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Found 64 Skills
Comprehensive data quality patterns using Great Expectations, DLT expectations, and custom validators for ensuring data reliability and trust.
Unity Catalog governance patterns, permissions models, security best practices, and policy enforcement for enterprise data governance.
Guides an end-to-end data-warehouse migration to Amazon Redshift — discovery, schema/SQL/stored-procedure/macro/script conversion, data migration, validation, performance comparison, and reporting. Source-routed via `references/<source>/`; Teradata (Vantage) is the supported source; additional sources are added as their own `references/<source>/` sets. Text-only knowledge (no executable code) — the AI generates all execution at runtime. Applies when a user wants to migrate Teradata to Amazon Redshift, convert Teradata DDL/SQL/stored procedures/macros/BTEQ to Redshift/RSQL, or assess Teradata-to-Redshift migration complexity. Applies only to migrations targeting Amazon Redshift; migrations to other platforms (Snowflake, BigQuery, Databricks, etc.) are out of scope regardless of source. Does not cover general Redshift administration, performance tuning, or troubleshooting of existing Redshift clusters (no migration involved), or sources not listed under references/.
Expert knowledge for Azure Data Factory development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when designing ADF pipelines, mapping data flows, SHIR/SSIS IR, SAP CDC, or CI/CD with ARM/DevOps, and other Azure Data Factory related development tasks. Not for Azure Synapse Analytics (use azure-synapse-analytics), Azure Databricks (use azure-databricks), Azure Stream Analytics (use azure-stream-analytics), Azure Data Explorer (use azure-data-explorer).
Cloud & AI FinOps advisory skill. Structured cost optimization using the FinOps Foundation framework. Covers AWS, Azure, GCP, OCI, AI inference, and data platforms (Databricks, Snowflake). Use for: cloud costs, cost optimization, cloud spend, AI costs, cloud bill, FinOps assessment, GreenOps, right-sizing, commitment strategy, tagging governance.
Expert knowledge for Azure Data Manager for Agriculture development including limits & quotas, security, configuration, and integrations & coding patterns. Use when setting up BYOL creds/Private Link, ag data ingestion/IoT, AI/nutrient APIs, throttling, or Event Grid logs, and other Azure Data Manager for Agriculture related development tasks. Not for Azure Data Explorer (use azure-data-explorer), Azure Data Factory (use azure-data-factory), Azure Synapse Analytics (use azure-synapse-analytics), Azure Databricks (use azure-databricks).
Expert knowledge for Azure Machine Learning development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when using Azure ML pipelines, AutoML, managed online/batch endpoints, prompt flow, or MLflow deployments, and other Azure Machine Learning related development tasks. Not for Azure Databricks (use azure-databricks), Azure Synapse Analytics (use azure-synapse-analytics), Azure HDInsight (use azure-hdinsight), Azure Data Science Virtual Machines (use azure-data-science-vm).
Expert knowledge for Azure Energy Data Services development including troubleshooting, decision making, architecture & design patterns, security, configuration, integrations & coding patterns, and deployment. Use when configuring ADME tiers, partitions & CORS, Reservoir DDMS, ACL/legal tags, or Geospatial CZ on AKS, and other Azure Energy Data Services related development tasks. Not for Azure Data Explorer (use azure-data-explorer), Azure Synapse Analytics (use azure-synapse-analytics), Azure Data Factory (use azure-data-factory), Azure Databricks (use azure-databricks).
Design Azure infrastructure using natural language, or analyze existing Azure resources to auto-generate architecture diagrams, refine them through conversation, and deploy with Bicep. When to use this skill: - "Create X on Azure", "Set up a RAG architecture" (new design) - "Analyze my current Azure infrastructure", "Draw a diagram for rg-xxx" (existing analysis) - "Foundry is slow", "I want to reduce costs", "Strengthen security" (natural language modification) - Azure resource deployment, Bicep template generation, IaC code generation - Microsoft Foundry, AI Search, OpenAI, Fabric, ADLS Gen2, Databricks, and all Azure services
Expert knowledge for Azure Synapse Analytics development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when building, debugging, or optimizing Azure Synapse Analytics applications. Not for Azure Data Factory (use azure-data-factory), Azure Data Explorer (use azure-data-explorer), Azure Databricks (use azure-databricks), Azure Stream Analytics (use azure-stream-analytics).
Discovers requirements and generates guidance to design and deploy a governed, secure agentic-analytics solution for data that's distributed across Google Cloud, other cloud providers, or on-premises. Data that's outside Google Cloud (such as data from Databricks, Snowflake, Salesforce, SAP, or Oracle systems) is accessed through federation mechanisms such as Apache Iceberg, other "zero-copy ETL" methods, or remote query push-down. Use this skill when designing an architecture for efficient analytics across large volumes of structured and unstructured data that's located in multiple systems and environments, including other cloud providers and on-premises.
Write correct, performant SQL across all major data warehouse dialects (Snowflake, BigQuery, Databricks, PostgreSQL, etc.). Use when writing queries, optimizing slow SQL, translating between dialects, or building complex analytical queries with CTEs, window functions, or aggregations.