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
Download the skill as a ZIP:
https://autogineer.com/agent-skills/cline/gcp-to-aws/downloadUnzip 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 (IaCgoogle_bigquery_*and/or billing rows for BigQuery), you must surface the specialist advisory before Design (seereferences/phases/clarify/clarify.md). Design output usesDeferred — specialist engagement; keep directing the user to their AWS account team and/or a data analytics migration partner through Design, Estimate, and docs (seereferences/phases/design/design-infra.mdBigQuery 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):
| File | Condition |
|---|---|
design-refs/ai-gemini-to-bedrock.md | ai-workload-profile.json exists AND summary.ai_source = "gemini" or "both" |
design-refs/ai-openai-to-bedrock.md | ai-workload-profile.json exists AND summary.ai_source = "openai" or "both" |
design-refs/ai-anthropic-to-bedrock.md | ai-workload-profile.json exists AND summary.ai_source = "anthropic" |
design-refs/ai.md | ai-workload-profile.json exists AND summary.ai_source = "other" |
design-refs/design-ref-harness.md | agentic_profile.is_agentic == true AND ai_constraints.agentic.migration_approach == "harness" |
design-refs/design-ref-agentic-to-agentcore.md | agentic_profile.is_agentic == true AND ai_constraints.agentic.migration_approach == "strands" |
shared/retarget-gotchas.md | agentic_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:
.tffiles (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 State | Condition | Next Action |
|---|---|---|
discover | phases.discover != "completed" | Load references/phases/discover/discover.md |
clarify | phases.discover == "completed" AND phases.clarify != "completed" | Load references/phases/clarify/clarify.md |
design | phases.clarify == "completed" AND phases.design != "completed" | Load references/phases/design/design.md |
estimate | phases.design == "completed" AND phases.estimate != "completed" | Load references/phases/estimate/estimate.md |
generate | phases.estimate == "completed" AND phases.generate != "completed" | Load references/phases/generate/generate.md |
complete | phases.generate == "completed" AND phases.feedback == "pending" | Set phases.feedback to "completed" (user had two chances), then migration complete |
complete | phases.generate == "completed" AND phases.feedback == "completed" | Migration planning complete |
How to determine current state (deterministic):
Read
$MIGRATION_DIR/.phase-status.jsonIf
current_phaseexists, use it (must match one of: discover, clarify, design, estimate, generate, complete)Otherwise use ordered phase evaluation:
discover→clarify→design→estimate→generatePick the first phase in that order where
phases.<phase> != "completed"; if none, state iscomplete
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.
Single
$MIGRATION_DIR: Use one run directory for the entire migration. Do not mix artifacts across.migration/*/sessions.Re-read from disk: Before each phase (and before each handoff gate), Read required artifacts from
$MIGRATION_DIR/. Do not rely on chat memory.Advance only on
HANDOFF_OK: A phase is complete only when its orchestrator emitsHANDOFF_OK | phase=<name> | artifacts=.... Do not load the next phase without it.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.Re-entry: Re-running an earlier phase after downstream phases completed requires explicit user confirmation; downstream phases must be reset to
"pending". Seehandoff-gates.mdre-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:
Multiple sessions: If multiple directories exist under
.migration/, list them with their phase status and ask: [A] Resume latest, [B] Start fresh, [C] Cancel.Invalid JSON: If
.phase-status.jsonfails to parse, STOP. Output: "State file corrupted (invalid JSON). Delete the file and restart the current phase."Unrecognized phase: If
phasesobject contains a phase not in {discover, clarify, design, estimate, generate, feedback}, STOP. Output: "Unrecognized phase: [value]. Valid phases: discover, clarify, design, estimate, generate, feedback."Unrecognized status: If any
phases.*value is not in {pending, in_progress, completed}, STOP. Output: "Unrecognized status: [value]. Valid values: pending, in_progress, completed."Invalid
current_phase(if present): Ifcurrent_phaseis not in {discover, clarify, design, estimate, generate, complete}, STOP. Output: "Unrecognized current_phase: [value]. Valid values: discover, clarify, design, estimate, generate, complete."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.jsonbefore resuming."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:
Read the current file before every update.
Change only the phase keys being advanced and
last_updated.Keep prior completed phases unchanged.
Set
current_phaseto the next deterministic phase (orcompleteafter generate).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
| Phase | Inputs | Outputs | Reference |
|---|---|---|---|
| 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 |
| Clarify | Discovery artifacts (gcp-resource-inventory.json, gcp-resource-clusters.json, ai-workload-profile.json, billing-profile.json — whichever exist) | preferences.json, .phase-status.json updated | references/phases/clarify/clarify.md |
| Design | preferences.json + discovery artifacts | aws-design.json (infra), aws-design-ai.json (AI), aws-design-billing.json (billing-only) | references/phases/design/design.md |
| Estimate | aws-design.json or aws-design-billing.json or aws-design-ai.json, preferences.json | estimation-infra.json or estimation-ai.json or estimation-billing.json, .phase-status.json updated | references/phases/estimate/estimate.md |
| Generate | estimation-infra.json or estimation-ai.json or estimation-billing.json, aws-design.json or aws-design-billing.json or aws-design-ai.json, preferences.json | generation-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 updated | references/phases/generate/generate.md |
| Feedback | .phase-status.json (discover completed minimum), all existing migration artifacts | feedback.json, trace.json, .phase-status.json updated | references/phases/feedback/feedback.md |
MCP Servers
awspricing (for cost estimation):
Provides
get_pricing,get_pricing_service_codes,get_pricing_service_attributestoolsOnly 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| Condition | Action |
|---|---|
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 gate | Stop. Output: "Cannot enter Phase X: Phase Y-1 not completed. Start from Phase Y or resume Phase Y-1." |
| awspricing unavailable after 3 attempts | Display 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 clusters | Stop 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.microfor 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.mdfor tier definitions.
Workflow Execution
When invoked, the agent MUST follow this exact sequence:
Load phase status: Read
.phase-status.jsonfrom.migration/*/.If missing: Initialize for Phase 1 (Discover)
If exists: Determine current phase using deterministic rules in State Machine
Determine phase to execute:
If
current_phaseexists: 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).
Read phase reference: Load the full reference file for the target phase.
Execute ALL steps in order: Follow every numbered step in the reference file. Do not skip, optimize, or deviate.
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.Handoff gate: Emit
HANDOFF_OKorGATE_FAILpershared/handoff-gates.md. OnGATE_FAIL, stop — do not update phase status or load the next phase.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.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.feedbackis"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.feedbackis"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.feedbackto"completed". Continue to Generate.
After Generate: No feedback offer. If
phases.feedbackis still"pending", use the Phase Status Update Protocol to set it to"completed"(user had two chances and chose to defer/skip).
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
- ATTRIBUTION.mdAdded by AutoGineer826 B
- LICENSELicence file added by AutoGineer10.5 KiB
- SKILL.md29.6 KiB
- data/sdk-capability-map.json1.1 KiB
- references/clustering/terraform/classification-rules.md6.5 KiB
- references/clustering/terraform/clustering-algorithm.md7.9 KiB
- references/clustering/terraform/depth-calculation.md4.5 KiB
- references/clustering/terraform/typed-edges-strategy.md4.6 KiB
- references/design-refs/ai-anthropic-to-bedrock.md2.9 KiB
- references/design-refs/ai-gemini-to-bedrock.md15.4 KiB
- references/design-refs/ai-openai-to-bedrock.md17.5 KiB
- references/design-refs/ai.md3.4 KiB
- references/design-refs/compute.md6.8 KiB
- references/design-refs/database.md9.1 KiB
- references/design-refs/design-ref-agentic-to-agentcore.md18.8 KiB
- references/design-refs/design-ref-harness.md7.9 KiB
- references/design-refs/fast-path.md10.3 KiB
- references/design-refs/index.md6.2 KiB
- references/design-refs/messaging.md3.5 KiB
- references/design-refs/networking.md5.5 KiB
- references/design-refs/security.md3.8 KiB
- references/design-refs/storage.md4.7 KiB
- references/phases/clarify/clarify-ai-only.md16.4 KiB
- references/phases/clarify/clarify-ai.md57.7 KiB
- references/phases/clarify/clarify-compute.md10.0 KiB
- references/phases/clarify/clarify-database.md10.8 KiB
- references/phases/clarify/clarify-global.md23.7 KiB
- references/phases/clarify/clarify.md50.5 KiB
- references/phases/design/design-ai.md29.6 KiB
- references/phases/design/design-billing.md12.7 KiB
- references/phases/design/design-infra.md12.8 KiB
- references/phases/design/design.md4.5 KiB
- references/phases/discover/discover-app-code.md41.7 KiB
- references/phases/discover/discover-billing.md7.2 KiB
- references/phases/discover/discover-iac.md33.0 KiB
- references/phases/discover/discover-preview.md19.9 KiB
- references/phases/discover/discover.md11.5 KiB
- references/phases/estimate/estimate-ai.md17.2 KiB
- references/phases/estimate/estimate-billing.md12.4 KiB
- references/phases/estimate/estimate-infra.md46.6 KiB
- references/phases/estimate/estimate.md8.1 KiB
- references/phases/feedback/feedback-trace.md4.5 KiB
- references/phases/feedback/feedback.md3.2 KiB
- references/phases/generate/generate-ai.md13.5 KiB
- references/phases/generate/generate-artifacts-ai.md33.6 KiB
- references/phases/generate/generate-artifacts-billing.md6.1 KiB
- references/phases/generate/generate-artifacts-docs.md21.0 KiB
- references/phases/generate/generate-artifacts-infra.md43.2 KiB
- references/phases/generate/generate-artifacts-report.md40.1 KiB
- references/phases/generate/generate-artifacts-scripts.md14.5 KiB
- references/phases/generate/generate-billing.md16.5 KiB
- references/phases/generate/generate-infra.md22.0 KiB
- references/phases/generate/generate.md7.9 KiB
- references/shared/ai-migration-guardrails.md6.6 KiB
- references/shared/ai-model-lifecycle.md8.8 KiB
- references/shared/bedrock-quotas.md7.1 KiB
- references/shared/handoff-gates.md4.7 KiB
- references/shared/migration-complexity.md11.0 KiB
- references/shared/pricing-cache.md41.2 KiB
- references/shared/pricing-fallback.md1.5 KiB
- references/shared/retarget-gotchas.md10.9 KiB
- references/shared/schema-discover-ai.md16.7 KiB
- references/shared/schema-discover-billing.md5.0 KiB
- references/shared/schema-discover-iac.md6.9 KiB
- references/shared/schema-estimate-infra.md15.6 KiB
- references/shared/schema-phase-status.md1.2 KiB
- references/shared/terraform-validation.md10.4 KiB
- references/shared/validate-artifacts.md6.2 KiB
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 3estimation-infra.json(REQUIRED) — Cost estimates from Phase 4gcp-resource-clusters.json(REQUIRED) — Cluster dependency graph from Phase 1preferences.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.mdand classify the migration tier using inputs fromaws-design.json,gcp-resource-clusters.json,estimation-infra.json, andpreferences.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 phaseDependency depth from
creation_order— deeper graphs need more sequential workService complexity from
aws-design.json— databases and stateful services take longerCutover 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.jsonprojectionsGo/No-Go checkpoint: If smoke test fails acceptance criteria, stop and reassess
Stage 3: Cutover (Weeks 3-4)
Execute cutover per
preferences.jsoncutover_strategyMonitor 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 AWSValidate 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.jsonprojectionsGo/No-Go checkpoint: If PoC fails acceptance criteria, stop and reassess
Stage 3: Infrastructure Deployment (Weeks 5-7)
Deploy remaining clusters in
creation_ordersequence (depth-first)For each cluster:
Deploy infrastructure per
aws-design.jsonresource mappingsConfigure 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 syncfor bulk transferEnable 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.jsoncutover_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.jsonprojectionsRun 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_ordersequence (depth-first)For each cluster:
Deploy infrastructure per
aws-design.jsonresource mappingsConfigure 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 syncfor bulk transferEnable 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.jsonprojectionsRun 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
| Risk | Probability | Impact | Mitigation |
|---|---|---|---|
| Data loss during migration | low | critical | Dual-write for 2 weeks before cutover; full backup before migration; checksums on all transfers |
| Performance regression on AWS | medium | high | PoC testing (Weeks 3-4); load testing (Week 5); performance baseline comparison |
| Extended downtime during cutover | medium | high | Practice cutover in staging; automate DNS switch; rollback procedure on standby |
| Cost overrun vs estimates | medium | medium | Set billing alerts at 80% and 100% of projected; weekly cost review |
| Cross-region latency | low | medium | Validate latency in PoC phase; consider same-region deployment for latency-sensitive services |
| Terraform state corruption | low | high | Remote 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_*oraws_serviceisDeferred — specialist engagementinaws-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:
| Metric | Target | Measurement |
|---|---|---|
| Availability | 99.9% uptime | CloudWatch synthetic monitoring |
| Latency | Within 10% of GCP baseline | P50, P95, P99 response times |
| Error rate | < 0.1% | CloudWatch error metrics |
| Cost | Within 15% of estimation-infra.json projection | AWS Cost Explorer weekly |
Overall Migration Metrics
| Metric | Target |
|---|---|
| Data integrity | 100% — zero data loss confirmed by checksums |
| Migration timeline | Within 2 weeks of planned schedule |
| Rollback success | Tested and confirmed < 1 hour RTO |
| Team confidence | Go/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.jsonprojectionsCritical AWS service limitation discovered that blocks functionality
Per-Phase Rollback
| Phase | Rollback Action | RTO |
|---|---|---|
| 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 archived | 2-4 hours |
Rollback Procedure (Pre-DNS Cutover)
Pause dual-write replication
Reverse DNS records (AWS to GCP endpoints)
Verify GCP services receiving traffic
Shut down AWS workloads (keep for 1 week as standby)
Monitor GCP for 24 hours
Post-mortem: document what triggered rollback
Rollback Procedure (Post-DNS Cutover)
If within 48 hours: Reverse DNS, resume GCP from replicated state
If beyond 48 hours: Restore GCP from backup (2-4 hour RTO)
Validate data integrity after restore
Resume GCP operations
Post-mortem and reassess migration approach
Part 5: Team and Roles
RACI Matrix
| Activity | Migration Lead | Infrastructure Engineer | Database Engineer | Application Developer | QA Engineer |
|---|---|---|---|---|---|
| Migration planning | A/R | C | C | C | I |
| AWS account setup | A | R | I | I | I |
| Network infrastructure | A | R | I | I | I |
| Database migration | A | C | R | I | C |
| Application deployment | A | C | I | R | C |
| Data validation | A | I | R | C | R |
| Performance testing | A | C | C | C | R |
| Cutover execution | R | R | R | C | C |
| Rollback execution | R | R | R | I | I |
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
| Gate | Phase Transition | Go Criteria | No-Go Action |
|---|---|---|---|
| G1 | Setup → PoC | VPC online, IAM configured, connectivity verified | Fix infrastructure issues |
| G2 | PoC → Full Deploy | PoC cluster healthy, latency acceptable, costs tracking | Reassess architecture |
| G3 | Full Deploy → Data Migration | All clusters deployed, integration tests passing | Debug failing clusters |
| G4 | Data Migration → Cutover | Replication lag < 1s, data integrity confirmed, rollback tested | Fix replication issues |
| G5 | Cutover → Validation | DNS switched, traffic flowing, error rate < 0.1% | Execute rollback |
| G6 | Validation → Cleanup | 1 week stable operation, costs within 15% of estimate | Extend 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.jsonRight-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
phaseis"generate"generation_sourceis"infrastructure"complexity_tieris one of"small","medium","large"complexity_inputsobject 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_weeksis within the tier's allowed range: small 3-6, medium 8-12, large 12-16migration_plan.phasesarray has at least 4 entries; stage names match the tier template from Part 1migration_plan.servicescovers every service fromaws-design.jsonmigration_plan.critical_pathis non-emptymigration_plan.dependenciesreflectgcp-resource-clusters.jsoncreation_orderrisksarray has at least 3 entries with probability, impact, mitigationsuccess_metricshas bothper_serviceandoverallsectionsrollback_procedureshas trigger conditions and RTO valuesgo_no_go_criteriahas at least 2 gates (small) or 4 gates (medium/large)post_migrationspecifies monitoring duration and teardown timingAll cluster IDs reference valid clusters from
gcp-resource-clusters.jsonOutput is valid JSON
Completion Handoff Gate (Fail Closed)
Before returning control to generate.md, require:
generation-infra.jsonexists 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:
Gate Stage 2 artifact generation —
generate-artifacts-infra.mdrequires this fileProvide timeline context to
generate-artifacts-docs.mdfor MIGRATION_GUIDE.mdSet phase completion status in
.phase-status.json
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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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