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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/phases/estimate/estimate-billing.md

Estimate Phase: Billing-Only Cost Analysis

Loaded by estimate.md when aws-design-billing.json exists and aws-design.json does NOT exist.

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

Known limitations: Cost inference from billing SKU details (tighter ranges), historical trend projection, and AWS Cost Calculator integration are not yet supported.

Pricing Mode

Without source configuration (CPU, memory, disk, scaling), billing-only estimates use GCP-cost-ratio projections (Step 2 below) rather than precise per-resource AWS pricing lookups. The pricing cache and MCP API are not directly used for per-service calculations — instead, GCP actual spend is scaled by service-type multipliers.

Overall accuracy: ±30-40% due to the lack of configuration detail.

Overview

When only billing data was available for design, cost estimates carry wider ranges. Without source configuration, we cannot calculate precise AWS costs. Instead, we produce low/mid/high estimates per service based on typical AWS pricing for the mapped service type.

Input: $MIGRATION_DIR/aws-design-billing.json, $MIGRATION_DIR/billing-profile.json, $MIGRATION_DIR/preferences.json Output: $MIGRATION_DIR/estimation-billing.json

Prerequisites

Read from $MIGRATION_DIR/:

From aws-design-billing.json:

  • services[] — GCP services with AWS mappings and confidence levels

  • metadata.design_source — must be "billing_only"

From billing-profile.json:

  • services[] — GCP services with monthly costs and SKUs

  • summary.total_monthly_spend — Total GCP spend (this is the baseline)

From preferences.json:

  • design_constraints — User preferences for optimization approach

Step 1: Establish GCP Baseline from Billing

Use billing-profile.json as the authoritative GCP cost baseline:

GCP Monthly Baseline: $[total_monthly_spend]

Per-service breakdown:
  [service 1]: $[monthly_cost]
  [service 2]: $[monthly_cost]
  ...

This is actual spend data — higher confidence than inferred costs.

CUD-Aware Baseline Adjustment

If billing-profile.json contains commitments.has_active_cuds == true:

  1. Use list price as baseline: The services[].monthly_cost values already reflect list price (commitment fee rows are excluded). Use these directly — do not subtract CUD credits from the baseline.

  2. Exclude commitment fees from workload costs: Commitment fee rows (e.g., "Commitment v1: E2 Cpu") are billing artifacts, not workload costs. They are already excluded from services[] by the discover phase.

  3. Note the customer's effective discount: Record commitments.effective_discount_percent for the comparison narrative. The customer currently pays this percentage below list price on GCP.

If commitments.has_active_cuds == false or the commitments section is absent, proceed with total_monthly_spend as-is.

Step 2: Generate AWS Cost Ranges

For each service in aws-design-billing.json, produce low/mid/high estimates:

Range Calculation

Since we lack source configuration, apply percentage-based ranges around the GCP cost:

For each service:
  GCP monthly cost: $X

  Low estimate:  $X x 0.6  (aggressive optimization, smallest viable config)
  Mid estimate:  $X x 1.0  (roughly equivalent config, typical pricing)
  High estimate: $X x 1.4  (premium config, higher availability, no optimization)
Adjust Ranges by Service Type

Some service types have tighter ranges than others:

Service TypeRange FactorRationale
Compute (Cloud Run, GKE)±30%Pricing varies by config and scaling
Database (Cloud SQL)±35%Engine, HA, and sizing drive large variance
Storage (GCS)±15%Storage pricing is relatively stable
Messaging (Pub/Sub)±20%Usage-based, somewhat predictable
Networking (LB, DNS)±20%Fixed + usage components
AI (Vertex AI)±40%Model choice drives massive variance
Adjust by SKU Hints

If aws-design-billing.json has sku_hints from SKU analysis, tighten the range:

  • SKU indicates specific instance size -> reduce range to ±15%

  • SKU indicates storage class -> reduce range to ±10%

  • No SKU hints available -> use full range

Step 3: Total Cost Projection

AWS Projected Monthly Cost:
  Low:  $[sum of all low estimates]
  Mid:  $[sum of all mid estimates]
  High: $[sum of all high estimates]

vs GCP Actual Monthly: $[total_monthly_spend]

Difference:
  Best case:  [low vs GCP] (potential savings of $X/month)
  Expected:   [mid vs GCP] (roughly equivalent cost)
  Worst case: [high vs GCP] (potential increase of $X/month)
Commitment-to-Commitment Comparison (if CUDs detected)

If billing-profile.json has commitments.has_active_cuds == true, add a commitment context note:

Commitment Context:
  GCP current effective rate: [effective_discount_percent]% below list price via CUDs
  GCP list price baseline:   $[cost_basis.total_at_list]/month
  GCP net-of-discounts:      $[cost_basis.total_net_of_discounts]/month

  AWS equivalent commitment options:
    Compute Savings Plans (Fargate/Lambda/EC2): up to 66% max; typical 20-40% (1yr no-upfront)
    Database Savings Plans (Aurora/RDS/etc.): up to 35% (serverless) / up to 20% (provisioned); 1yr no-upfront
    RDS Reserved Instances: up to 69% (3yr All Upfront); mutually exclusive with Database SP on same workload
    1-year Compute Savings Plan: typically 20-40% below on-demand
    3-year Compute Savings Plan: typically 40-66% below on-demand

  Fair comparison: GCP list price vs AWS on-demand (both uncommitted)
  Committed comparison: GCP net-of-CUD vs AWS with applicable 1yr commitments
  Cloud Run / GKE → Fargate note: Do not size Compute Savings Plans from GCP billing — establish 30-90 day AWS baseline post-migration. GKE node pools may inform a rough floor estimate only.

This ensures the customer understands that their current GCP discount has an AWS equivalent, and the comparison is apples-to-apples.

Step 4: Human One-Time Migration Costs (Out of Scope)

Do not present human labor, professional services, engineering, training, discovery/design effort, or similar people-time work as one-time migration costs or budget categories.

Populate migration_cost_considerations.categories as an empty array []. Use migration_cost_considerations.note to state that human and professional-services one-time migration costs are intentionally excluded. You may still recommend IaC discovery in recommendation.next_steps or unknowns as a precision improvement — without framing it as a cost line item.

Vendor fees: If you discuss GCP egress in narrative, describe it only as vendor/network charges when grounded in billing-profile.json (do not invent dollar amounts). Do not mix human effort into “one-time cost” lists.

Step 5: Document Unknowns

List what would narrow the cost ranges:

Unknowns that affect cost precision:
1. Compute sizing (CPU, memory) -- Would narrow compute range from ±30% to ±10%
2. Database engine and HA config -- Would narrow database range from ±35% to ±15%
3. Scaling configuration -- Affects whether Reserved Instances are viable
4. Network topology -- VPC, subnets, peering costs
5. Security requirements -- Encryption, compliance may add costs

Recommendation:
  Run IaC discovery (provide Terraform files) to reduce unknowns.
  This would narrow total estimate range from ±30-40% to ±10-15%.

Step 6: Generate Output

Write estimation-billing.json.

estimation-billing.json schema
{
  "phase": "estimate",
  "timestamp": "[ISO 8601]",
  "metadata": {
    "estimate_source": "billing_only",
    "pricing_source": "cached|live|cached_fallback|unavailable",
    "confidence_note": "Estimates have wider ranges due to billing-only source"
  },
  "accuracy_confidence": "±30-40%",

  "gcp_baseline": {
    "source": "billing_data",
    "total_monthly_spend": 0.00,
    "service_count": 0,
    "services": [
      {
        "gcp_service": "Cloud Run",
        "monthly_cost": 450.00
      }
    ]
  },

  "aws_projection": {
    "low_monthly": 0.00,
    "mid_monthly": 0.00,
    "high_monthly": 0.00,
    "services": [
      {
        "gcp_service": "Cloud Run",
        "gcp_monthly": 450.00,
        "aws_target": "Fargate",
        "aws_low": 270.00,
        "aws_mid": 450.00,
        "aws_high": 630.00,
        "range_factor": "±30%",
        "unknowns": ["instance sizing", "scaling config"]
      }
    ]
  },

  "cost_comparison": {
    "gcp_monthly": 0.00,
    "aws_monthly_low": 0.00,
    "aws_monthly_mid": 0.00,
    "aws_monthly_high": 0.00,
    "best_case_savings": 0.00,
    "worst_case_increase": 0.00
  },

  "migration_cost_considerations": {
    "categories": [],
    "note": "Human and professional-services one-time migration costs are not presented by this advisor. Billing-only source increases estimate variance; IaC discovery narrows recurring cost ranges.",
    "complexity_factors": ["billing_only_source", "unknown_infrastructure_config"]
  },

  "unknowns": [
    {
      "category": "compute_sizing",
      "impact": "high",
      "resolution": "Provide Terraform files or describe instance configurations"
    },
    {
      "category": "database_config",
      "impact": "high",
      "resolution": "Provide Terraform files or describe database engine, HA, sizing"
    },
    {
      "category": "scaling_config",
      "impact": "medium",
      "resolution": "Describe traffic patterns and scaling requirements"
    },
    {
      "category": "network_topology",
      "impact": "medium",
      "resolution": "Describe VPC, subnet, and peering configuration"
    }
  ],

  "recommendation": {
    "confidence": "low",
    "note": "These estimates are based on billing data only. For precise estimates (±10-15%), run IaC discovery by providing Terraform files.",
    "next_steps": [
      "Review cost ranges with stakeholders",
      "Consider running IaC discovery for tighter estimates",
      "Use mid estimate for initial budgeting",
      "Plan for high estimate as worst-case budget"
    ]
  }
}

Output Validation Checklist

  • metadata.estimate_source is "billing_only"

  • accuracy_confidence is "±30-40%" (never tighter for billing-only)

  • gcp_baseline matches billing-profile.json totals

  • Every service has low/mid/high estimates

  • unknowns array is populated with resolution steps

  • recommendation.confidence is "low" (billing-only never produces high confidence)

  • No reference to Terraform-based configurations

  • All unknowns documented with impact and resolution

  • All cost values are numbers, not strings

  • migration_cost_considerations.categories is [] — no human one-time migration costs presented

  • Output is valid JSON

Completion Handoff Gate (Fail Closed)

Before returning control to estimate.md, require:

  • estimation-billing.json exists and passes the Output Validation Checklist above.

If this gate fails: STOP and output: "estimate-billing did not produce a valid estimation-billing.json; do not complete Phase 4."

Present Summary

After writing estimation-billing.json, present a concise summary to the user:

  1. Pricing source and accuracy: State that estimates are billing-only projections with ±30-40% accuracy due to lack of infrastructure configuration. Example: "Billing-only estimates, accuracy ±30-40%. Provide Terraform files to narrow to ±10-15%."

  2. GCP baseline from billing data (total monthly spend)

  3. AWS projected cost ranges: low / mid / high per service

  4. Total projection: best case / expected / worst case vs GCP

  5. Key unknowns that would narrow the estimates

  6. Recommendation: run IaC discovery for tighter estimates (±10-15% vs ±30-40%) — as a precision step, not as a human cost estimate

Keep it under 20 lines. The user can ask for details or re-read estimation-billing.json at any time.

Generate Phase Integration

The Generate phase uses estimation-billing.json:

  • Uses wide cost ranges for conservative timeline planning

  • Recommends IaC discovery as a prerequisite step

  • Documents unknowns as prerequisites per generation step

  • Do not surface human one-time migration costs from this artifact — migration_cost_considerations.categories remains empty in user-facing docs

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

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Published by
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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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