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GCP To AWS

ClineOfficialPublished by Cline

  • Skill
  • Workflow
  • Cloud and DevOps
  • Apache-2.0
  • Low risk

From the author

Migrate workloads from Google Cloud Platform to AWS — including AI and agentic workloads regardless of cloud provider. Triggers on: migrate from GCP, GCP to AWS, move off Google Cloud, migrate Terraform to AWS, migrate Cloud SQL to RDS, migrate GKE to EKS, migrate Cloud Run to Fargate, Google Cloud migration, migrate from OpenAI to Bedrock, move off OpenAI, switch from ChatGPT API to AWS, migrate from Gemini to Bedrock, migrate LangChain to Bedrock, migrate LangGraph to AWS, migrate agentic workloads to AWS, move AI workloads to AWS, migrate my AI app to AWS. Runs a 6-phase process: discover GCP resources from Terraform files, app code, or billing exports, clarify migration requirements, design AWS architecture, estimate costs, generate migration artifacts, and collect optional feedback. Clarify must finish before Design, Estimate, or Generate. Includes AI provider migration guidance (for example, OpenAI to Amazon Bedrock) by selecting closest-fit Bedrock model families for required modality, latency/quality
— the description by Cline

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

The gcp-to-aws skill provides a deterministic, multi-phase process to migrate Google Cloud Platform (GCP) workloads to AWS. It supports infrastructure migration via Terraform analysis and AI workload migration by mapping models to Amazon Bedrock. The skill enforces a strict state machine, requiring discovery, clarification, design, estimation, and generation phases to ensure a reliable migration path. It includes specific logic for handling hybrid stacks, AI provider transitions, and complex infrastructure clustering.

What it does

  • Discovers GCP resources from Terraform, application code, or billing data.
  • Clarifies migration requirements through a structured questionnaire.
  • Designs AWS architecture based on GCP workload types and AI model requirements.
  • Estimates costs for the proposed AWS infrastructure and AI services.
  • Generates migration artifacts including Terraform configurations and AI provider adapters.
  • Provides specialized guidance for migrating AI workloads from Gemini or OpenAI to Amazon Bedrock.

When to use it

  • When you need to migrate existing GCP infrastructure to AWS.
  • When you are looking to move AI-driven applications from GCP or OpenAI to Amazon Bedrock.
  • When you have Terraform files or billing exports and need a structured migration plan.
  • When you require a step-by-step, validated approach to re-platforming cloud services.

Advantages

  • Enforces a rigorous, fail-closed state machine to prevent skipped steps.
  • Provides intelligent mapping of GCP services to AWS equivalents.
  • Includes specific logic for AI workload migration and model compatibility.
  • Supports multi-signal discovery using IaC, code, and billing data.
  • Ensures consistent artifact generation through a structured 6-phase process.

Usage examples

“Migrate my GKE and Cloud SQL setup to AWS.”
The agent initiates the discovery phase, analyzing your Terraform files to identify GKE and Cloud SQL resources, then guides you through the clarification and design phases for EKS and RDS.
“I need to move my LangChain app from OpenAI to Bedrock.”
The agent identifies the AI workload, selects the best-fit Bedrock model family, and generates the necessary provider adapters and migration documentation.

Install

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/cline/gcp-to-aws/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip gcp-to-aws.zip -d .agents/skills

Documentation by Cline

GCP-to-AWS Migration Skill

Philosophy

  • Re-platform by default: Select AWS services that match GCP workload types (e.g., Cloud Run → Fargate, Cloud SQL → RDS).

  • Dev sizing unless specified: Default to development-tier capacity (e.g., db.t4g.micro, single AZ). Upgrade only on user direction.

  • No human one-time migration costs: Do not present human labor, professional services, or people-time work as dollar estimates or "one-time migration cost" budget categories. Vendor charges grounded in data (for example GCP data transfer egress in the infra estimate when billing exists) are allowed.

  • Multi-signal approach: Design phase adapts based on available inputs — Terraform IaC for infrastructure, billing data for service mapping, and app code for AI workload detection.

  • BigQuery / google_bigquery_*: The skill does not recommend a specific AWS analytics or warehouse service. During Clarify, if discovery shows BigQuery (IaC google_bigquery_* and/or billing rows for BigQuery), you must surface the specialist advisory before Design (see references/phases/clarify/clarify.md). Design output uses Deferred — specialist engagement; keep directing the user to their AWS account team and/or a data analytics migration partner through Design, Estimate, and docs (see references/phases/design/design-infra.md BigQuery specialist gate).


Definitions

  • "Load" = Read the file using the Read tool and follow its instructions. Do not summarize or skip sections.

  • $MIGRATION_DIR = The run-specific directory under .migration/ (e.g., .migration/0226-1430/). Set during Phase 1 (Discover).


Context Loading Rules

Each phase loads reference files on demand. To keep per-turn context manageable and prevent instruction-following degradation:

  • Budget: Each phase should load no more than ~800 lines of instructions (excluding user artifacts like JSON profiles and MCP tool results).

  • Conditional loading: Reference files with trigger conditions (e.g., agentic_profile.is_agentic == true) MUST NOT be loaded unless the condition is met. Do not speculatively load files.

  • No duplication: Model mapping tables, pricing data, and shared warnings exist in one canonical file. Other files reference them; they do not copy them inline.

  • Progressive depth: Phase orchestrators (design.md, generate.md) contain short routing logic that points to detailed sub-files. Load the sub-file only when its path is selected.

Conditional reference files (load ONLY when condition is true):

FileCondition
design-refs/ai-gemini-to-bedrock.mdai-workload-profile.json exists AND summary.ai_source = "gemini" or "both"
design-refs/ai-openai-to-bedrock.mdai-workload-profile.json exists AND summary.ai_source = "openai" or "both"
design-refs/ai-anthropic-to-bedrock.mdai-workload-profile.json exists AND summary.ai_source = "anthropic"
design-refs/ai.mdai-workload-profile.json exists AND summary.ai_source = "other"
design-refs/design-ref-harness.mdagentic_profile.is_agentic == true AND ai_constraints.agentic.migration_approach == "harness"
design-refs/design-ref-agentic-to-agentcore.mdagentic_profile.is_agentic == true AND ai_constraints.agentic.migration_approach == "strands"
shared/retarget-gotchas.mdagentic_profile.is_agentic == true AND ai_constraints.agentic.migration_approach == "retarget"

When adding new reference files, verify the phase's total loaded instructions remain under budget. If a new file would exceed ~800 lines when combined with other loaded refs, split it or make it conditional.

Hybrid stack budget warning:

When both gcp-resource-inventory.json AND ai-workload-profile.json exist, the combined design refs will approach the ~800-line budget. Output this warning to the user before loading the AI design refs:

"⚠️ This is a large hybrid stack (infrastructure + AI workloads). To ensure complete and accurate recommendations, consider running the migration in two separate passes:

Pass 1 — Infrastructure: Run with only your Terraform files to get infra mapping, Terraform generation, and cost estimates.

Pass 2 — AI workloads: Run with only your application code to get Bedrock model recommendations, provider adapters, and AI migration artifacts.

Continue with the combined run? (Y/N)"

If the user chooses to continue, proceed with the combined run. Load AI refs after infra refs to preserve infra instruction fidelity. If the user declines, stop and instruct them to re-run with a single input source type.

This warning is advisory only — it does not block the run.


Prerequisites

User must provide at least one GCP source:

  • Terraform IaC: .tf files (with optional .tfvars, .tfstate)

  • Application code: Source files with GCP SDK or AI framework imports

  • Billing data: GCP billing/cost/usage export files (CSV or JSON)

If none of the above are found, stop and ask user to provide at least one source type.


State Machine

This is the execution controller. After completing each phase, consult this table to determine the next action.

Current StateConditionNext Action
discoverphases.discover != "completed"Load references/phases/discover/discover.md
clarifyphases.discover == "completed" AND phases.clarify != "completed"Load references/phases/clarify/clarify.md
designphases.clarify == "completed" AND phases.design != "completed"Load references/phases/design/design.md
estimatephases.design == "completed" AND phases.estimate != "completed"Load references/phases/estimate/estimate.md
generatephases.estimate == "completed" AND phases.generate != "completed"Load references/phases/generate/generate.md
completephases.generate == "completed" AND phases.feedback == "pending"Set phases.feedback to "completed" (user had two chances), then migration complete
completephases.generate == "completed" AND phases.feedback == "completed"Migration planning complete

How to determine current state (deterministic):

  1. Read $MIGRATION_DIR/.phase-status.json

  2. If current_phase exists, use it (must match one of: discover, clarify, design, estimate, generate, complete)

  3. Otherwise use ordered phase evaluation: discover → clarify → design → estimate → generate

  4. Pick the first phase in that order where phases.<phase> != "completed"; if none, state is complete

Phase gate checks: If prior phase incomplete, do not advance (e.g., cannot enter estimate without completed design).

Clarify is mandatory: Do not load references/phases/design/design.md, references/phases/estimate/estimate.md, or references/phases/generate/generate.md unless $MIGRATION_DIR/.phase-status.json exists and phases.clarify is exactly "completed". A preferences.json file alone is not sufficient proof that Clarify ran. If the user asks to skip Clarify or jump straight to Design, cost estimate, or artifact generation, refuse briefly, then load references/phases/clarify/clarify.md and run Phase 2. There is no exception for "quick" or "obvious" migrations.

Feedback checkpoints: Feedback is not a sequential phase — it is offered at two interleaved checkpoints (after Discover and after Estimate). See the Feedback Checkpoints section below for details.

Handoff Gate Orchestration (Fail Closed)

Load references/shared/handoff-gates.md when executing any phase completion step.

  1. Single $MIGRATION_DIR: Use one run directory for the entire migration. Do not mix artifacts across .migration/*/ sessions.

  2. Re-read from disk: Before each phase (and before each handoff gate), Read required artifacts from $MIGRATION_DIR/. Do not rely on chat memory.

  3. Advance only on HANDOFF_OK: A phase is complete only when its orchestrator emits HANDOFF_OK | phase=<name> | artifacts=.... Do not load the next phase without it.

  4. On GATE_FAIL: Output the failure line(s) to the user in plain language. Do NOT modify artifacts to pass the gate. Do NOT continue to the next phase. Tell the user which phase to re-run.

  5. Re-entry: Re-running an earlier phase after downstream phases completed requires explicit user confirmation; downstream phases must be reset to "pending". See handoff-gates.md re-entry table.

Generate phase additionally loads references/shared/validate-artifacts.md before writing migration-report.html.


State Validation

When reading $MIGRATION_DIR/.phase-status.json, validate before proceeding:

  1. Multiple sessions: If multiple directories exist under .migration/, list them with their phase status and ask: [A] Resume latest, [B] Start fresh, [C] Cancel.

  2. Invalid JSON: If .phase-status.json fails to parse, STOP. Output: "State file corrupted (invalid JSON). Delete the file and restart the current phase."

  3. Unrecognized phase: If phases object contains a phase not in {discover, clarify, design, estimate, generate, feedback}, STOP. Output: "Unrecognized phase: [value]. Valid phases: discover, clarify, design, estimate, generate, feedback."

  4. Unrecognized status: If any phases.* value is not in {pending, in_progress, completed}, STOP. Output: "Unrecognized status: [value]. Valid values: pending, in_progress, completed."

  5. Invalid current_phase (if present): If current_phase is not in {discover, clarify, design, estimate, generate, complete}, STOP. Output: "Unrecognized current_phase: [value]. Valid values: discover, clarify, design, estimate, generate, complete."

  6. Out-of-order completion: For ordered phases [discover, clarify, design, estimate, generate], if any later phase is "completed" while an earlier phase is not "completed", STOP. Output: "Inconsistent phase ordering detected. Reconcile .phase-status.json before resuming."

  7. Multiple active phases: Across core phases {discover, clarify, design, estimate, generate}, at most one phase may be "in_progress". If >1, STOP. Output: "Multiple phases are in_progress. Keep only one active phase before resuming."


State Management

Migration state lives in $MIGRATION_DIR (.migration/[MMDD-HHMM]/), created by Phase 1 and persisted across invocations.

.phase-status.json schema:

{
  "migration_id": "0226-1430",
  "last_updated": "2026-02-26T15:35:22Z",
  "current_phase": "design",
  "phases": {
    "discover": "completed",
    "clarify": "completed",
    "design": "in_progress",
    "estimate": "pending",
    "generate": "pending",
    "feedback": "pending"
  }
}

Status values: "pending" → "in_progress" → "completed". Never goes backward. For core phases (discover, clarify, design, estimate, generate), at most one phase may be "in_progress" at any time. current_phase is optional but recommended; when present it is authoritative.

The .migration/ directory is automatically protected by a .gitignore file created in Phase 1.

Phase Status Update Protocol

Use read-merge-write updates for .phase-status.json:

  1. Read the current file before every update.

  2. Change only the phase keys being advanced and last_updated.

  3. Keep prior completed phases unchanged.

  4. Set current_phase to the next deterministic phase (or complete after generate).

  5. Write the full file in the same turn as your final phase work message.

Example — after completing the Clarify phase, write $MIGRATION_DIR/.phase-status.json with:

{
  "migration_id": "MMDD-HHMM",
  "last_updated": "2026-02-26T15:35:22Z",
  "current_phase": "design",
  "phases": {
    "discover": "completed",
    "clarify": "completed",
    "design": "pending",
    "estimate": "pending",
    "generate": "pending",
    "feedback": "pending"
  }
}

Replace MMDD-HHMM with the actual migration ID, generate the last_updated ISO 8601 UTC timestamp yourself, and set each phase to its correct status at that point.


Phase Summary Table

PhaseInputsOutputsReference
Discover.tf files, app source code, and/or billing exports (at least one required)gcp-resource-inventory.json, gcp-resource-clusters.json, ai-workload-profile.json, billing-profile.json, .phase-status.json updated (outputs vary by input)references/phases/discover/discover.md
ClarifyDiscovery artifacts (gcp-resource-inventory.json, gcp-resource-clusters.json, ai-workload-profile.json, billing-profile.json — whichever exist)preferences.json, .phase-status.json updatedreferences/phases/clarify/clarify.md
Designpreferences.json + discovery artifactsaws-design.json (infra), aws-design-ai.json (AI), aws-design-billing.json (billing-only)references/phases/design/design.md
Estimateaws-design.json or aws-design-billing.json or aws-design-ai.json, preferences.jsonestimation-infra.json or estimation-ai.json or estimation-billing.json, .phase-status.json updatedreferences/phases/estimate/estimate.md
Generateestimation-infra.json or estimation-ai.json or estimation-billing.json, aws-design.json or aws-design-billing.json or aws-design-ai.json, preferences.jsongeneration-infra.json or generation-ai.json or generation-billing.json + terraform/, scripts/, ai-migration/, validation-report.json (when infra route active), MIGRATION_GUIDE.md, README.md, .phase-status.json updatedreferences/phases/generate/generate.md
Feedback.phase-status.json (discover completed minimum), all existing migration artifactsfeedback.json, trace.json, .phase-status.json updatedreferences/phases/feedback/feedback.md

MCP Servers

awspricing (for cost estimation):

  • Provides get_pricing, get_pricing_service_codes, get_pricing_service_attributes tools

  • Only needed during Estimate phase. Discover and Design do not require it.

  • Primary pricing source: references/shared/pricing-cache.md (cached 2026 rates, ±5-10% for infrastructure, ±15-25% for AI models). MCP is secondary — used only for services not found in the cache.


Files in This Skill

gcp-to-aws/
├── SKILL.md                                    ← You are here (orchestrator + state machine)
│
├── references/
│   ├── phases/
│   │   ├── discover/
│   │   │   ├── discover.md                     # Phase 1: Discover orchestrator
│   │   │   ├── discover-iac.md                 # Terraform/IaC discovery
│   │   │   ├── discover-app-code.md            # App code discovery
│   │   │   └── discover-billing.md             # Billing data discovery
│   │   ├── clarify/
│   │   │   ├── clarify.md                     # Phase 2: Clarify orchestrator
│   │   │   ├── clarify-global.md              # Category A: Global/Strategic (Q1-Q7)
│   │   │   ├── clarify-compute.md             # Categories B+C: Config Gaps + Compute (Q8-Q11)
│   │   │   ├── clarify-database.md            # Category D: Database (Q12–Q13b)
│   │   │   ├── clarify-ai.md                  # Category F: AI/Bedrock (Q14-Q22)
│   │   │   └── clarify-ai-only.md             # Standalone AI-only migration flow
│   │   ├── design/
│   │   │   ├── design.md                       # Phase 3: Design orchestrator
│   │   │   ├── design-infra.md                 # Infrastructure design (IaC-based)
│   │   │   ├── design-ai.md                    # AI workload design (Bedrock)
│   │   │   └── design-billing.md               # Billing-only design (fallback)
│   │   ├── estimate/
│   │   │   ├── estimate.md                     # Phase 4: Estimate orchestrator
│   │   │   ├── estimate-infra.md               # Infrastructure cost analysis
│   │   │   ├── estimate-ai.md                  # AI workload cost analysis
│   │   │   └── estimate-billing.md             # Billing-only cost analysis
│   │   ├── generate/
│   │   │   ├── generate.md                     # Phase 5: Generate orchestrator
│   │   │   ├── generate-infra.md               # Infrastructure migration plan
│   │   │   ├── generate-ai.md                  # AI migration plan
│   │   │   ├── generate-billing.md             # Billing-only migration plan
│   │   │   ├── generate-artifacts-infra.md     # Terraform configurations
│   │   │   ├── generate-artifacts-scripts.md  # Migration scripts
│   │   │   ├── generate-artifacts-ai.md        # Provider adapter + test harness
│   │   │   ├── generate-artifacts-billing.md   # Skeleton Terraform
│   │   │   └── generate-artifacts-docs.md      # MIGRATION_GUIDE.md + README.md
│   │   └── feedback/
│   │       ├── feedback.md                     # Phase 6: Feedback orchestrator
│   │       └── feedback-trace.md               # Anonymized trace builder
│   │
│   ├── design-refs/
│   │   ├── index.md                            # Lookup table: GCP type → design-ref file
│   │   ├── fast-path.md                        # Deterministic 1:1 mappings (Pass 1)
│   │   ├── compute.md                          # Compute mappings (Cloud Run, GCE, GKE, etc.)
│   │   ├── database.md                         # Database mappings (Cloud SQL, Spanner, etc.)
│   │   ├── storage.md                          # Storage mappings (GCS, Filestore, etc.)
│   │   ├── networking.md                       # Networking mappings (VPC, LB, DNS, etc.)
│   │   ├── messaging.md                        # Messaging mappings (Pub/Sub, etc.)
│   │   └── ai.md                               # AI mappings (Vertex AI → Bedrock)
│   │
│   ├── clustering/terraform/
│   │   ├── classification-rules.md             # Primary/secondary classification
│   │   ├── clustering-algorithm.md             # Cluster formation rules
│   │   ├── depth-calculation.md                # Topological depth calculation
│   │   └── typed-edges-strategy.md             # Edge type assignment
│   │
│   └── shared/
│       ├── schema-phase-status.md              # .phase-status.json schema (canonical reference)
│       ├── schema-discover-iac.md              # gcp-resource-inventory + clusters schemas (loaded by discover-iac.md)
│       ├── schema-discover-ai.md               # ai-workload-profile schema (loaded by discover-app-code.md and discover-iac.md Step 7d)
│       ├── schema-discover-billing.md          # billing-profile schema (loaded by discover-billing.md)
│       ├── schema-estimate-infra.md            # estimation-infra.json schema (loaded by estimate-infra.md at write time)
│       ├── handoff-gates.md                    # Fail-closed phase handoff protocol (GATE_FAIL / HANDOFF_OK)
│       ├── validate-artifacts.md               # Pre-report validation (Generate Step 0; read-only)
│       ├── migration-complexity.md             # Complexity tier definitions (small/medium/large) for timeline scaling
│       ├── pricing-cache.md                    # Cached AWS + source provider pricing (±5-25%, primary source)
│       └── bedrock-quotas.md                   # Bedrock TPM/RPM quota awareness, burndown rates, capacity planning
ConditionAction
No GCP sources found (no .tf, no app code, no billing data)Stop. Output: "No GCP sources detected. Provide at least one source type (Terraform files, application code, or billing exports) and try again."
.phase-status.json missing phase gateStop. Output: "Cannot enter Phase X: Phase Y-1 not completed. Start from Phase Y or resume Phase Y-1."
awspricing unavailable after 3 attemptsDisplay user warning about ±5-25% accuracy. Use pricing-cache.md. Add pricing_source: "cached_fallback" to the applicable estimation-*.json file.
User skips questions or says "use defaults for the rest"Apply documented defaults for remaining questions in the current batch and all subsequent batches. Phase 2 completes either way.
aws-design.json missing required clustersStop Phase 4. Output: "Re-run Phase 3 to generate missing cluster designs."

Defaults

  • IaC output: Terraform configurations, migration scripts, AI migration code, and documentation

  • Region: us-east-1 (unless user specifies, or GCP region → AWS region mapping suggests otherwise)

  • Sizing: Development tier (e.g., db.t4g.micro for databases, 0.5 CPU for Fargate)

  • Migration mode: Adapts based on available inputs (infrastructure, AI, or billing-only)

  • Cost currency: USD

  • Timeline assumption: 2-16 weeks depending on migration complexity — small (2-6 weeks), medium (6-12 weeks), large (12-18 weeks). See references/shared/migration-complexity.md for tier definitions.

Workflow Execution

When invoked, the agent MUST follow this exact sequence:

  1. Load phase status: Read .phase-status.json from .migration/*/.

    • If missing: Initialize for Phase 1 (Discover)

    • If exists: Determine current phase using deterministic rules in State Machine

  2. Determine phase to execute:

    • If current_phase exists: execute that phase.

    • Otherwise execute the first non-completed phase in ordered list: discover → clarify → design → estimate → generate.

    • If all ordered phases are completed: migration is complete (with feedback finalization rule).

  3. Read phase reference: Load the full reference file for the target phase.

  4. Execute ALL steps in order: Follow every numbered step in the reference file. Do not skip, optimize, or deviate.

  5. Validate outputs: Confirm all required output files exist with correct schema before proceeding. Phase orchestrators run Completion Handoff Gate checks per shared/handoff-gates.md.

  6. Handoff gate: Emit HANDOFF_OK or GATE_FAIL per shared/handoff-gates.md. On GATE_FAIL, stop — do not update phase status or load the next phase.

  7. Update phase status: Only after HANDOFF_OK. Use the Phase Status Update Protocol (read-merge-write) in the same turn as the phase's final output message.

  8. Feedback checkpoint: After a phase completes, check if feedback is due (see rules below). This runs before advancing to the next phase.

    • After Discover (if phases.feedback is "pending"): Output to user: "Would you like to share quick feedback (5 optional questions + anonymized usage data) to help improve this tool? Your data never includes resource names, file paths, or account IDs. [A] Send feedback now [B] Wait until after the Estimate phase"

      • If user picks A → Load references/phases/feedback/feedback.md, execute it, then continue to Clarify.

      • If user picks B → Continue to Clarify (feedback stays "pending").

    • After Estimate (if phases.feedback is "pending"): Output to user: "Would you like to share quick feedback now? (5 optional questions + anonymized usage data) [A] Yes, share feedback [B] No thanks, continue to Generate"

      • If user picks A → Load references/phases/feedback/feedback.md, execute it, then continue to Generate.

      • If user picks B → Use the Phase Status Update Protocol to set phases.feedback to "completed". Continue to Generate.

    • After Generate: No feedback offer. If phases.feedback is still "pending", use the Phase Status Update Protocol to set it to "completed" (user had two chances and chose to defer/skip).

  9. Display summary: Show user what was accomplished, highlight next phase, or confirm migration completion.

Critical constraint: Agent must strictly adhere to the reference file's workflow. If unable to complete a step, stop and report the exact step that failed.

User can invoke the skill again to resume from current_phase (or deterministic ordered evaluation when current_phase is absent).

Scope Notes

v1.0 includes:

  • Terraform infrastructure discovery

  • App code scanning (AI workload detection)

  • Billing data import from GCP

  • User requirement clarification (adaptive questions by category)

  • Multi-path Design (infrastructure, AI workloads, billing-only fallback)

  • AWS cost estimation (from pricing API or fallback)

  • Migration artifact generation (Terraform, scripts, AI adapters, documentation)

  • Optional feedback collection with anonymized telemetry

Files

references/phases/discover/discover-preview.md

Migration Preview Heuristic

Loaded by discover.md Step 3 to compute a lightweight preview signal and rough cost estimate from discovery artifacts alone — before Clarify, Design, or Estimate run. This is NOT the full complexity tier (that lives in migration-complexity.md and requires preferences + billing). This is a fast, honest "at a glance" for the user.


Route Detection

Before executing Steps 1–6, determine which route applies:

IF gcp-resource-inventory.json does NOT exist
   AND ai-workload-profile.json exists
THEN route = "ai_only"

ELSE
  route = "infra"   // covers infra-only, hybrid infra+AI, and billing-only
END

AI-only route executes Steps 1A–6A below. Infra route executes Steps 1–6 below (original behavior, unchanged).


AI-Only Route (Steps 1A–6A)

Used when only ai-workload-profile.json exists — no Terraform, no billing data. Infrastructure stays on GCP; only AI/LLM calls move to AWS Bedrock.

Step 1A: Compute AI complexity_signal

Read from ai-workload-profile.json:

InputSourceKey
model_countai-workload-profile.jsonCount of distinct entries in models[]
is_agenticai-workload-profile.jsonagentic_profile.is_agentic == true
has_multi_model_routingai-workload-profile.jsonintegration.gateway_type is "openrouter", "litellm", "kong", or "apigee"
has_multiple_providersai-workload-profile.jsonsummary.ai_source == "both" or distinct provider values across models[] > 1
capability_countai-workload-profile.jsonCount of true values in integration.capabilities_summary

Classify (first match wins, top to bottom):

IF is_agentic == true
   OR has_multi_model_routing == true
   OR model_count > 3
   OR has_multiple_providers == true
THEN ai_complexity_signal = "complex"

ELSE IF model_count == 1
        AND is_agentic != true
        AND has_multi_model_routing != true
        AND capability_count <= 2
THEN ai_complexity_signal = "likely_simple"

ELSE
  ai_complexity_signal = "standard"
END

Fast-path eligibility: Always false for AI-only route — AI profiles always route to full Clarify.

eligible_for_clarify_fast_path = false

Step 2A: Build per-token price comparison

Purpose: Show the user what their models map to on Bedrock and whether the per-token price is higher, lower, or roughly equivalent. Do NOT compute a monthly dollar total — usage volume is unknown at Discover time and will be collected in Clarify (Q3, Q7).

For each model in models[] of ai-workload-profile.json, map to the closest Bedrock equivalent using the table below, then look up both source and Bedrock per-token prices from references/shared/pricing-cache.md (Source Provider Pricing + Bedrock Models sections).

Source model → Bedrock equivalent mapping:

Source model patternBedrock equivalentBedrock model ID
gpt-4o, gpt-4.1, gpt-5.* flagshipClaude Sonnet 4.6anthropic.claude-sonnet-4-6
gpt-4o-mini, gpt-4.1-mini, gpt-5.*-miniClaude Haiku 4.5anthropic.claude-haiku-4-5-20251001-v1:0
gpt-3.5-turbo, gpt-4.1-nano, gpt-5.*-nanoAmazon Nova Microamazon.nova-micro-v1:0
o3, o4-mini, reasoning modelsClaude Sonnet 4.6anthropic.claude-sonnet-4-6
gemini-2.5-pro, gemini-3.*-proClaude Sonnet 4.6anthropic.claude-sonnet-4-6
gemini-2.5-flash, gemini-2.0-flashClaude Haiku 4.5anthropic.claude-haiku-4-5-20251001-v1:0
gemini-2.0-flash-liteAmazon Nova Liteamazon.nova-lite-v1:0
claude-3-5-sonnet, claude-sonnet-*Claude Sonnet 4.6anthropic.claude-sonnet-4-6
claude-3-5-haiku, claude-haiku-*Claude Haiku 4.5anthropic.claude-haiku-4-5-20251001-v1:0
claude-3-opus, claude-opus-*Claude Opus 4.6anthropic.claude-opus-4-6-v1
text-embedding-*, *-embedding-*Amazon Titan Embeddings v2amazon.titan-embed-text-v2:0
dall-e-*, imagen-*, image generationAmazon Nova Canvasamazon.nova-canvas-v1:0
whisper-*, speech-to-textAmazon Transcribe(non-token service — note separately)
tts-*, text-to-speechAmazon Polly(non-token service — note separately)
Unknown / otherAmazon Nova Proamazon.nova-pro-v1:0

For each mapped model pair, record source_model and bedrock_equivalent (model name only). Do NOT compute or display per-token pricing comparisons at this stage — cost analysis belongs in the Estimate phase where full usage volume context is available.


Step 3A: Build key_decisions_ahead

Generate 2-4 bullets based on what was detected in ai-workload-profile.json:

SignalDecision bullet
Always"Bedrock model selection for [list detected model IDs, max 3, then '+ N more']"
is_agentic == true"Agentic migration path (retarget / AgentCore Harness / Strands)"
has_multi_model_routing == true"Multi-model routing strategy on Bedrock (LiteLLM adapter vs native routing)"
has_multiple_providers == true"Re-embedding requirements and cascade pair testing across providers"
integration.pattern == "streaming""Streaming transport layer (Bedrock streaming vs current SDK)"

Cap at 4 bullets.


Step 4A: Build timeline_hint string
ai_complexity_signaltimeline_hint
likely_simple"1-3 weeks (single model swap; confirm after Clarify)"
standard"2-6 weeks (multi-model migration; confirm after Clarify)"
complex"4-8 weeks (agentic or multi-provider stack; confirm after Clarify)"

Always append "(confirm after Clarify)" — full classification requires preferences.


Step 5A: Write migration-preview.json (AI-only)

Write $MIGRATION_DIR/migration-preview.json:

{
  "preview_version": 1,
  "computed_at": "<ISO 8601 UTC>",
  "route": "ai_only",
  "primary_resource_count": 0,
  "complexity_signal": "standard",
  "ai_complexity_signal": "standard",
  "eligible_for_clarify_fast_path": false,
  "services_summary": [],
  "ai_summary": {
    "model_count": 2,
    "model_ids": ["gpt-4o", "text-embedding-3-small"],
    "bedrock_targets": [
      {
        "source_model": "gpt-4o",
        "source_input_per_1m": 2.50,
        "source_output_per_1m": 10.00,
        "bedrock_equivalent": "Claude Sonnet 4.6",
        "bedrock_model_id": "anthropic.claude-sonnet-4-6",
        "bedrock_input_per_1m": 3.00,
        "bedrock_output_per_1m": 15.00,
        "cost_direction": "higher"
      },
      {
        "source_model": "text-embedding-3-small",
        "source_input_per_1m": 0.02,
        "source_output_per_1m": null,
        "bedrock_equivalent": "Amazon Titan Embeddings v2",
        "bedrock_model_id": "amazon.titan-embed-text-v2:0",
        "bedrock_input_per_1m": 0.02,
        "bedrock_output_per_1m": null,
        "cost_direction": "lower"
      }
    ],
    "is_agentic": false,
    "has_multi_model_routing": false,
    "gateway_type": "direct"
  },
  "cost_preview": {
    "monthly_estimate": null,
    "monthly_estimate_note": "Monthly estimate available after Clarify (usage volume collected in Q3, Q7)",
    "disclaimer": "Per-token prices from pricing-cache.md; full cost analysis in Estimate phase"
  },
  "timeline_hint": "2-6 weeks (multi-model migration; confirm after Clarify)",
  "ai_detected": true,
  "key_decisions_ahead": [
    "Bedrock model selection for gpt-4o, text-embedding-3-small",
    "Streaming transport layer (Bedrock streaming vs current SDK)"
  ]
}

Field rules:

  • route is "ai_only" for this path

  • primary_resource_count is 0 for AI-only runs (no IaC)

  • complexity_signal mirrors ai_complexity_signal for downstream consumers

  • services_summary is [] for AI-only runs

  • ai_summary.bedrock_targets lists one entry per distinct source model with actual per-token prices

  • cost_preview.monthly_estimate is always null at Discover time — no invented token volumes

  • eligible_for_clarify_fast_path is always false for AI-only route


Step 6A: Build preview chat message (AI-only)

Output this block as part of discover.md Step 3's user message (chat only — not a file):

### Your AI migration at a glance *(preview — not final)*

| | |
|---|---|
| **Models detected** | [model_ids joined by ", "] |
| **Bedrock targets** | [for each bedrock_target: "source_model → bedrock_equivalent"] |
| **Routing** | [if has_multi_model_routing: gateway_type + " (multi-model routing)" else "Direct SDK"] |
| **Monthly estimate** | Available after Estimate phase |
| **Timeline (rough)** | [timeline_hint] |
| **Decisions ahead** | [key_decisions_ahead joined by "; "] |

*Full cost breakdown in Estimate; runnable adapter code in Generate.*
AI workload detected — full Clarify recommended for best results.

Do NOT write this to a file. Chat output only.


Infra Route (Steps 1–6)

Used when gcp-resource-inventory.json exists (infra-only, hybrid infra+AI, or billing-only). Original behavior — unchanged.

Step 1: Compute complexity_signal

Read from available discovery artifacts:

InputSourceKey
primary_resource_countgcp-resource-inventory.jsonCount resources where classification: "PRIMARY"
has_databasegcp-resource-inventory.jsonAny resource type matching google_sql_*, google_spanner_*, google_redis_*
has_bigquerygcp-resource-inventory.json or billing-profile.jsonAny google_bigquery_* resource or BigQuery billing SKU
has_ai_profileFile presenceai-workload-profile.json exists
is_agenticai-workload-profile.jsonagentic_profile.is_agentic == true (if file exists)
billing_monthly_usdbilling-profile.jsonsummary.total_monthly_spend (null if absent)

Classify (first match wins, top to bottom):

IF has_bigquery
   OR is_agentic == true
   OR primary_resource_count > 8
   OR (billing_monthly_usd != null AND billing_monthly_usd > 10000)
THEN complexity_signal = "complex"
ELSE IF primary_resource_count <= 3
        AND has_database == false
        AND has_bigquery == false
        AND is_agentic != true
        AND (billing_monthly_usd == null OR billing_monthly_usd < 1000)
THEN complexity_signal = "likely_simple"
ELSE
  complexity_signal = "standard"
END

Fast-path eligibility:

eligible_for_clarify_fast_path =
   complexity_signal == "likely_simple"
   AND has_ai_profile == false

eligible_for_clarify_simple_path =
   complexity_signal == "likely_simple"
   AND has_ai_profile == true
   AND is_agentic != true
   AND ai_complexity_signal == "likely_simple"

ai_complexity_signal (compute when ai-workload-profile.json exists):

IF agentic_profile.is_agentic == true
   OR integration.frameworks is non-empty (LangChain, CrewAI, etc.)
   OR models.length > 3
THEN ai_complexity_signal = "standard"

ELSE IF integration.pattern in ("direct_sdk", "direct")
   AND models.length <= 2
   AND agentic_profile is absent
THEN ai_complexity_signal = "likely_simple"

ELSE
   ai_complexity_signal = "standard"
END

Step 2: Compute rough AWS cost range

Purpose: Give the user a ballpark before Estimate runs. Always label as rough. Never invent GCP spend if billing data is absent.

Service type -> dev-tier AWS line item mapping

For each PRIMARY resource in gcp-resource-inventory.json, map to a dev-tier AWS equivalent and look up its monthly cost from references/shared/pricing-cache.md:

GCP Primary TypeTypical AWS TargetDev-tier sizing for preview
google_cloud_run_v2_service / google_cloud_run_serviceFargate0.5 vCPU, 1GB RAM, 730 hrs/mo
google_cloudfunctions_function / google_cloudfunctions2_functionLambda1M requests, 128MB, 200ms avg
google_compute_instanceEC2 t4g.smallOn-demand, us-east-1
google_container_clusterEKS (2x t4g.small nodes)On-demand, us-east-1
google_sql_database_instanceRDS db.t4g.microSingle-AZ, gp3 20GB
google_redis_instanceElastiCache cache.t4g.microSingle-AZ
google_storage_bucketS350GB standard + 10K GET + 1K PUT
google_pubsub_topicSQS1M requests/mo
google_filestore_instanceEFS10GB standard
google_spanner_instanceAurora Serverless v20.5-1 ACU
google_bigquery_datasetDeferred -- specialist$0 (not estimated)

Sum the dev-tier line items to get aws_monthly_range_usd.low. Multiply by 1.5 for high (accounts for NAT gateway, data transfer, CloudWatch, and sizing variance).

If billing-profile.json exists: Set gcp_monthly_usd from summary.total_monthly_spend. Show both GCP actual and AWS range.

If only IaC: Set gcp_monthly_usd: null. Show AWS range only -- never invent GCP spend.

If neither IaC nor billing: Omit cost preview entirely (cost_preview: null).


Step 3: Build key_decisions_ahead

Generate 2-4 bullets based on what was detected. Use only signals present in discovery artifacts:

SignalDecision bullet
Any compute resource"Target region and deployment model (Fargate vs EKS)"
has_database == true"Database migration tooling and cutover window"
has_ai_profile == true"Bedrock model selection for [detected model IDs]"
is_agentic == true"Agentic migration path (retarget / Harness / Strands)"
has_bigquery == true"BigQuery analytics target (specialist engagement required)"

Always include "Target region" if any compute is present. Cap at 4 bullets.


Step 4: Build timeline_hint string

complexity_signaltimeline_hint
likely_simple"3-6 weeks (likely simple infra; confirm after Clarify)"
standard"8-12 weeks (standard migration; confirm after Clarify)"
complex"12-16+ weeks (complex stack; confirm after Clarify)"

Always append "(confirm after Clarify)" -- full tier classification requires preferences.


Step 5: Write migration-preview.json

Write $MIGRATION_DIR/migration-preview.json:

{
  "preview_version": 1,
  "computed_at": "<ISO timestamp>",
  "primary_resource_count": 3,
  "complexity_signal": "likely_simple",
  "eligible_for_clarify_fast_path": true,
  "eligible_for_clarify_simple_path": false,
  "ai_complexity_signal": null,
  "services_summary": [
    { "gcp_type": "google_cloud_run_v2_service", "typical_aws_target": "Fargate" },
    { "gcp_type": "google_storage_bucket", "typical_aws_target": "S3" }
  ],
  "cost_preview": {
    "gcp_monthly_usd": 240.00,
    "aws_monthly_range_usd": { "low": 120, "high": 180 },
    "disclaimer": "Dev-tier rough estimate (+-30%); full analysis in Estimate phase"
  },
  "timeline_hint": "3-6 weeks (likely simple infra; confirm after Clarify)",
  "ai_detected": false,
  "key_decisions_ahead": [
    "Target region and deployment model (Fargate vs EKS)",
    "Cutover window"
  ]
}

Field rules:

  • cost_preview is null if neither IaC nor billing data was available

  • cost_preview.gcp_monthly_usd is null if no billing data (IaC-only run)

  • ai_detected is true if ai-workload-profile.json exists

  • services_summary lists only PRIMARY resources, deduplicated by gcp_type

  • eligible_for_clarify_fast_path is false whenever ai_detected == true, regardless of infra complexity

  • eligible_for_clarify_simple_path is true only when ai_detected == true, complexity_signal == "likely_simple", and ai_complexity_signal == "likely_simple" (non-agentic direct SDK, ≤2 models)

  • ai_complexity_signal is null when no AI profile exists; otherwise "likely_simple" or "standard"


Step 6: Build preview chat message

Output this block as part of discover.md Step 3's user message (chat only -- not a file):

### Your migration at a glance *(preview -- not final)*

| | |
|---|---|
| **Services** | [primary_resource_count] resources -> [services_summary as "Fargate, S3"] *(standard pairings)* |
| **AWS cost (rough)** | ~$[low]-$[high]/mo [vs GCP ~$[gcp]/mo if billing present] *(dev-tier estimate, +-30%)* |
| **Timeline (rough)** | [timeline_hint] |
| **AI** | [if ai_detected: "[model IDs] detected -- AI migration path will run" else "None detected"] |
| **Decisions ahead** | [key_decisions_ahead joined by "; "] |

*Full cost breakdown in Estimate; runnable Terraform in Generate.*

[if eligible_for_clarify_fast_path: "Your stack looks straightforward -- next step is 3 quick questions."]
[if eligible_for_clarify_simple_path: "Simple stack with lightweight AI detected -- next step is a short question set (~6 questions)."]
[if ai_detected and not eligible_for_clarify_simple_path and not eligible_for_clarify_fast_path: "AI workload detected -- full Clarify recommended for best results."]

Do NOT write this to a file. Chat output only.

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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