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Design, review, and validate Google Cloud (GCP) architectures.
Passed 18 of 23 test cases with the skill, and 17 of 23 without it.
1 fixed17 passed either way5 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
A test case the skill fixed. Same model, same prompt, with and without the skill.
I have a directory of Terraform files that define our GCP infrastructure, including VPC networks, Kubernetes clusters, and service account IAM bindings. Please run an architectural audit on these IaC manifests to detect any configuration anti-patterns and assess how well our setup aligns with Google Cloud Architecture Framework standards.
| Checked | Without skill | With skill |
|---|---|---|
| Ask the user to confirm the priority pillar for the Cloud Architecture Framework assessment. | Missed | Passed |
The instructions your model reads when the skill loads.
End-to-end GCP-specific architecture: service selection, Google Cloud Architecture Framework assessment, identity and networking patterns, cost optimization, operational defaults. Provider-specific complement to our generic senior-cloud-architect skill — that one covers cross-cloud patterns; this one knows when to pick Spanner over Cloud SQL, how Workload Identity Federation differs from Service Account keys, and the right Cloud Run vs GKE call.
| Situation | Skill applies |
|---|---|
| Designing a GCP architecture from scratch | Yes — start with compute decision tree |
| Reviewing an existing GCP architecture | Yes — run CAF assessment via scripts/gcp_caf_scorer.py |
| Validating a Terraform / Deployment Manager plan | Yes — scripts/gcp_architecture_validator.py |
| Estimating GCP cost for a workload | Yes — scripts/gcp_cost_estimator.py |
| Picking between GKE / Cloud Run / Functions / Cloud Run Jobs | Yes — see compute decision tree |
| Setting up IAM / Workload Identity correctly | Yes — see identity reference |
| Designing multi-region / multi-zone resilience | Yes — see reliability reference |
| Picking Cloud SQL vs Spanner vs Firestore vs BigQuery | Yes — see data store decision tree |
| Going to production without CAF review | Don't — run the CAF scorer first |
Before designing or assessing, confirm these inputs. If any is unknown or vague, ASK — do not assume:
gcp_architecture_validator.py vs gcp_cost_estimator.py vs gcp_caf_scorer.py)Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.
test cases passed, vs 17 / 23 without
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