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Get Started Free →Design, review, and validate Azure cloud architectures. Use when choosing Azure compute, storage, networking, or identity services, or applying the Azure Well-Architected Framework to a workload.
.claude/skills/borghei-azure-cloud-architect/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-03 | ✗→✓ | ▲ Improved | 57% | 0% |
| case-04 | ✗→✓ | ▲ Improved | -10% | 0% |
| case-05 | ✗→✓ | ▲ Improved | 12% | 0% |
| case-06 | ✗→✓ | ▲ Improved | 21% | 0% |
| case-07 | ✗→✓ | ▲ Improved | -18% | 0% |
End-to-end Azure-specific architecture: service selection, Well-Architected Framework assessment, identity and networking patterns, cost optimization, and operational defaults. Provider-specific complement to our generic senior-cloud-architect skill — that one covers cross-cloud patterns; this one knows AKS pricing tiers, when to pick Cosmos over SQL DB, and how Front Door differs from Application Gateway.
| Situation | Skill applies | |-----------|---------------| | Designing an Azure architecture from scratch | Yes — start with the compute decision tree | | Reviewing an existing Azure architecture | Yes — run WAF assessment via scripts/azure_waf_scorer.py | | Validating an ARM/Bicep/Terraform plan | Yes — scripts/azure_architecture_validator.py | | Estimating Azure cost for a workload | Yes — scripts/azure_cost_estimator.py | | Picking compute, data store, networking, or identity | Yes — see the decision-trees reference | | Going to production without WAF review | Don't — run the WAF scorer first |
Before designing or assessing, confirm these inputs. If any is unknown or vague, ASK — do not assume:
azure_architecture_validator.py vs azure_cost_estimator.py vs azure_waf_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 | |------|---------|---------| | azure_architecture_validator.py | Validate a Bicep/ARM/YAML workload for anti-patterns and missing best-practice settings | python scripts/azure_architecture_validator.py --bicep ./infra/*.bicep | | azure_cost_estimator.py | Estimate monthly Azure cost from a YAML workload spec | python scripts/azure_cost_estimator.py --workload-config workload.yaml | | azure_waf_scorer.py | Score a workload against the five Well-Architected pillars | python scripts/azure_waf_scorer.py --workload-config workload.yaml |
All scripts: stdlib only, argparse CLI, JSON or markdown output (--format).
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/gcp-cloud-architect — GCP counterpartengineering/kubernetes-operator — for AKS operator-pattern workloadsra-qm-team/information-security-manager-iso27001 — for compliance-mapped controls (Azure has built-in Defender / Compliance Manager)ra-qm-team/soc2-compliance-expert — Azure-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-15 | pass→pass | 8,810 | 11,687 | +33% | 1 | 1 | 0% | 1,500 | 3,169 | +111% | 0 | 0 | — |
case-01 | fail→fail | 15,926 | 12,067 | -24% | 1 | 1 | 0% | 1,344 | 3,154 | +135% | 0 | 0 | — |
case-02 | fail→fail | 21,851 | 10,800 | -51% | 1 | 1 | 0% | 2,842 | 2,963 | +4% | 0 | 0 | — |
case-03 | fail→pass | 6,419 | 3,402 | -47% | 1 | 1 | 0% | 1,155 | 1,816 | +57% | 0 | 0 | — |
case-04 | fail→pass | 11,235 | 2,188 | -81% | 1 | 1 | 0% | 1,763 | 1,590 | -10% | 0 | 0 | — |
case-05 | fail→pass | 9,034 | 2,425 | -73% | 1 | 1 | 0% | 1,484 | 1,665 | +12% | 0 | 0 | — |
case-06 | fail→pass | 8,757 | 2,307 | -74% | 1 | 1 | 0% | 1,336 | 1,614 | +21% | 0 | 0 | — |
case-07 | fail→pass | 13,104 | 2,489 | -81% | 1 | 1 | 0% | 2,014 | 1,653 | -18% | 0 | 0 | — |
case-08 | fail→pass | 8,731 | 2,566 | -71% | 1 | 1 | 0% | 1,451 | 1,708 | +18% | 0 | 0 | — |
case-14 | fail→fail | 19,242 | 21,651 | +13% | 1 | 1 | 0% | 3,114 | 4,803 | +54% | 0 | 0 | — |
case-09 | fail→pass | 12,511 | 5,804 | -54% | 1 | 1 | 0% | 2,015 | 2,144 | +6% | 0 | 0 | — |
case-10 | fail→pass | 8,587 | 2,520 | -71% | 1 | 1 | 0% | 1,415 | 1,693 | +20% | 0 | 0 | — |
case-11 | fail→pass | 12,125 | 16,540 | +36% | 1 | 1 | 0% | 1,930 | 4,058 | +110% | 0 | 0 | — |
case-12 | fail→fail | 19,549 | 17,859 | -9% | 1 | 1 | 0% | 3,492 | 4,385 | +26% | 0 | 0 | — |
case-13 | fail→fail | 20,321 | 20,122 | -1% | 1 | 1 | 0% | 3,433 | 4,741 | +38% | 0 | 0 | — |
case-16 | pass→pass | 12,241 | 11,227 | -8% | 1 | 1 | 0% | 1,922 | 2,993 | +56% | 0 | 0 | — |
case-17 | pass→pass | 11,471 | 11,166 | -3% | 1 | 1 | 0% | 1,954 | 3,070 | +57% | 0 | 0 | — |
case-18 | pass→pass | 7,532 | 8,100 | +8% | 1 | 1 | 0% | 1,397 | 2,668 | +91% | 0 | 0 | — |
case-19 | pass→pass | 9,872 | 2,577 | -74% | 1 | 1 | 0% | 1,710 | 1,695 | -1% | 0 | 0 | — |
case-20 | pass→pass | 4,949 | 6,237 | +26% | 1 | 1 | 0% | 668 | 2,254 | +237% | 0 | 0 | — |
case-21 | pass→pass | 5,788 | 6,055 | +5% | 1 | 1 | 0% | 1,004 | 2,298 | +129% | 0 | 0 | — |
case-22 | pass→pass | 7,829 | 11,081 | +42% | 1 | 1 | 0% | 1,388 | 3,065 | +121% | 0 | 0 | — |
case-23 | pass→pass | 10,114 | 10,745 | +6% | 1 | 1 | 0% | 1,893 | 3,231 | +71% | 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 +39 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.