ai.json or generation-billing.json + terraform/, scripts/, ai-migration/, validation-report.json (when infra route active), MIGRATION_GUIDE.md, README.md, .phase-status.json updatedreferences/phases/generate/generate.md
Feedback.phase-status.json (discover completed minimum), all existing migration artifactsfeedback.json, trace.json, .phase-status.json updatedreferences/phases/feedback/feedback.md

MCP Servers

awspricing (for cost estimation):

  • Provides get_pricing, get_pricing_service_codes, get_pricing_service_attributes tools

  • Only needed during Estimate phase. Discover and Design do not require it.

  • Primary pricing source: references/shared/pricing-cache.md (cached 2026 rates, ±5-10% for infrastructure, ±15-25% for AI models). MCP is secondary — used only for services not found in the cache.


Files in This Skill

gcp-to-aws/
├── SKILL.md                                    ← You are here (orchestrator + state machine)
│
├── references/
│   ├── phases/
│   │   ├── discover/
│   │   │   ├── discover.md                     # Phase 1: Discover orchestrator
│   │   │   ├── discover-iac.md                 # Terraform/IaC discovery
│   │   │   ├── discover-app-code.md            # App code discovery
│   │   │   └── discover-billing.md             # Billing data discovery
│   │   ├── clarify/
│   │   │   ├── clarify.md                     # Phase 2: Clarify orchestrator
│   │   │   ├── clarify-global.md              # Category A: Global/Strategic (Q1-Q7)
│   │   │   ├── clarify-compute.md             # Categories B+C: Config Gaps + Compute (Q8-Q11)
│   │   │   ├── clarify-database.md            # Category D: Database (Q12–Q13b)
│   │   │   ├── clarify-ai.md                  # Category F: AI/Bedrock (Q14-Q22)
│   │   │   └── clarify-ai-only.md             # Standalone AI-only migration flow
│   │   ├── design/
│   │   │   ├── design.md                       # Phase 3: Design orchestrator
│   │   │   ├── design-infra.md                 # Infrastructure design (IaC-based)
│   │   │   ├── design-ai.md                    # AI workload design (Bedrock)
│   │   │   └── design-billing.md               # Billing-only design (fallback)
│   │   ├── estimate/
│   │   │   ├── estimate.md                     # Phase 4: Estimate orchestrator
│   │   │   ├── estimate-infra.md               # Infrastructure cost analysis
│   │   │   ├── estimate-ai.md                  # AI workload cost analysis
│   │   │   └── estimate-billing.md             # Billing-only cost analysis
│   │   ├── generate/
│   │   │   ├── generate.md                     # Phase 5: Generate orchestrator
│   │   │   ├── generate-infra.md               # Infrastructure migration plan
│   │   │   ├── generate-ai.md                  # AI migration plan
│   │   │   ├── generate-billing.md             # Billing-only migration plan
│   │   │   ├── generate-artifacts-infra.md     # Terraform configurations
│   │   │   ├── generate-artifacts-scripts.md  # Migration scripts
│   │   │   ├── generate-artifacts-ai.md        # Provider adapter + test harness
│   │   │   ├── generate-artifacts-billing.md   # Skeleton Terraform
│   │   │   └── generate-artifacts-docs.md      # MIGRATION_GUIDE.md + README.md
│   │   └── feedback/
│   │       ├── feedback.md                     # Phase 6: Feedback orchestrator
│   │       └── feedback-trace.md               # Anonymized trace builder
│   │
│   ├── design-refs/
│   │   ├── index.md                            # Lookup table: GCP type → design-ref file
│   │   ├── fast-path.md                        # Deterministic 1:1 mappings (Pass 1)
│   │   ├── compute.md                          # Compute mappings (Cloud Run, GCE, GKE, etc.)
│   │   ├── database.md                         # Database mappings (Cloud SQL, Spanner, etc.)
│   │   ├── storage.md                          # Storage mappings (GCS, Filestore, etc.)
│   │   ├── networking.md                       # Networking mappings (VPC, LB, DNS, etc.)
│   │   ├── messaging.md                        # Messaging mappings (Pub/Sub, etc.)
│   │   └── ai.md                               # AI mappings (Vertex AI → Bedrock)
│   │
│   ├── clustering/terraform/
│   │   ├── classification-rules.md             # Primary/secondary classification
│   │   ├── clustering-algorithm.md             # Cluster formation rules
│   │   ├── depth-calculation.md                # Topological depth calculation
│   │   └── typed-edges-strategy.md             # Edge type assignment
│   │
│   └── shared/
│       ├── schema-phase-status.md              # .phase-status.json schema (canonical reference)
│       ├── schema-discover-iac.md              # gcp-resource-inventory + clusters schemas (loaded by discover-iac.md)
│       ├── schema-discover-ai.md               # ai-workload-profile schema (loaded by discover-app-code.md and discover-iac.md Step 7d)
│       ├── schema-discover-billing.md          # billing-profile schema (loaded by discover-billing.md)
│       ├── schema-estimate-infra.md            # estimation-infra.json schema (loaded by estimate-infra.md at write time)
│       ├── handoff-gates.md                    # Fail-closed phase handoff protocol (GATE_FAIL / HANDOFF_OK)
│       ├── validate-artifacts.md               # Pre-report validation (Generate Step 0; read-only)
│       ├── migration-complexity.md             # Complexity tier definitions (small/medium/large) for timeline scaling
│       ├── pricing-cache.md                    # Cached AWS + source provider pricing (±5-25%, primary source)
│       └── bedrock-quotas.md                   # Bedrock TPM/RPM quota awareness, burndown rates, capacity planning
ConditionAction
No GCP sources found (no .tf, no app code, no billing data)Stop. Output: "No GCP sources detected. Provide at least one source type (Terraform files, application code, or billing exports) and try again."
.phase-status.json missing phase gateStop. Output: "Cannot enter Phase X: Phase Y-1 not completed. Start from Phase Y or resume Phase Y-1."
awspricing unavailable after 3 attemptsDisplay user warning about ±5-25% accuracy. Use pricing-cache.md. Add pricing_source: "cached_fallback" to the applicable estimation-*.json file.
User skips questions or says "use defaults for the rest"Apply documented defaults for remaining questions in the current batch and all subsequent batches. Phase 2 completes either way.
aws-design.json missing required clustersStop Phase 4. Output: "Re-run Phase 3 to generate missing cluster designs."

Defaults

  • IaC output: Terraform configurations, migration scripts, AI migration code, and documentation

  • Region: us-east-1 (unless user specifies, or GCP region → AWS region mapping suggests otherwise)

  • Sizing: Development tier (e.g., db.t4g.micro for databases, 0.5 CPU for Fargate)

  • Migration mode: Adapts based on available inputs (infrastructure, AI, or billing-only)

  • Cost currency: USD

  • Timeline assumption: 2-16 weeks depending on migration complexity — small (2-6 weeks), medium (6-12 weeks), large (12-18 weeks). See references/shared/migration-complexity.md for tier definitions.

Workflow Execution

When invoked, the agent MUST follow this exact sequence:

  1. Load phase status: Read .phase-status.json from .migration/*/.

    • If missing: Initialize for Phase 1 (Discover)

    • If exists: Determine current phase using deterministic rules in State Machine

  2. Determine phase to execute:

    • If current_phase exists: execute that phase.

    • Otherwise execute the first non-completed phase in ordered list: discover → clarify → design → estimate → generate.

    • If all ordered phases are completed: migration is complete (with feedback finalization rule).

  3. Read phase reference: Load the full reference file for the target phase.

  4. Execute ALL steps in order: Follow every numbered step in the reference file. Do not skip, optimize, or deviate.

  5. Validate outputs: Confirm all required output files exist with correct schema before proceeding. Phase orchestrators run Completion Handoff Gate checks per shared/handoff-gates.md.

  6. Handoff gate: Emit HANDOFF_OK or GATE_FAIL per shared/handoff-gates.md. On GATE_FAIL, stop — do not update phase status or load the next phase.

  7. Update phase status: Only after HANDOFF_OK. Use the Phase Status Update Protocol (read-merge-write) in the same turn as the phase's final output message.

  8. Feedback checkpoint: After a phase completes, check if feedback is due (see rules below). This runs before advancing to the next phase.

    • After Discover (if phases.feedback is "pending"): Output to user: "Would you like to share quick feedback (5 optional questions + anonymized usage data) to help improve this tool? Your data never includes resource names, file paths, or account IDs. [A] Send feedback now [B] Wait until after the Estimate phase"

      • If user picks A → Load references/phases/feedback/feedback.md, execute it, then continue to Clarify.

      • If user picks B → Continue to Clarify (feedback stays "pending").

    • After Estimate (if phases.feedback is "pending"): Output to user: "Would you like to share quick feedback now? (5 optional questions + anonymized usage data) [A] Yes, share feedback [B] No thanks, continue to Generate"

      • If user picks A → Load references/phases/feedback/feedback.md, execute it, then continue to Generate.

      • If user picks B → Use the Phase Status Update Protocol to set phases.feedback to "completed". Continue to Generate.

    • After Generate: No feedback offer. If phases.feedback is still "pending", use the Phase Status Update Protocol to set it to "completed" (user had two chances and chose to defer/skip).

  9. Display summary: Show user what was accomplished, highlight next phase, or confirm migration completion.

Critical constraint: Agent must strictly adhere to the reference file's workflow. If unable to complete a step, stop and report the exact step that failed.

User can invoke the skill again to resume from current_phase (or deterministic ordered evaluation when current_phase is absent).

Scope Notes

v1.0 includes:

  • Terraform infrastructure discovery

  • App code scanning (AI workload detection)

  • Billing data import from GCP

  • User requirement clarification (adaptive questions by category)

  • Multi-path Design (infrastructure, AI workloads, billing-only fallback)

  • AWS cost estimation (from pricing API or fallback)

  • Migration artifact generation (Terraform, scripts, AI adapters, documentation)

  • Optional feedback collection with anonymized telemetry

Files

references/phases/generate/generate-infra.md

Generate Phase: Infrastructure Migration Plan

Loaded by generate.md when estimation-infra.json exists.

Execute ALL steps in order. Do not skip or optimize.

Prerequisites

Read the following artifacts from $MIGRATION_DIR/:

  • aws-design.json (REQUIRED) — AWS architecture design from Phase 3

  • estimation-infra.json (REQUIRED) — Cost estimates from Phase 4

  • gcp-resource-clusters.json (REQUIRED) — Cluster dependency graph from Phase 1

  • preferences.json (REQUIRED) — User migration preferences from Phase 2

If any required file is missing: STOP. Output: "Missing required artifact: [filename]. Complete the prior phase that produces it."

Part 1: Migration Timeline

Before building the timeline, load references/shared/migration-complexity.md and classify the migration tier using inputs from aws-design.json, gcp-resource-clusters.json, estimation-infra.json, and preferences.json.

Build a timeline using the Infrastructure Path ranges for the determined tier. Also consider:

  • Cluster count from gcp-resource-clusters.json — more clusters = longer infrastructure phase

  • Dependency depth from creation_order — deeper graphs need more sequential work

  • Service complexity from aws-design.json — databases and stateful services take longer

  • Cutover strategy from preferences.json — maintenance window vs. blue-green affects timeline

Small Tier (3-6 weeks)

Compressed setup. PoC is a 2-day smoke test integrated into deployment. No data migration stage (small tier excludes databases by definition).

Stage 1: Setup (Week 1)
  • Provision VPC, subnets, routing, IAM, monitoring baseline

  • Set up CI/CD pipeline for Terraform deployments

Stage 2: Deploy + Smoke Test (Week 2)
  • Deploy all clusters (few services, shallow dependency graph)

  • Validate application connectivity and performance

  • Run integration tests

  • Confirm cost tracking matches estimation-infra.json projections

  • Go/No-Go checkpoint: If smoke test fails acceptance criteria, stop and reassess

Stage 3: Cutover (Weeks 3-4)
  • Execute cutover per preferences.json cutover_strategy

  • Monitor for 24-48 hours post-cutover

  • Keep GCP resources running as hot standby

Stage 4: Validation + Cleanup (Weeks 5-6)
  • Monitor AWS performance for 1 week

  • Compare actual costs against projections

  • Begin GCP teardown planning (execute teardown after 2-week stability period)

Medium Tier (8-12 weeks)

Standard phased plan. Apply the data-migration skip rule below.

Stage 1: Setup (Weeks 1-2)
  • Finalize AWS account structure and billing alerts

  • Provision foundational infrastructure: VPC, subnets, routing, NAT gateways

  • Configure IAM roles and policies per cluster requirements

  • Establish GCP-to-AWS connectivity (VPN or Direct Connect for data migration)

  • Set up CI/CD pipeline for Terraform deployments

  • Configure monitoring baseline (CloudWatch, alarms)

Stage 2: Proof of Concept (Weeks 3-4)
  • Deploy the shallowest-depth cluster (lowest creation_order_depth) to AWS

  • Validate application connectivity and performance

  • Test data pipeline from GCP to AWS (database replication, storage sync)

  • Measure baseline latency and throughput

  • Confirm cost tracking matches estimation-infra.json projections

  • Go/No-Go checkpoint: If PoC fails acceptance criteria, stop and reassess

Stage 3: Infrastructure Deployment (Weeks 5-7)
  • Deploy remaining clusters in creation_order sequence (depth-first)

  • For each cluster:

    • Deploy infrastructure per aws-design.json resource mappings

    • Configure cross-cluster networking and security groups

    • Validate service health checks

    • Run integration tests

  • Implement monitoring and logging per cluster

  • Establish backup and restore procedures

Stage 4: Data Migration (Weeks 8-9)

Include this phase ONLY if aws-design.json contains database or storage resources (see resource detection rules in generate-artifacts-scripts.md Step 1). If no data migration is needed, compress the timeline: move Cutover to Weeks 8-9 and Validation to Week 10. Reduce total_weeks accordingly.

  • Databases: Set up continuous replication (Cloud SQL to RDS/Aurora)

    • Initial full snapshot transfer

    • Enable ongoing replication (DMS or native replication)

    • Validate data integrity (row counts, checksums)

  • Object storage: Sync GCS buckets to S3

    • Use AWS DataSync or gsutil/aws s3 sync for bulk transfer

    • Enable ongoing sync for new objects

  • Secrets: Migrate secrets from Secret Manager to AWS Secrets Manager

  • Establish dual-write pattern for production data

Stage 5: Cutover (Weeks 10-11, or Weeks 8-9 if Stage 4 skipped)
  • Pre-cutover validation:

    • All clusters deployed and healthy on AWS

    • Data replication lag < 1 second

    • All integration tests passing

    • Rollback procedures tested

  • Execute cutover per preferences.json cutover_strategy:

    • maintenance-window: Schedule downtime, switch DNS, validate

    • blue-green: Gradual traffic shift (10% → 50% → 100%)

  • Monitor for 24-48 hours post-cutover

  • Keep GCP resources running as hot standby

Stage 6: Validation and Cleanup (Week 12)
  • Monitor AWS performance for 1 full week

  • Compare actual costs against estimation-infra.json projections

  • Run final data integrity checks

  • Document any drift or issues

  • Begin GCP teardown planning (execute teardown after 2-week stability period)

Large Tier (12-16 weeks)

Extended timeline for complex migrations. Extra time for multi-cluster orchestration, large data volumes, and extended validation. Apply the data-migration skip rule from Stage 4.

Stage 1: Setup (Weeks 1-2)

Same as Medium.

Stage 2: Proof of Concept (Weeks 3-4)

Same as Medium.

Stage 3: Infrastructure Deployment (Weeks 5-8)

Extended by 1 week for additional clusters and complex cross-cluster networking.

  • Deploy remaining clusters in creation_order sequence (depth-first)

  • For each cluster:

    • Deploy infrastructure per aws-design.json resource mappings

    • Configure cross-cluster networking and security groups

    • Validate service health checks

    • Run integration tests

  • Implement monitoring and logging per cluster

  • Establish backup and restore procedures

Stage 4: Data Migration (Weeks 9-11)

Include this phase ONLY if aws-design.json contains database or storage resources. If no data migration is needed, compress the timeline: move Cutover to Weeks 9-10 and Validation to Weeks 11-12. Reduce total_weeks accordingly.

Extended for large data volumes and complex replication topologies.

  • Databases: Set up continuous replication (Cloud SQL to RDS/Aurora)

    • Initial full snapshot transfer

    • Enable ongoing replication (DMS or native replication)

    • Validate data integrity (row counts, checksums)

  • Object storage: Sync GCS buckets to S3

    • Use AWS DataSync or gsutil/aws s3 sync for bulk transfer

    • Enable ongoing sync for new objects

  • Secrets: Migrate secrets from Secret Manager to AWS Secrets Manager

  • Establish dual-write pattern for production data

Stage 5: Cutover (Weeks 12-13)

Same structure as Medium, adjusted week numbers.

Stage 6: Validation and Cleanup (Weeks 14-16)

Extended monitoring before GCP teardown.

  • Monitor AWS performance for 2 full weeks

  • Compare actual costs against estimation-infra.json projections

  • Run final data integrity checks

  • Document any drift or issues

  • Begin GCP teardown planning (execute teardown after 2-week stability period)

Part 2: Risk Assessment

Build a risk matrix from the discovered infrastructure and migration complexity.

Risk Categories

For each risk, assess:

  • Probability: "high" (>60%), "medium" (30-60%), "low" (<30%)

  • Impact: "critical" (service outage), "high" (degraded service), "medium" (delayed timeline), "low" (minor inconvenience)

  • Mitigation: Specific action to reduce probability or impact

Standard Risks
RiskProbabilityImpactMitigation
Data loss during migrationlowcriticalDual-write for 2 weeks before cutover; full backup before migration; checksums on all transfers
Performance regression on AWSmediumhighPoC testing (Weeks 3-4); load testing (Week 5); performance baseline comparison
Extended downtime during cutovermediumhighPractice cutover in staging; automate DNS switch; rollback procedure on standby
Cost overrun vs estimatesmediummediumSet billing alerts at 80% and 100% of projected; weekly cost review
Cross-region latencylowmediumValidate latency in PoC phase; consider same-region deployment for latency-sensitive services
Terraform state corruptionlowhighRemote state with locking (S3 + DynamoDB); state backups before each apply
Dynamic Risks

Add additional risks based on discovered infrastructure:

  • If databases with replication exist: Add "Replication lag during cutover" (medium/high)

  • If stateful services (Redis, Elasticsearch): Add "State migration data loss" (low/critical)

  • If multiple regions in GCP: Add "Cross-region dependency during migration" (medium/medium)

  • If AI workloads coexist: Add "Model performance drift on Bedrock" (medium/high)

  • If BigQuery resources exist (google_bigquery_* or aws_service is Deferred — specialist engagement in aws-design.json): Add "BigQuery migration complexity" (high/high) with mitigation: "No automated AWS analytics target — engage AWS account team and/or data analytics migration partner before architecture or cost commitments; plugin does not prescribe Athena/Redshift/Glue."

Part 3: Success Metrics

Per-Service Metrics

For each service in aws-design.json, define:

MetricTargetMeasurement
Availability99.9% uptimeCloudWatch synthetic monitoring
LatencyWithin 10% of GCP baselineP50, P95, P99 response times
Error rate< 0.1%CloudWatch error metrics
CostWithin 15% of estimation-infra.json projectionAWS Cost Explorer weekly
Overall Migration Metrics
MetricTarget
Data integrity100% — zero data loss confirmed by checksums
Migration timelineWithin 2 weeks of planned schedule
Rollback successTested and confirmed < 1 hour RTO
Team confidenceGo/No-Go passed at each phase gate

Part 4: Rollback Procedures

Trigger Conditions

Initiate rollback if ANY of:

  • Data integrity issues detected during validation (checksum mismatch)

  • Performance regression > 20% vs GCP baseline (P95 latency)

  • Error rate > 1% sustained for 15 minutes

  • Cost overrun > 50% vs estimation-infra.json projections

  • Critical AWS service limitation discovered that blocks functionality

Per-Phase Rollback
PhaseRollback ActionRTO
Setup (1-2)Delete AWS resources; no impact< 1 hour
PoC (3-4)Tear down PoC cluster; GCP unaffected< 1 hour
Infrastructure (5-7)Tear down AWS clusters; GCP still primary< 2 hours
Data Migration (8-9)Stop replication; GCP data is authoritative< 30 minutes
Cutover (10-11)Reverse DNS to GCP; resume GCP traffic< 1 hour
Post-Cutover (12+)Manual restore from backup; GCP resources archived2-4 hours
Rollback Procedure (Pre-DNS Cutover)
  1. Pause dual-write replication

  2. Reverse DNS records (AWS to GCP endpoints)

  3. Verify GCP services receiving traffic

  4. Shut down AWS workloads (keep for 1 week as standby)

  5. Monitor GCP for 24 hours

  6. Post-mortem: document what triggered rollback

Rollback Procedure (Post-DNS Cutover)
  1. If within 48 hours: Reverse DNS, resume GCP from replicated state

  2. If beyond 48 hours: Restore GCP from backup (2-4 hour RTO)

  3. Validate data integrity after restore

  4. Resume GCP operations

  5. Post-mortem and reassess migration approach

Part 5: Team and Roles

RACI Matrix
ActivityMigration LeadInfrastructure EngineerDatabase EngineerApplication DeveloperQA Engineer
Migration planningA/RCCCI
AWS account setupARIII
Network infrastructureARIII
Database migrationACRIC
Application deploymentACIRC
Data validationAIRCR
Performance testingACCCR
Cutover executionRRRCC
Rollback executionRRRII

R = Responsible, A = Accountable, C = Consulted, I = Informed

Part 6: Go/No-Go Framework

Each phase gate requires explicit approval before proceeding.

Phase Gate Criteria
GatePhase TransitionGo CriteriaNo-Go Action
G1Setup → PoCVPC online, IAM configured, connectivity verifiedFix infrastructure issues
G2PoC → Full DeployPoC cluster healthy, latency acceptable, costs trackingReassess architecture
G3Full Deploy → Data MigrationAll clusters deployed, integration tests passingDebug failing clusters
G4Data Migration → CutoverReplication lag < 1s, data integrity confirmed, rollback testedFix replication issues
G5Cutover → ValidationDNS switched, traffic flowing, error rate < 0.1%Execute rollback
G6Validation → Cleanup1 week stable operation, costs within 15% of estimateExtend monitoring period

Part 7: Post-Migration Monitoring

30-Day Post-Migration Plan

Week 1 (Days 1-7): Intensive monitoring

  • Check all CloudWatch dashboards every 4 hours

  • Compare latency/throughput to GCP baseline daily

  • Review AWS costs daily vs projections

  • Maintain GCP hot standby

Week 2 (Days 8-14): Stabilization

  • Reduce monitoring frequency to twice daily

  • Begin decommissioning GCP hot standby (read-only mode)

  • Address any performance tuning items

  • Validate backup/restore procedures on AWS

Weeks 3-4 (Days 15-30): Optimization

  • Implement cost optimization opportunities from estimation-infra.json

  • Right-size instances based on actual usage data

  • Enable Reserved Instances or Savings Plans for stable workloads

  • Archive GCP resources and begin teardown

  • Final cost reconciliation: actual vs projected

Part 8: Decision Tree

Cutover Decision Flowchart
START: All clusters deployed?
  NO → Continue infrastructure deployment
  YES → Data replication active?
    NO → Start data migration
    YES → Replication lag < 1 second?
      NO → Wait for replication to catch up
      YES → All integration tests passing?
        NO → Fix failing tests
        YES → Rollback procedure tested?
          NO → Execute rollback drill
          YES → READY FOR CUTOVER
            → Execute cutover per strategy
            → Monitor 24-48 hours
            → Error rate < 0.1%?
              NO → EXECUTE ROLLBACK
              YES → CUTOVER SUCCESSFUL
                → Begin 30-day monitoring
                → Plan GCP teardown (Week 14)

Part 9: Output Format

Generate generation-infra.json in $MIGRATION_DIR/ with the following schema:

{
  "phase": "generate",
  "generation_source": "infrastructure",
  "complexity_tier": "medium",
  "complexity_inputs": {
    "service_count": 5,
    "monthly_spend": 3500.00,
    "has_databases": true,
    "has_stateful_storage": false,
    "has_ai_workloads": false,
    "availability": "single-az",
    "compliance": "none",
    "multi_region": false
  },
  "timestamp": "2026-02-26T14:30:00Z",
  "migration_plan": {
    "total_weeks": 12,
    "phases": [
      {
        "name": "Setup",
        "weeks": "1-2",
        "clusters_targeted": [],
        "key_activities": ["AWS account setup", "VPC provisioning", "IAM configuration"],
        "dependencies": [],
        "go_no_go_criteria": "VPC online, IAM configured, connectivity verified"
      }
    ],
    "services": [
      {
        "gcp_service": "Cloud Run",
        "aws_service": "Fargate",
        "migration_week": 5,
        "cluster_id": "compute_cloudrun_us-central1_001",
        "estimated_effort_hours": 40,
        "data_migration_required": false, // derive from resource detection flags (has_databases, has_storage)
        "human_expertise_required": false // propagate from aws-design.json; true for BigQuery mappings
      }
    ],
    "critical_path": [
      "VPC setup (Week 1)",
      "PoC deployment (Week 3-4)",
      "Database replication (Week 8-9)",
      "DNS cutover (Week 10-11)"
    ],
    "dependencies": [
      {
        "from_cluster": "networking_vpc_us-central1_001",
        "to_cluster": "compute_cloudrun_us-central1_001",
        "type": "must_precede"
      }
    ]
  },
  "risks": [
    {
      "category": "data_loss",
      "probability": "low",
      "impact": "critical",
      "mitigation": "Dual-write for 2 weeks; full backup before cutover; checksum validation",
      "phase_affected": "Data Migration"
    }
  ],
  "success_metrics": {
    "per_service": [
      {
        "service": "Fargate",
        "availability_target": "99.9%",
        "latency_target": "within 10% of GCP baseline",
        "cost_target": "within 15% of estimation"
      }
    ],
    "overall": {
      "data_integrity": "100%",
      "timeline_variance": "within 2 weeks",
      "rollback_rto": "< 1 hour"
    }
  },
  "rollback_procedures": {
    "trigger_conditions": [
      "Data integrity failure",
      "Performance regression > 20%",
      "Error rate > 1% sustained 15 min",
      "Cost overrun > 50%"
    ],
    "pre_cutover_rto": "< 1 hour",
    "post_cutover_rto": "2-4 hours",
    "rollback_window": "Reversible until 48 hours post-DNS cutover"
  },
  "go_no_go_criteria": [
    {
      "gate": "G1",
      "transition": "Setup to PoC",
      "criteria": "VPC online, IAM configured, connectivity verified"
    }
  ],
  "post_migration": {
    "monitoring_duration_days": 30,
    "gcp_teardown_week": 14,
    "optimization_start_week": 15
  },
  "recommendation": {
    "approach": "Phased cluster-by-cluster migration",
    "confidence": "high",
    "key_risks": ["Data migration complexity", "Performance validation"],
    "estimated_total_effort_hours": 480
  }
}

Output Validation Checklist

  • phase is "generate"

  • generation_source is "infrastructure"

  • complexity_tier is one of "small", "medium", "large"

  • complexity_inputs object is present with all required fields (service_count, monthly_spend, has_databases, has_stateful_storage, has_ai_workloads, availability, compliance, multi_region)

  • migration_plan.total_weeks is within the tier's allowed range: small 3-6, medium 8-12, large 12-16

  • migration_plan.phases array has at least 4 entries; stage names match the tier template from Part 1

  • migration_plan.services covers every service from aws-design.json

  • migration_plan.critical_path is non-empty

  • migration_plan.dependencies reflect gcp-resource-clusters.json creation_order

  • risks array has at least 3 entries with probability, impact, mitigation

  • success_metrics has both per_service and overall sections

  • rollback_procedures has trigger conditions and RTO values

  • go_no_go_criteria has at least 2 gates (small) or 4 gates (medium/large)

  • post_migration specifies monitoring duration and teardown timing

  • All cluster IDs reference valid clusters from gcp-resource-clusters.json

  • Output is valid JSON

Completion Handoff Gate (Fail Closed)

Before returning control to generate.md, require:

  • generation-infra.json exists and passes the Output Validation Checklist above.

If this gate fails: STOP and output: "generate-infra did not produce a valid generation-infra.json; do not continue Generate Stage 2."

Generate Phase Integration

The parent orchestrator (generate.md) uses generation-infra.json to:

  1. Gate Stage 2 artifact generation — generate-artifacts-infra.md requires this file

  2. Provide timeline context to generate-artifacts-docs.md for MIGRATION_GUIDE.md

  3. Set phase completion status in .phase-status.json

Requirements and compatibility

Written for: Claude Code

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Names Claude Code tools, Needs an MCP server

Security

What our scanner noticed· Low risk

  • Instructions that use the network

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Attribution and licence

Published by
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Source
cline/skills · skills/gcp-to-aws · commit 2637846
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Apache License 2.0 (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
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