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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/clarify/clarify-global.md

Category A — Global/Strategic (Always Fires)

These foundational constraints gate everything downstream — region selection, service catalog, data residency, credits eligibility, compute platform, availability topology, and migration strategy.

Present questions with a conversational tone and brief context explaining why each matters.


Q1 — Where are your users located?

Auto-extract signal: When gcp-resource-inventory.json shows a single GCP region among PRIMARY compute/database resources, map to the closest AWS region and skip Q1 with target_region chosen_by: "extracted". When multiple regions are present, suggest the closest AWS region as default but still ask Q1.

Rationale: Geography drives AWS region selection and CDN strategy.

I need to understand your user base to recommend the right AWS region and CDN strategy.

A) Single region (e.g., US-only, EU-only) B) Multi-region (2–3 regions, e.g., US + EU) C) Global (users worldwide, latency critical) D) I don't know

AnswerRecommendation Impact
Single regionDeploy in closest AWS region to users; standard Route 53 routing
Multi-regionPrimary region closest to majority; CloudFront for static assets and API caching; Route 53 latency-based routing — multi-region infrastructure deferred to Q6
GlobalPrimary region by largest user concentration; CloudFront globally distributed; Route 53 geolocation routing — Aurora Global Database and multi-region compute only if Q6 = Catastrophic AND write latency is a confirmed hard requirement

Interpret:

A -> target_region: "<closest AWS region to GCP region in inventory>"
B -> target_region: "<closest AWS region>", replication: "cross-region"
C -> target_region: "<closest AWS region>", replication: "cross-region", cdn: "required"
D -> same as default (A)

Default: A — single region, closest AWS region to GCP region in inventory.


Q2 — Do you have any compliance or regulatory requirements?

Rationale: Compliance requirements gate entire service categories and regions. A HIPAA customer cannot use the same architecture as an unconstrained startup.

Compliance requirements determine which AWS services, regions, and configurations are available to you. This gates the entire architecture.

A) None — No specific compliance requirements B) SOC 2 / ISO 27001 — Security and availability standards C) PCI DSS — Payment card data handling D) HIPAA — Healthcare data E) FedRAMP / Government — Federal compliance F) GDPR / Data residency — EU data sovereignty requirements G) CCPA / CPRA — California Consumer Privacy Act / California Privacy Rights Act H) I don't know

(Multiple selections allowed)

AnswerRecommendation Impact
NoneFull service catalog available, any region
SOC 2 / ISO 27001CloudTrail, Config, Security Hub enabled by default; encryption at rest required
PCI DSSCloudTrail, Config, Security Hub + PCI DSS standard enabled by default; dedicated VPC with strict segmentation; WAF required; no shared tenancy for cardholder data; specific RDS encryption config
HIPAACloudTrail, Config, Security Hub (FSBP only — Security Hub does not provide a HIPAA-specific standard) enabled by default; BAA-eligible services only; encryption in transit and at rest mandatory; specific logging requirements; us-east-1/us-west-2 preferred; engage a qualified HIPAA auditor for end-to-end posture validation
FedRAMPCloudTrail, Config, Security Hub (FSBP only — NIST 800-53 is the target control set but is not directly subscribable in Security Hub the way PCI DSS is; engage your AWS account team for agency-level attestation) enabled by default; GovCloud regions required (us-gov-east-1, us-gov-west-1); GovCloud-specific service endpoints; limited service catalog
GDPREU regions required (eu-west-1, eu-central-1), data residency constraints, no cross-region replication outside EU without explicit consent
CCPA / CPRAConsumer privacy posture: data inventory, access/deletion workflows, opt-out of sale/sharing where applicable, retention minimization, encryption and audit logging (CloudTrail); prefer documenting data flows and subprocessors — confirm target regions with legal/compliance (often US)

Interpret:

A -> (no constraint written — full service catalog available, any region)
B -> compliance: ["soc2"] — CloudTrail, Config, Security Hub enabled; encryption at rest required
C -> compliance: ["pci"] — Dedicated VPC, WAF required, strict segmentation
D -> compliance: ["hipaa"] — BAA-eligible services only, encryption mandatory, us-east-1/us-west-2 preferred
E -> compliance: ["fedramp"] — GovCloud regions required (us-gov-east-1, us-gov-west-1)
F -> compliance: ["gdpr"] — EU regions required (eu-west-1, eu-central-1), data residency constraints
G -> compliance: ["ccpa"] — CCPA/CPRA: logging, retention, consumer-request readiness; document data flows; align region/subprocessor choices with legal review
H -> same as default (A) — no constraint assumed; verify with compliance team before production

Default: A — no constraint.


Q3 — Approximately how much are you spending on GCP per month in total?

Auto-extract signal: If billing-profile.json exists, map summary.total_monthly_spend to the spend band below and skip Q3 when unambiguous (chosen_by: "extracted"). If billing is absent or ambiguous, ask Q3.

Monthly USDgcp_monthly_spend
< 1,000"<$1K"
1,000–4,999"$1K-$5K"
5,000–19,999"$5K-$20K"
20,000–99,999"$20K-$100K"
≥ 100,000">$100K"

Rationale: Total GCP spend is the primary input for ARR estimation, which determines credits eligibility tier. Also provides a sanity check for cost estimates when billing data is not uploaded.

Total GCP spend helps me estimate AWS credits eligibility and provides a cost baseline for the migration plan.

A) < $1,000/month B) $1,000–$5,000/month C) $5,000–$20,000/month D) $20,000–$100,000/month E) > $100,000/month F) I don't know

Billing enrichment (when Q3 is not skipped): If billing-profile.json exists but extraction was skipped due to ambiguity, show:

Your billing data shows ~$[total_monthly_spend]/month. Does this match your expectation?

AnswerRecommendation Impact
< $1,000/monthEntry-tier migration funding programs may apply; cost estimates use conservative ranges
$1,000–$5,000/monthMigration funding review may apply; cost estimates use mid-range assumptions
$5,000–$20,000/monthMigration funding review may apply; reserved pricing options are evaluated in cost recommendations
$20,000–$100,000/monthMigration funding and support program review may apply; savings commitment options are evaluated
> $100,000/monthEnterprise migration program review may apply; dedicated migration support path may be recommended

Interpret:

A -> gcp_monthly_spend: "<$1K" — entry-tier funding review; conservative cost assumptions
B -> gcp_monthly_spend: "$1K-$5K" — funding review; mid-range cost assumptions
C -> gcp_monthly_spend: "$5K-$20K" — funding review; reserved pricing recommendations
D -> gcp_monthly_spend: "$20K-$100K" — funding/support review; savings commitment analysis
E -> gcp_monthly_spend: ">$100K" — enterprise program/support review
F -> same as default (B)

Default: B — gcp_monthly_spend: "$1K-$5K".


Q3.5 — Do you have active GCP Committed Use Discounts (CUDs)?

Rationale: Active CUDs affect migration timing and cost comparison accuracy. If a customer has unexpired CUDs, they'll continue paying commitment fees even after migrating — this is a sunk cost that affects the migration ROI timeline. Also determines whether to compare against GCP list price or committed rate.

Conditional: Only ask if billing-profile.json exists AND commitments.has_active_cuds == true. If billing data shows active CUDs, present the detected information and ask for confirmation/details.

Your billing data shows active Committed Use Discounts (~[effective_discount_percent]% effective discount). CUD timing affects migration ROI — commitment fees continue regardless of usage until the term expires.

A) Yes, and they expire within 6 months B) Yes, and they expire in 6–12 months C) Yes, and they have more than 12 months remaining D) Yes, but I'm not sure when they expire E) No active CUDs / I don't know F) I plan to let them expire and not renew

AnswerRecommendation Impact
Expire within 6 monthsMigration timing favorable — plan migration to coincide with CUD expiration for clean cost transition
Expire in 6–12 monthsConsider phased migration starting now; some overlap cost is acceptable for operational benefits
More than 12 months remainingFactor CUD overlap cost into ROI analysis; migration still viable if operational benefits justify dual-payment period
Not sure when they expireRecommend customer check GCP console (Billing → Commitments) before finalizing migration timeline
No active CUDsNo commitment overlap concern; migrate on any timeline
Plan to let them expireAlign migration completion with CUD expiration date for optimal cost transition

Interpret:

A -> cud_status: "expiring_soon" — Align migration with CUD expiration
B -> cud_status: "expiring_medium" — Phased migration acceptable; some overlap cost
C -> cud_status: "long_remaining" — Factor overlap into ROI; justify with operational benefits
D -> cud_status: "unknown_expiry" — Recommend checking GCP console
E -> cud_status: "none" — No constraint
F -> cud_status: "not_renewing" — Align migration completion with expiration

Default: E — cud_status: "none" (no constraint on migration timing).

If billing-profile.json does not exist or commitments.has_active_cuds == false, skip this question entirely.


Q4 — (Skipped)

Credits program eligibility is inferred from Q3 (GCP spend) alone. No question asked.

Default: funding_stage: not set.


Q5 — Do you need to run workloads across multiple cloud providers?

Rationale: Multi-cloud portability is an early exit condition that immediately determines the compute recommendation without needing further questions. If multi-cloud is required, Kubernetes (EKS) is the only portable abstraction layer.

Multi-cloud portability is an immediate decision point — if required, Kubernetes (EKS) is the only portable abstraction, and we can skip several compute questions.

A) Yes, multi-cloud required B) No, AWS-only is acceptable C) I don't know

AnswerRecommendation Impact
Yes, multi-cloud requiredImmediate EKS recommendation — Kubernetes is the only portable abstraction layer. Skip Q8. ECS Fargate excluded.
No, AWS-only acceptableFull compute decision tree continues — EKS vs ECS Fargate evaluated based on K8s sentiment (Q8)

Interpret:

A -> compute: "eks" — Immediate EKS recommendation. EARLY EXIT: skip Q8.
B -> (no constraint written — full compute decision tree continues)
C -> same as default (B) — assume AWS-only

Default: B — no constraint, evaluate full compute options.


Q6 — If your application went down unexpectedly right now, what would happen?

Auto-extract signal (Cloud SQL PostgreSQL/MySQL only): Read availability_type from google_sql_database_instance (config.availability_type or top-level). When unambiguous:

GCP valueavailability extractedSkip Q6?
ZONAL"single-az"Yes — chosen_by: "extracted"
REGIONAL"multi-az"Yes — chosen_by: "extracted"

Never auto-extract: multi-az-ha and multi-region require Q6 user answers (Mission-Critical / Catastrophic) — IaC cannot infer these. Cloud SQL REGIONAL is RDS Multi-AZ (multi-az), not Aurora (multi-az-ha). Skip Q6 only when all instances agree. When instances disagree or availability_type is missing on any instance, ask Q6.

Rationale: Availability requirements drive database engine selection, deployment topology, and whether multi-AZ is mandatory. Aurora Global Database and multi-region compute are only recommended when Catastrophic is selected AND Q1 confirms global users — both signals are required.

Cloud SQL PostgreSQL / MySQL → RDS vs Aurora (decision order): For customers on Cloud SQL (PostgreSQL or MySQL), Q6 is the only question that selects the AWS product family — RDS (PostgreSQL or MySQL, matching the Cloud SQL engine) vs Aurora (Aurora PostgreSQL or Aurora MySQL). Q12–Q13 never override Q6; they tune sizing, replicas, storage/I/O billing, and Aurora variants after Q6 has chosen RDS or Aurora. When Cloud SQL is detected, you may add: "For dev/staging or workloads where brief outage is tolerable, RDS PostgreSQL is usually simpler and cheaper; Aurora is for mission-critical HA needs."

Context for user: When asking, include these descriptions so the user can self-select accurately:

  • Inconvenient — users can wait, no revenue impact (e.g., internal tool, dev/staging environment, hobby project)

  • Significant Issue — users notice and complain, some revenue impact, but workarounds exist (e.g., B2B SaaS with email support SLA)

  • Mission-Critical — direct revenue loss per minute of downtime, SLA obligations to customers, needs fast recovery (e.g., e-commerce checkout, paid API)

  • Catastrophic — regulatory, safety, or major financial consequences; every minute of downtime is measurable loss (e.g., financial transactions, healthcare systems, real-time trading)

Availability requirements drive database engine selection, deployment topology, and whether multi-AZ is mandatory.

A) INCONVENIENT — Users can wait, brief outages tolerable (5–30 min) B) SIGNIFICANT ISSUE — Customers frustrated, revenue loss C) MISSION-CRITICAL — Cannot tolerate outages, SLA violations D) CATASTROPHIC — Regulatory, safety, or major financial consequences per minute of downtime E) I don't know

AnswerRecommendation Impact
InconvenientSingle-AZ RDS acceptable, standard ECS/EKS deployment, no special HA requirements
Significant IssueMulti-AZ RDS required, ALB with health checks, auto-scaling groups
Mission-CriticalAurora Multi-AZ (higher availability than RDS), multi-AZ mandatory, Route 53 health checks; single-region with fast failover is sufficient for most mission-critical workloads
CatastrophicIf Q1 = Global: Aurora Global Database + active-active multi-region + Route 53 failover routing; If Q1 = Single/Multi-region: Aurora Multi-AZ with aggressive RTO/RPO targets is sufficient — global infrastructure not warranted without global users

Interpret:

A -> availability: "single-az" — Single-AZ RDS acceptable, standard deployment
B -> availability: "multi-az" — Multi-AZ RDS required, ALB with health checks, auto-scaling
C -> availability: "multi-az-ha" — Aurora Multi-AZ, multi-AZ mandatory, Route 53 health checks
D -> IF Q1 = C (Global): availability: "multi-region" — Aurora Global Database + active-active multi-region + Route 53 failover
     IF Q1 = A or B: availability: "multi-az-ha" — Aurora Multi-AZ with aggressive RTO/RPO (global infra not warranted without global users)
E -> same as default (B) — assume multi-AZ for safety

Default: B — availability: "multi-az".


Q7 — Do you have a scheduled maintenance window where downtime is acceptable?

Rationale: Determines cutover strategy and which database migration tooling is recommended. Zero-downtime migrations require significantly more complex infrastructure (blue/green, traffic shifting). With a maintenance window, databases can be taken offline briefly and migrated with native tools — without one, live replication via DMS is required.

Database migration tooling notes:

  • Read preferences.json → design_constraints.db_size.value (set by Q13b in clarify-database.md) to select the right tool. If absent, fall back to the size thresholds below.

  • For PostgreSQL databases db_size: "<10GB" or unknown-small: pg_dump/pg_restore is sufficient.

  • For PostgreSQL databases db_size: "10-100GB" or "100-500GB": pgcopydb offers parallel table copying and index rebuilding, significantly reducing migration time within the same maintenance window.

  • For PostgreSQL databases db_size: ">500GB": AWS DMS strongly recommended regardless of maintenance window — single-pass export/import at this scale is high-risk.

  • pgcopydb's CDC mode requires wal_level=logical on Cloud SQL, which must be enabled explicitly.

The maintenance window determines your migration cutover strategy and which database migration tooling we recommend. Zero-downtime migrations require significantly more complex infrastructure.

A) Yes — weekly maintenance window (e.g., Sunday 2–4am) B) Yes — monthly maintenance window only C) No — zero downtime required, must use blue/green or rolling deployment D) Flexible — we can schedule one if needed E) I don't know

AnswerRecommendation Impact
Weekly windowStandard cutover with DNS switchover during window; pg_dump/pg_restore for PostgreSQL <10GB; pgcopydb for larger databases — parallel copying cuts migration time significantly; no DMS licensing, no replication lag risk
Monthly windowCutover timed to monthly window; pg_dump/pg_restore or pgcopydb depending on DB size; blue/green for application layer
Zero downtimeAWS DMS required for live database replication; blue/green deployment for application layer; RDS blue/green deployments (RDS path per Q6) or Aurora blue/green deployments (Aurora path per Q6); Route 53 weighted routing for traffic shifting
FlexibleRecommend scheduling a weekly window to enable pg_dump/pgcopydb approach; falls back to DMS if window cannot be arranged

Interpret:

A -> cutover_strategy: "maintenance-window-weekly" — pg_dump/pg_restore or pgcopydb recommended; standard cutover with DNS switchover
B -> cutover_strategy: "maintenance-window-monthly" — pg_dump/pg_restore or pgcopydb recommended; blue/green for app layer
C -> cutover_strategy: "zero-downtime" — AWS DMS required for live DB replication; blue/green deployment; Route 53 weighted routing
D -> cutover_strategy: "flexible" — Recommend scheduling weekly window for pg_dump approach; DMS fallback
E -> same as default (D) — assume flexible

Default: D — cutover_strategy: "flexible".

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

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Needs: Names Claude Code tools, Needs an MCP server

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