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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://mp-staging.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/shared/schema-estimate-infra.md

Infrastructure Estimate Schema

Schema for estimation-infra.json, produced by estimate-infra.md.


Cost tiers (projected_costs / cost_comparison)

The fields aws_monthly_premium, aws_monthly_balanced, aws_monthly_optimized (under projected_costs) and option_a_premium, option_b_balanced, option_c_optimized (under cost_comparison) are three pricing scenarios for the same GCP->AWS mapping in aws-design.json. They are not three alternative Terraform roots.

Tier keyUser-facing labelSubtitle (use in reports / MIGRATION_GUIDE)
premiumPremiumHighest resilience / highest monthly estimate in this model
balancedBalancedDefault scenario; compare GCP to this first
optimizedOptimizedLower monthly estimate; reservations / Spot / storage trade-offs assumed

How to read: Scenario order is highest -> middle -> lowest monthly AWS estimate for the modeled architecture. Balanced is the primary comparison row vs the GCP baseline. Premium and Optimized are bounds (HA vs cost-optimization skew).

Terraform: When the Generate phase produces terraform/, it implements one infrastructure baseline aligned with the Balanced scenario (aligned_with_estimate_tier in the migration_summary output). Premium and Optimized remain estimate-only unless the customer edits IaC. See references/phases/generate/generate-artifacts-infra.md (terraform/README.md, main.tf header comment).


estimation-infra.json schema

{
  "phase": "estimate",
  "design_source": "infrastructure",
  "timestamp": "2026-02-24T14:00:00Z",
  "pricing_source": {
    "status": "cached|live|cached_fallback|unavailable",
    "message": "Using cached prices from 2026-03-04 (±5-10% accuracy)|Using live AWS pricing API|MCP unavailable, using cached rates (±5-25% accuracy)|Pricing unavailable for [service]",
    "fallback_staleness": {
      "last_updated": "2026-02-24",
      "days_old": 3,
      "is_stale": false,
      "staleness_warning": null
    },
    "services_by_source": {
      "live": ["Fargate", "RDS Aurora", "S3", "ALB"],
      "fallback": ["NAT Gateway"],
      "estimated": []
    },
    "services_with_missing_fallback": []
  },
  "accuracy_confidence": "±5-10%|±15-25%",

  "current_costs": {
    "source": "billing_data|inventory_estimate|preferences|user_provided|unavailable",
    "gcp_monthly": 300,
    "gcp_annual": 3600,
    "baseline_note": "From billing-profile.json actual spend data",
    "breakdown": { "compute": 75, "database": 50, "storage": 40, "networking": 20, "other": 15 }
  },

  "projected_costs": {
    "aws_monthly_premium": 1003,
    "aws_monthly_balanced": 265,
    "aws_monthly_optimized": 194,
    "aws_annual_optimized": 2328,
    "breakdown": {
      "compute": {
        "service": "Fargate",
        "monthly": 71,
        "alternative": { "service": "Lambda", "monthly": 9, "savings": 62 }
      },
      "database": {
        "service": "Aurora PostgreSQL",
        "monthly": 269,
        "alternative": { "service": "RDS PostgreSQL", "monthly": 75, "savings": 194 }
      },
      "storage": {
        "service": "S3 Standard + Intelligent-Tiering",
        "monthly": 86,
        "alternative": { "service": "S3-IA", "monthly": 65, "savings": 21 }
      },
      "networking": { "service": "ALB + NAT Gateway", "monthly": 53 },
      "supporting": { "secrets_manager": 1.20, "cloudwatch": 35.30 }
    }
  },

  "cost_comparison": {
    "gcp_monthly_baseline": 300,
    "option_a_premium": {
      "aws_monthly": 1003,
      "monthly_difference": 703,
      "annual_difference": 8436,
      "percent_change": "+234%"
    },
    "option_b_balanced": {
      "aws_monthly": 265,
      "monthly_difference": -35,
      "annual_difference": -420,
      "percent_change": "-12%"
    },
    "option_c_optimized": {
      "aws_monthly": 194,
      "monthly_difference": -106,
      "annual_difference": -1272,
      "percent_change": "-35%"
    },
    "commitment_context": {
      "gcp_has_active_cuds": true,
      "gcp_effective_discount_percent": 8.2,
      "gcp_monthly_at_list": 300,
      "gcp_monthly_net_of_discounts": 275,
      "aws_compute_savings_plan_discount": "up to 66% (Fargate/Lambda/EC2; max term); typical 20-40% (1yr no-upfront)",
      "aws_database_savings_plan_discount": "up to 35% (serverless) / up to 20% (provisioned RDS/Aurora)",
      "aws_rds_reserved_instance_discount": "up to 69% (specific instance family, 3yr All Upfront)",
      "aws_1yr_savings_plan_typical_discount": "20-40%",
      "aws_3yr_savings_plan_typical_discount": "40-66%",
      "note": "GCP baseline uses list price for apples-to-apples comparison. Customer currently saves 8.2% via CUDs. Database Savings Plans and RDS RIs are mutually exclusive per workload. For Cloud Run → Fargate, establish 30-90 day AWS baseline before Compute Savings Plans."
    }
  },

  "migration_cost_considerations": {
    "billing_data_available": true,
    "categories": [
      "Data transfer (GCP egress fees based on migration volume)"
    ],
    "note": "GCP charges for outbound data transfer during migration. Volume depends on database sizes and storage to migrate."
  },

  "roi_analysis": {
    "recurring_savings": {
      "monthly_difference_balanced": -35,
      "monthly_difference_optimized": -106,
      "annual_difference_balanced": -420,
      "annual_difference_optimized": -1272,
      "note": "Negative = AWS cheaper. Based on balanced/optimized tiers vs GCP baseline."
    },
    "operational_efficiency_factors": [
      "Reduced operational overhead from managed services (Fargate, RDS)",
      "Reduced on-call burden from AWS-managed HA, patching, and scaling",
      "Engineering time freed for product work instead of infrastructure maintenance"
    ],
    "non_cost_benefits": [
      "Operational efficiency (fewer engineers needed for managed services)",
      "Better global reach (more AWS regions)",
      "Broader service catalog for future workloads",
      "Better enterprise tool integration",
      "Vendor diversification (reduce single-vendor risk)",
      "Auto-scaling, spot instances, savings plans flexibility"
    ],
    "note": "GCP data transfer egress fees (if estimated) are vendor one-time charges excluded from recurring ROI calculations. Human/professional-services migration costs are not modeled here."
  },

  "optimization_opportunities": [
    {
      "opportunity": "Database Savings Plans",
      "type": "database_savings_plan",
      "target_services": ["RDS", "Aurora"],
      "savings_monthly": 15,
      "savings_percent": "up to 20% (provisioned)",
      "commitment": "1-year no-upfront",
      "timing": "post-migration or after instance right-sizing",
      "implementation_effort": "low",
      "prerequisite": "Confirm target instance class; omit savings_monthly when DB on-demand < $50/month",
      "description": "Cloud SQL 24/7 usage is predictable. Database Savings Plans offer flexibility post-migration. Mutually exclusive with RDS RIs on the same workload.",
      "alternative": {
        "opportunity": "RDS Reserved Instances",
        "type": "rds_reserved_instances",
        "savings_percent": "up to 69%",
        "trade_off": "Locked to specific instance family and region"
      },
      "references": [
        "https://aws.amazon.com/savingsplans/database-pricing/",
        "https://aws.amazon.com/rds/reserved-instances/"
      ]
    },
    {
      "opportunity": "Compute Savings Plans",
      "type": "compute_savings_plan",
      "target_services": ["Fargate", "Lambda"],
      "savings_monthly": null,
      "savings_percent": "20-66%",
      "commitment": "1-year or 3-year",
      "timing": "post-migration (after 30-90 days of usage data)",
      "implementation_effort": "low",
      "prerequisite": "Establish AWS compute usage baseline before committing",
      "description": "GCP compute billing (Cloud Run or GKE re-platform) makes pre-migration commitment sizing unreliable. Use Cost Explorer recommendations after migration.",
      "references": [
        "https://aws.amazon.com/savingsplans/compute-pricing/",
        "https://aws.amazon.com/savingsplans/faqs/"
      ]
    },
    {
      "opportunity": "S3 Infrequent Access",
      "target_services": ["S3"],
      "savings_monthly": 52,
      "savings_percent": "38%",
      "commitment": "none",
      "implementation_effort": "low",
      "description": "Move infrequently accessed data to S3-IA storage class"
    },
    {
      "opportunity": "Spot Instances for Batch",
      "target_services": ["EC2"],
      "savings_monthly": 6,
      "savings_percent": "70%",
      "commitment": "none",
      "implementation_effort": "medium",
      "description": "Use Spot instances for fault-tolerant batch processing jobs"
    }
  ],

  "financial_summary": {
    "current_gcp_monthly": 300,
    "projected_aws_balanced_monthly": 265,
    "projected_aws_optimized_monthly": 194,
    "monthly_savings_balanced": 35,
    "monthly_savings_optimized": 106,
    "annual_savings_optimized": 1272,
    "recommendation": "Migrate with optimizations for best ROI"
  },

  "recommendation": {
    "path": "migrate_optimized",
    "path_label": "Migrate with Optimizations",
    "roi_justification": "2.6 month payback with operational efficiency; $475K 5-year savings",
    "confidence": "high",
    "migrate_if": [
      "operational efficiency matters",
      "AWS-specific services needed",
      "long-term AWS strategy"
    ],
    "stay_if": [
      "cost is the only metric and AWS is more expensive",
      "team deeply experienced with GCP"
    ],
    "next_steps": [
      "Review financial case with stakeholders",
      "Confirm service tier selections (Aurora vs RDS, Fargate vs Lambda)",
      "Get approval to proceed to Execute phase",
      "Schedule migration timeline per cluster evaluation order"
    ]
  }
}
recommendation block in estimation-infra.json

The recommendation block is the single source of truth for migrate/stay guidance. Consumed by Estimate chat output AND HTML migration report (Section 0). Do not duplicate this logic in the report template.

path valuepath_label (display)
"migrate_optimized""Migrate with Optimizations"
"migrate_phased""Phased Migration"
"stay""Stay on GCP"

Validation:

  • path is one of: "migrate_optimized", "migrate_phased", "stay"

  • path_label matches the corresponding display string for path

  • migrate_if and stay_if are non-empty arrays of strings

  • next_steps is a non-empty array of strings

  • Block is REQUIRED in estimation-infra.json output (Part 7 must write it)

Observability Entry in projected_costs.breakdown

When Part 2B of estimate-infra.md produces an observability cost, it is included as an entry in projected_costs.breakdown[] with this shape:

{
  "service": "CloudWatch + X-Ray (Observability)",
  "low": 7.00,
  "mid": 10.00,
  "high": 15.00,
  "accuracy": "±30%",
  "pricing_source": "cached",
  "components": {
    "log_ingestion": 5.00,
    "log_storage": 0.45,
    "custom_metrics": 3.00,
    "alarms": 0.50,
    "tracing": 0.00
  },
  "volume_source": "heuristic",
  "note": "GCP Cloud Operations includes 50 GB/month free logging, free alerting, and free profiling. CloudWatch charges from the first GB."
}

Validation for observability entry:

  • components keys are exactly: log_ingestion, log_storage, custom_metrics, alarms, tracing

  • volume_source is one of: "heuristic", "billing" (reflects log volume source — the largest cost component; metrics are always heuristic regardless of this field)

  • tracing is 0 when no tracing signals detected in source — do not add X-Ray costs unprompted

  • mid equals the sum of all components values

  • This entry REPLACES any CloudWatch/log/metric portion in the "Supporting" row — never both

Output Validation Checklist

  • design_source is "infrastructure"

  • pricing_source.status is "cached", "live", "cached_fallback", or "unavailable"

  • accuracy_confidence matches the pricing mode (±5-10% for cached/live, ±15-25% for fallback)

  • current_costs.source is "billing_data" if billing-profile.json was used, "inventory_estimate", "preferences", "user_provided" (asked during estimate), or "unavailable" (user declined) otherwise

  • current_costs.gcp_monthly matches billing-profile.json total (if used) or is a reasonable estimate

  • projected_costs has all three tiers (premium, balanced, optimized)

  • Tier semantics: Three totals are scenario $ only (same design); Balanced matches generated Terraform baseline — see Cost tiers section above; user-facing labels must use the subtitles there (also estimate-infra.md Present Summary / generate-artifacts-report.md)

  • projected_costs.breakdown covers compute, database, storage, networking, supporting services, and observability

  • Every service in aws-design.json is represented in the cost breakdown

  • projected_costs.breakdown observability entry (when present) REPLACES any CloudWatch/log/metric costs in the "Supporting" row — never double-count

  • cost_comparison shows all three options with monthly and annual differences

  • cost_comparison.commitment_context is present if billing-profile.json has commitments.has_active_cuds == true; omitted otherwise

  • migration_cost_considerations.billing_data_available is true if billing-profile.json exists, false otherwise

  • If billing_data_available is true: migration_cost_considerations.categories lists GCP vendor egress / data transfer only (never human or professional-services costs)

  • If billing_data_available is false: migration_cost_considerations.categories is empty; note explains that billing data is required for GCP egress fee estimates

  • roi_analysis presents recurring monthly/annual savings (or increase) per tier

  • roi_analysis is honest — if migration increases cost, say so and justify with non-cost benefits

  • optimization_opportunities only includes strategies relevant to the designed architecture

  • Each optimization_opportunities[] entry includes required fields: opportunity, target_services, savings_percent, implementation_effort, description. Optional fields: type, savings_monthly (null when post-migration sizing unavailable), commitment, timing, prerequisite, references, alternative

  • Compute Savings Plans entries for Cloud Run migrations MUST NOT include savings_monthly sized from GCP billing — use savings_monthly: null and timing: post-migration

  • Database Savings Plans entries MAY include savings_monthly only when projected DB on-demand exceeds $50/month

  • optimization_opportunities savings are incremental to Balanced on-demand totals — not additive on Optimized tier (which already embeds reservation/Spot assumptions)

  • financial_summary provides a clear executive-level view

  • recommendation block exists with path, path_label, migrate_if, stay_if, and next_steps all populated

  • recommendation.path is one of: "migrate_optimized", "migrate_phased", "stay"

  • recommendation.next_steps includes actionable items

  • No references to AI-specific costs (those belong in estimate-ai.md)

  • No references to billing-only estimates (those belong in estimate-billing.md)

  • All cost values are numbers, not strings

  • Output is valid JSON

Requirements and compatibility

Written for: Claude Code

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

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

Needs: Names Claude Code tools, Needs an MCP server

Security

What our scanner noticed· Low risk

  • Instructions that use the network

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Attribution and licence

Published by
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)
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