メニュー
Establish data ownership, quality standards, compliance policies, and metadata management. Build organizational data practices. Use when defining data strategy or improving data quality and compliance.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
Identify and avoid common architectural mistakes. Recognize patterns of failure. Use when reviewing designs or learning from mistakes.
Practice architectural design with structured problems. Time-limited design challenges. Use for skill development, interviews, team exercises.
Reference catalog of proven architecture patterns. Know when to apply each pattern, tradeoffs, and examples. Use as reference when designing systems.
Lead effective architecture reviews. Manage discussions, surface disagreements, build consensus, document decisions. Use when conducting reviews or running architecture forums.
Prepare for architecture interview questions and scenarios. Practice system design, tradeoff discussions, communication. Use for career development and interview readiness.
Conduct comprehensive system architecture evaluation. Assess design quality, technical debt, operational readiness, scalability. Use when auditing existing systems.
| name | data-governance |
| description | Establish data ownership, quality standards, compliance policies, and metadata management. Build organizational data practices. Use when defining data strategy or improving data quality and compliance. |
Establish organizational data management practices, ownership, quality standards, and compliance policies.
You are building data governance practices. Define who owns data, how quality is enforced, compliance requirements, and metadata standards. Read organizational context, regulatory requirements, and existing data practices.
Based on enterprise data management practices:
Define Data Owner Roles: Who is accountable for each data domain? Product for customer data, Finance for transaction data. Owners define quality standards and usage policies.
Establish Metadata Catalog: Inventory all data assets: databases, tables, APIs, data lakes. For each: owner, description, lineage (source), refresh frequency, classification (public/confidential/PII).
Set Quality Standards: For critical datasets, define SLAs: freshness (how recent), completeness (% non-null), accuracy (validated against source), consistency (matches across systems).
Build Data Dictionary: Business definitions of key entities and attributes. Customer means "active subscriber". Amount means "invoice total in USD". Shared vocabulary reduces confusion.
Implement Access Control: Who can access what data? Implement principle of least privilege. GDPR: individuals can request deletion; log access to sensitive data.
