This skill should be used when the user asks to "check accessibility", "audit WCAG compliance", "scan HTML for a11y issues", "check color contrast", or "find accessibility violations in web pages".
日本語の概要は準備中です。原文の説明を表示しています。
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.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
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まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
This skill should be used when the user asks to "check accessibility", "audit WCAG compliance", "scan HTML for a11y issues", "check color contrast", or "find accessibility violations in web pages".
日本語の概要は準備中です。原文の説明を表示しています。
Design and run statistically rigorous A/B tests and experiments. Use when planning experiments, calculating sample sizes, designing test variants, selecting metrics, analyzing results, or when someone says "let's test that."
日本語の概要は準備中です。原文の説明を表示しています。
Design and analyze A/B tests: sample size, test duration, and statistical significance for conversion experiments. Use when setting up an A/B test, calculating sample size, designing an experiment, or analyzing results.
日本語の概要は準備中です。原文の説明を表示しています。
Sales execution across pipeline, discovery, demos, negotiation, and closing. Use when qualifying opportunities, running MEDDIC discovery, building account plans, handling objections, structuring proposals, or forecasting pipeline.
日本語の概要は準備中です。原文の説明を表示しています。
Design ad creative across Google, Meta, LinkedIn, Twitter/X, and TikTok with platform format specs, headline formulas, and A/B testing. Use when writing ad copy, generating headline variations, creating ad sets, or validating creative.
日本語の概要は準備中です。原文の説明を表示しています。
Answer Engine Optimization (AEO): optimize content to be cited by LLMs (ChatGPT, Claude, Perplexity, Gemini) in their answers. Use when designing content for LLM citation, auditing citability, or structuring Q&A schema.
日本語の概要は準備中です。原文の説明を表示しています。