Business & Operations

Expert guidance for designing, optimizing, and maintaining database schemas for SQL and NoSQL systems. Use when creating new databases, optimizing existing schemas, planning migrations, implementing security policies, or ensuring GDPR compliance. Covers normalization, indexing, data types, relationships, performance optimization, and audit logging.

Provides expert design guidance for creating truthful, clear, beautiful data visualizations. Focuses on **DESIGN DECISIONS ONLY**—chart selection, color strategy, visual encoding, and validation. Assumes data is accurate and prepared. Auto-activates when user mentions: data viz, dashboard, chart type, visualization, infographic

Implementing comprehensive validation rules across database, application, and pipeline layers to ensure data integrity.

Use when designing database schemas, need to model domain entities and relationships clearly, building knowledge graphs or ontologies, creating API data models, defining system boundaries and invariants, migrating between data models, establishing taxonomies or hierarchies, user mentions "schema", "data model", "entities", "relationships", "ontology", "knowledge graph", or when scattered/inconsistent data structures need formalization.

Techniques and tools for ensuring the accuracy, completeness, and reliability of data across the pipeline.

Enforce data quality rules and validations on pilot data streams and repositories. Use when checking for missing values, schema compliance, consistency issues, or anomalies before analysis and reporting.

Polibaseのデータ処理ワークフローとパイプラインを説明します。議事録処理、Web scraping、政治家データ収集、話者マッチングなどの処理フロー、依存関係、実行順序を理解する際にアクティベートされます。

Coordinates data pipeline tasks (ETL, analytics, feature engineering). Use when implementing data ingestion, transformations, quality checks, or analytics. Applies data-quality-standard.md (95% minimum).

Data modeling with Entity-Relationship Diagrams (ERDs), data dictionaries, and conceptual/logical/physical models. Documents data structures, relationships, and attributes.

Expert-level data mesh architecture, domain-oriented ownership, data products, federated governance, and self-serve platforms

Mapping the flow of data from source to destination for transparency, impact analysis, and troubleshooting.

Data Lake architecture and management including medallion architecture (bronze/silver/gold zones), data catalog with AWS Glue, partitioning strategies, schema evolution, data quality, governance, cost optimization, S3 lifecycle policies, data retention, compliance, query optimization with Athena, data formats (Parquet, ORC, Avro), incremental processing, CDC patterns, and production best practices for scalable data lakes.

Provides architectural guidance for data lake design including partitioning strategies, storage layout, schema design, and lakehouse patterns. Activates when users discuss data lake architecture, partitioning, or large-scale data organization.

Procedures and playbooks for responding to data quality incidents, data loss, corruption, and pipeline failures.

Create, validate, test, and manage data contracts using the Open Data Contract Specification (ODCS) and the datacontract CLI. Use when working with data contracts, ODCS specifications, data quality rules, or when the user mentions datacontract CLI or data contract workflows.

Salesforce Data Cloud integration patterns and architecture (2025)

Clean and standardize vehicle insurance CSV/Excel data. Use when handling missing values, fixing data formats, removing duplicates, or standardizing fields. Mentions "clean data", "handle nulls", "standardize", "duplicates", or "normalize".

Detect and mask PII (names, emails, phones, SSN, addresses) in text and CSV files. Multiple masking strategies with reversible tokenization option.

This skill should be used when analyzing business sales and revenue data from CSV files to identify weak areas, generate statistical insights, and provide strategic improvement recommendations. Use when the user requests a business performance report, asks to analyze sales data, wants to identify areas of weakness, or needs recommendations on business improvement strategies.

Aggregate and merge data from multiple sources including App Store sales, GitHub commits, Skillz events, and more. Use when combining data for reports, dashboards, or analysis.

实现关注点分离，确保不同职责模块化和边界清晰。当需要架构分层设计、模块职责划分、横切关注点处理时使用。覆盖层次分离、功能分离、横切关注点三大维度，提供分离检查清单和耦合问题的诊断修复方法。

Use when lINQ query and method syntax, deferred execution, and performance optimization. Use when querying collections in C#.

Use when working with Crystal language development including WebSocket communication, TLS/SSL configuration, HTTP frameworks, ORM operations, and high-performance concurrent systems.

Use when implementing concurrent programming in Crystal using fibers, channels, and parallel execution patterns for high-performance, non-blocking applications.