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://mp-staging.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-billing.md
Generate Phase: Billing-Only Migration Plan
Loaded by generate.md when estimation-billing.json exists.
Execute ALL steps in order. Do not skip or optimize.
Known limitations: Partial IaC discovery (mixed Terraform + billing-only services) is not yet supported. Confidence scoring per service based on billing SKU specificity is not yet implemented.
Overview
This file produces a complexity-scaled migration plan with wider timelines and lower confidence thresholds than the infrastructure path. Billing-only data provides service-level spend but lacks configuration details (instance sizes, replication settings, networking topology). The plan accounts for this uncertainty with:
Complexity tier classification to right-size the timeline (small: 2-4 weeks, medium: 6-10 weeks, large: 12-18 weeks)
Wider success criteria thresholds scaled by tier
Explicit recommendation to run IaC discovery before executing the plan
Prerequisites
Read the following artifacts from $MIGRATION_DIR/:
aws-design-billing.json(REQUIRED) — Billing-based service mapping from Phase 3estimation-billing.json(REQUIRED) — Billing-only cost estimates from Phase 4billing-profile.json(REQUIRED) — GCP billing breakdown 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: Context and Limitations
What Billing Data Provides
Service-level monthly spend (which GCP services are in use)
Relative cost distribution (which services are most expensive)
AI signal detection (whether AI/ML services appear in billing)
SKU-level hints about usage patterns
What Billing Data Does NOT Provide
Instance sizes and configurations (CPU, memory, storage)
Networking topology (VPC, subnets, firewall rules)
Database engines and versions
Replication and high-availability settings
Inter-service dependencies
Scaling configurations (min/max instances, autoscaling policies)
Security configurations (IAM roles, encryption settings)
Recommendation
For a more accurate migration plan, provide Terraform files and re-run discovery. This billing-only plan is suitable for initial budgeting and stakeholder discussions, but must be refined with IaC discovery before executing the actual migration.
Part 2: Complexity-Scaled Timeline
Before building the timeline, load
references/shared/migration-complexity.mdand classify the migration tier using the inputs fromaws-design-billing.json,billing-profile.json, andpreferences.json.
Use the Billing-Only Path stage templates from migration-complexity.md for the determined tier. The three tiers produce different timelines:
Small Tier (2-4 weeks)
No parallel-run stage. Discovery and provisioning overlap. Suitable for migrations with <=3 services, <$1K/month, no databases, single-AZ, no compliance.
Stage 1: Discovery + Provisioning (Week 1)
Quick audit of GCP infrastructure (few services, low complexity)
Provision AWS VPC, compute, and supporting resources
Configure IAM roles and security groups
Stage 2: Deploy + Test (Week 2)
Deploy applications to AWS
Run functional and integration tests
Validate cost tracking against estimates
Stage 3: Cutover + Validation (Weeks 3-4)
Execute cutover during maintenance window (DNS switch)
24-hour intensive monitoring
Stabilization and GCP teardown planning
Medium Tier (6-10 weeks)
Shortened parallel run. Discovery takes 1-2 weeks instead of 4. For migrations with 4-8 services, $1K-$10K/month, or databases present.
Stage 1: Discovery Refinement (Weeks 1-2)
Audit current GCP infrastructure
Document instance sizes, database configs, networking topology
Map dependencies between services
Refine AWS design based on discovered configurations
Stage 2: Service Migration (Weeks 3-5)
Provision AWS infrastructure (VPC, compute, databases, storage)
Deploy applications, set up CI/CD pipelines
Integration testing and data migration dry run
Stage 3: Parallel Run (Weeks 6-7)
Run both environments simultaneously
Compare performance, reliability, and costs
Validate data consistency
Stage 4: Cutover and Validation (Weeks 8-10)
Execute cutover (DNS switch, traffic migration)
48-hour intensive monitoring
Stabilization and GCP teardown planning
Large Tier (12-18 weeks)
Full conservative plan with extended discovery and parallel-run phases. For migrations with 9+ services, >$10K/month, multi-region, compliance, or AI alongside infrastructure.
Stage 1: Discovery Refinement (Weeks 1-4)
Weeks 1-2: Manual infrastructure audit, dependency mapping, configuration documentation
Weeks 3-4: Refine AWS design, re-estimate costs, identify services needing different AWS targets
Consider running IaC discovery if Terraform files become available
Stage 2: Service Migration (Weeks 5-9)
Weeks 5-6: Provision AWS infrastructure (VPC, compute, databases, storage)
Weeks 7-8: Deploy applications, set up CI/CD, migrate to staging
Week 9: Integration testing, performance baseline, data migration dry run
Stage 3: Parallel Run (Weeks 10-12)
Run both GCP and AWS simultaneously
Compare performance, reliability, and costs
Validate data consistency between environments
Monitor for 2+ weeks before cutover decision
Stage 4: Cutover and Validation (Weeks 13-15+)
Execute cutover (DNS switch, traffic migration)
48-hour intensive monitoring
Stabilization and GCP teardown planning
Part 3: Risk Assessment
Risks are scaled by complexity tier. Use the Risk Scaling by Tier table in references/shared/migration-complexity.md to set probability values. The table below shows risk templates — replace the probability column with the tier-appropriate value.
Standard Risks
| Risk | Small Prob. | Medium Prob. | Large Prob. | Impact | Mitigation |
|---|---|---|---|---|---|
| Incorrect service sizing | low | medium | high | high | Discovery phase audit; right-size after validation |
| Missing dependencies discovered late | low | medium | high | medium | Manual dependency mapping in discovery; buffer in timeline |
| Data migration complexity underestimated | n/a | medium | high | high | Dry run before cutover; parallel run as safety net (medium/large only) |
| Cost overrun due to unknown configurations | low | medium | high | medium | Set billing alerts at 80% of high estimate; weekly cost reviews |
| Performance regression from incorrect sizing | low | medium | medium | high | Parallel run comparison (medium/large); resize before cutover |
| Longer timeline than planned | low | medium | high | medium | Build buffer into schedule; communicate planned timeline upfront |
| Unmapped services block migration | low | medium | medium | high | Address unknowns in discovery refinement |
| BigQuery migration complexity (if BigQuery in billing) | high | high | high | high | Engage AWS account team for specialist guidance on query patterns, data volumes, ETL pipelines, and BI integrations |
For the determined tier, include only risks with probability > "n/a". Use the probability from that tier's column.
Part 4: Per-Service Migration Steps
For each service in aws-design-billing.json.services[], generate a migration step template.
Migration Step Template
Service: [gcp_service] → [aws_service]
Monthly Cost: $[monthly_cost] (GCP) → $[aws_mid] estimated (AWS)
How chosen: Estimated from billing only (JSON: billing_inferred) — see design-refs/fast-path.md User-facing vocabulary
Steps:
1. [ ] Determine actual configuration (instance size, storage, etc.)
- TODO: Check GCP console or Terraform for [gcp_service] configuration
2. [ ] Provision AWS [aws_service] with discovered configuration
3. [ ] Migrate data (if applicable)
4. [ ] Test functionality and performance
5. [ ] Validate cost aligns with estimate
Unknowns:
- Instance sizing: TODO — verify in GCP console
- Scaling configuration: TODO — verify current autoscaling policies
- Dependencies: TODO — map which services depend on this oneExample: Cloud Run to Fargate
Service: Cloud Run → Fargate
Monthly Cost: $450.00 (GCP) → $270-$630 estimated (AWS)
How chosen: Estimated from billing only (JSON: billing_inferred)
SKU Hints: CPU Allocation Time, Memory Allocation Time
Steps:
1. [ ] Determine actual configuration
- TODO: Check CPU/memory allocation per Cloud Run service
- TODO: Check concurrency and scaling settings
- TODO: Check number of Cloud Run services
2. [ ] Create Fargate task definitions with matching CPU/memory
3. [ ] Set up ALB and target groups
4. [ ] Deploy container images to ECR
5. [ ] Configure autoscaling to match Cloud Run behavior
6. [ ] Test endpoint connectivity and performance
Unknowns:
- CPU allocation: TODO — check Cloud Run service configurations
- Memory allocation: TODO — check Cloud Run service configurations
- Number of services: TODO — count from GCP console or gcloud CLI
- Concurrency settings: TODO — check Cloud Run concurrency limitsUnmapped Services
For each entry in aws-design-billing.json.unknowns[]:
Service: [gcp_service] — UNMAPPED
Monthly Cost: $[monthly_cost] (GCP)
Reason: [reason]
Suggestion: [suggestion]
Action Required:
- [ ] TODO: Manually identify the AWS equivalent for [gcp_service]
- [ ] TODO: Determine configuration and sizing
- [ ] TODO: Add to migration plan once mappedPart 5: Success Criteria
Thresholds scaled by complexity tier. Simpler migrations have fewer unknowns and tighter targets. Use the Success Criteria Scaling by Tier > Billing-Only Path table in references/shared/migration-complexity.md.
| Criteria | Small | Medium | Large |
|---|---|---|---|
| Performance within baseline | Within 15% of GCP | Within 20% of GCP | Within 20% of GCP |
| Monitoring stability | 24-hour watch period | 48-hour watch period | 48-hour watch period |
| Post-migration stability | 14-day observation | 30-day observation | 45-day observation |
| Cost variance | Within 25% of mid estimate | Within 30% of mid estimate | Within 40% of mid estimate |
| Data integrity | 100% | 100% | 100% |
| Service availability | 99% | 99% | 99% |
Apply the column matching the determined complexity tier.
Part 6: Output Format
Generate generation-billing.json in $MIGRATION_DIR/ with the following schema.
The example below shows a small tier migration. Adjust complexity_tier, complexity_inputs, total_weeks, approach, phases, risk probabilities, success metric thresholds, and estimated_total_effort_hours according to the determined tier (see Part 2, Part 3, Part 5, and references/shared/migration-complexity.md).
{
"phase": "generate",
"generation_source": "billing_only",
"confidence": "low",
"complexity_tier": "small",
"complexity_inputs": {
"service_count": 2,
"monthly_spend": 75.71,
"has_databases": false,
"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": 4,
"approach": "compressed",
"phases": [
{
"name": "Discovery + Provisioning",
"weeks": "1",
"key_activities": [
"Quick infrastructure audit",
"AWS provisioning",
"IAM and security configuration"
]
},
{
"name": "Deploy + Test",
"weeks": "2",
"key_activities": [
"Application deployment",
"Functional and integration testing",
"Cost validation"
]
},
{
"name": "Cutover + Validation",
"weeks": "3-4",
"key_activities": [
"DNS switch during maintenance window",
"24-hour intensive monitoring",
"Stabilization and GCP teardown planning"
]
}
],
"services": [
{
"gcp_service": "Compute Engine",
"aws_service": "EC2",
"monthly_cost_gcp": 75.46,
"estimated_cost_aws_mid": 75.46,
"confidence": "billing_inferred",
"human_expertise_required": false,
"unknowns": ["instance sizing", "scaling config"]
}
]
},
"risks": [
{
"category": "incorrect_sizing",
"probability": "low",
"impact": "high",
"mitigation": "Discovery audit; right-size after validation",
"phase_affected": "Discovery + Provisioning"
}
],
"success_metrics": {
"performance_threshold": "within 15% of GCP baseline",
"monitoring_period_hours": 24,
"stability_period_days": 14,
"cost_variance_threshold": "within 25% of mid estimate",
"data_integrity": "100%",
"availability_target": "99%"
},
"recommendation": {
"approach": "Compressed migration",
"confidence": "low",
"iac_discovery_offered": true,
"note": "For tighter estimates and a shorter timeline, provide Terraform files and re-run discovery.",
"key_risks": [
"Configuration uncertainty",
"Missing dependency information",
"Cost variance due to unknown sizing"
],
"estimated_total_effort_hours": 60
}
}Output Validation Checklist
phaseis"generate"generation_sourceis"billing_only"confidenceis"low"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 2-4, medium 6-10, large 12-18migration_plan.approachmatches tier: small ="compressed", medium ="standard_with_discovery", large ="conservative_with_discovery"migration_plan.phasesstage names match the tier template from Part 2migration_plan.servicescovers every service fromaws-design-billing.jsonrisksarray has at least 2 entries (small), 4 entries (medium), or 5 entries (large)Each risk
probabilitymatches the tier column from Part 3success_metricsthresholds match the tier column from Part 5recommendation.iac_discovery_offeredistruerecommendation.confidenceis"low"recommendation.estimated_total_effort_hoursis within the tier's range frommigration-complexity.mdOutput is valid JSON
Completion Handoff Gate (Fail Closed)
Before returning control to generate.md, require:
generation-billing.jsonexists and passes the Output Validation Checklist above.
If this gate fails: STOP and output: "generate-billing did not produce a valid generation-billing.json; do not continue Generate Stage 2."
Generate Phase Integration
The parent orchestrator (generate.md) uses generation-billing.json to:
Gate Stage 2 artifact generation —
generate-artifacts-billing.mdrequires this fileProvide billing context to
generate-artifacts-docs.mdfor MIGRATION_GUIDE.mdSet 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
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Attribution and licence
- Published by
- Cline
- Publisher
- Cline (Official)
- Source
- cline/skills · skills/gcp-to-aws · commit 2637846
- Licence
- Apache License 2.0 (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
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