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Get Started Free →Design, review, and validate Google Cloud (GCP) architectures. Use when choosing GCP compute, storage, networking, or identity services, or applying the Google Cloud Architecture Framework (reliability, security, cost, performance).
.claude/skills/borghei-gcp-cloud-architect/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-02 | ✗→✓ | ▲ Improved | 2% | 0% |
| case-08 | ✓→✓ | = Same ✓ | 126% | 0% |
| case-01 | ✓→✓ | = Same ✓ | 50% | 0% |
| case-03 | ✓→✓ | = Same ✓ | 142% | 0% |
| case-04 | ✓→✓ | = Same ✓ | 97% | 0% |
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.
| Tool | Purpose | Command | |------|---------|---------| | gcp_architecture_validator.py | Validate a Terraform plan or YAML workload spec for anti-patterns | python scripts/gcp_architecture_validator.py --terraform ./infra/*.tf | | gcp_cost_estimator.py | Estimate monthly GCP cost from a workload spec, with optimization opportunities | python scripts/gcp_cost_estimator.py --workload-config workload.yaml | | gcp_caf_scorer.py | Score a workload against the five Cloud Architecture Framework pillars | python scripts/gcp_caf_scorer.py --workload-config workload.yaml |
Load the reference that matches the task — keep this file lean and pull detail on demand:
engineering/senior-cloud-architect — generic multi-cloud architecture patternsengineering/aws-solution-architect — AWS counterpartengineering/azure-cloud-architect — Azure counterpartengineering/kubernetes-operator — for GKE operator-pattern workloadsra-qm-team/information-security-manager-iso27001 — compliance-mapped controls (GCP has Security Command Center)ra-qm-team/soc2-compliance-expert — GCP-specific SOC 2 evidence collection| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-08 | pass→pass | 6,055 | 6,096 | +1% | 1 | 1 | 0% | 1,194 | 2,702 | +126% | 0 | 0 | — |
case-01 | pass→pass | 17,195 | 19,992 | +16% | 1 | 1 | 0% | 3,112 | 4,682 | +50% | 0 | 0 | — |
case-02 | fail→pass | 10,561 | 3,495 | -67% | 1 | 1 | 0% | 1,875 | 1,908 | +2% | 0 | 0 | — |
case-03 | pass→pass | 8,425 | 11,755 | +40% | 1 | 1 | 0% | 1,392 | 3,373 | +142% | 0 | 0 | — |
case-04 | pass→pass | 9,882 | 11,979 | +21% | 1 | 1 | 0% | 1,704 | 3,355 | +97% | 0 | 0 | — |
case-05 | pass→pass | 6,027 | 8,451 | +40% | 1 | 1 | 0% | 1,115 | 3,007 | +170% | 0 | 0 | — |
case-06 | fail→fail | 9,956 | 15,548 | +56% | 1 | 1 | 0% | 1,725 | 4,164 | +141% | 0 | 0 | — |
case-07 | pass→pass | 5,266 | 5,135 | -2% | 1 | 1 | 0% | 1,033 | 2,466 | +139% | 0 | 0 | — |
case-09 | pass→pass | 6,662 | 11,507 | +73% | 1 | 1 | 0% | 1,024 | 3,529 | +245% | 0 | 0 | — |
case-10 | pass→pass | 7,929 | 10,701 | +35% | 1 | 1 | 0% | 1,213 | 2,996 | +147% | 0 | 0 | — |
case-11 | pass→pass | 10,392 | 11,320 | +9% | 1 | 1 | 0% | 1,514 | 3,230 | +113% | 0 | 0 | — |
case-12 | pass→pass | 10,928 | 11,136 | +2% | 1 | 1 | 0% | 1,748 | 3,421 | +96% | 0 | 0 | — |
case-13 | pass→pass | 4,191 | 6,423 | +53% | 1 | 1 | 0% | 626 | 2,539 | +306% | 0 | 0 | — |
case-14 | pass→pass | 10,170 | 14,385 | +41% | 1 | 1 | 0% | 1,898 | 3,792 | +100% | 0 | 0 | — |
case-15 | pass→pass | 5,123 | 9,998 | +95% | 1 | 1 | 0% | 802 | 3,064 | +282% | 0 | 0 | — |
case-16 | pass→pass | 11,065 | 19,346 | +75% | 1 | 1 | 0% | 1,823 | 4,215 | +131% | 0 | 0 | — |
case-17 | pass→pass | 6,938 | 11,916 | +72% | 1 | 1 | 0% | 1,053 | 3,386 | +222% | 0 | 0 | — |
case-18 | pass→pass | 7,613 | 11,119 | +46% | 1 | 1 | 0% | 1,228 | 3,210 | +161% | 0 | 0 | — |
case-19 | fail→fail | 3,422 | 5,027 | +47% | 1 | 1 | 0% | 459 | 2,202 | +380% | 0 | 0 | — |
case-20 | pass→pass | 4,871 | 7,347 | +51% | 1 | 1 | 0% | 630 | 2,426 | +285% | 0 | 0 | — |
case-21 | fail→fail | 20,738 | 17,716 | -15% | 1 | 1 | 0% | 3,231 | 4,229 | +31% | 0 | 0 | — |
case-22 | fail→fail | 13,754 | 21,874 | +59% | 1 | 1 | 0% | 2,316 | 4,561 | +97% | 0 | 0 | — |
case-23 | fail→fail | 6,406 | 8,142 | +27% | 1 | 1 | 0% | 1,003 | 2,632 | +162% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 23 cases were attempted. The headline lift of +4 percentage points is the difference between those two pass rates over the 23 comparable cases.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.