Data Warehouses skills for Claude Code and Codex
Skills for data warehouses and lakehouses: BigQuery, Redshift, Snowflake semantic views, Microsoft Fabric lakehouses, AWS data lakes, Kusto, dbt models and Honeydew semantic layers. They help an agent query, model and tune analytical data at scale.
Highest-scoring data warehouse skills in the October 22, 2026 snapshot: dbt-sf-to-bq-translator from google/skills (89/100), bigquery from costory-io/costory-finops-mcp-skills (85/100) and model-exploration from honeydew-ai/honeydew-ai-coding-agents-plugins (80/100). Scores come from the Suede Skill Check.
- 29 listed skills
- 6 repositories
- 6 with full pages
23 from official publishers. 1 bundle scripts. 2 connect to an outside service. Listed in Data, Analytics and Databases (24), DevOps and Cloud (2), Frontend and Design (1), Writing and Editing (1), Developer Tools and Frameworks (1).
All 29 data warehouse skills
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| Skill | Repository | What it does |
|---|---|---|
| bigquery 85/100 · B | costory-io/costory-finops-mcp-skills | Investigate BigQuery compute and storage costs, compare billing models, and map spend through the hosted Costory MCP. Requires a Costory workspace, connected billing data and browser OAuth; review proposed changes before applying them. |
| dbt-sf-to-bq-translator 89/100 · B | google/skills | Translates Snowflake dbt SQL models to BigQuery SQL, handling SQL compilation, Jinja macro masking, BigQuery Translation Service workflows, config transformations, type casting, and deduplication. |
| model-exploration 80/100 · B | honeydew-ai/honeydew-ai-coding-agents-plugins | Explores the Honeydew semantic layer by discovering entities and fields, setting workspace and branch context, and running structured queries to spot-check field values. |
| query 71/100 · C | honeydew-ai/honeydew-ai-coding-agents-plugins | Queries and analyzes data through the Honeydew semantic layer, including natural language analysis questions, deep multi-step investigations, and structured queries. |
| sql-queries 78/100 · C | phuryn/pm-skills | Generates SQL queries from natural language descriptions for BigQuery, PostgreSQL, MySQL, and other dialects, reading database schemas from uploaded diagrams or documentation. |
| virtual-dimensions 81/100 · B | costory-io/costory-finops-mcp-skills | Create and preview custom cost-allocation dimensions with ordered CEL rules, leftover analysis and overlap checks. Requires the hosted Costory MCP and a connected workspace; approve rules and confirm publication. |
| bigquery-ai-ml 79/100 · C | google/skills | Leverages BigQuery's built-in machine learning and GenAI capabilities for advanced data analytics. Use when you need to write SQL queries that… |
| bigquery-basics 80/100 · B | google/skills | Manages datasets, tables, and jobs in BigQuery. Use when you need to interact with BigQuery, run SQL queries, manage BigQuery resources (datasets… |
| bigquery-bigframes 87/100 · B | google/skills | Generates Python code using BigQuery DataFrames (BigFrames). Use by default for any Python data task involving BigQuery, including data processing… |
| bigquery-observability 85/100 · B | google/skills | Provides data-retrieval best practices, tool selection guidance, and performant SQL query syntax for BigQuery telemetry across INFORMATION_SCHEMA… |
| bigquery-optimization 85/100 · B | google/skills | Provides workflows to optimize BigQuery environments (capacity planning, editions), storage assets (partitioning, clustering, storage lifecycles… |
| bigquery-pipeline-audit 67/100 · D | github/awesome-copilot | Audits Python + BigQuery pipelines for cost safety, idempotency, and production readiness. Returns a structured report with exact patch locations. |
| bigquery-troubleshooting 85/100 · B | google/skills | Provides diagnostic workflows and step-by-step root-cause analysis procedures for actively broken, failing, or slow BigQuery jobs, execution graph… |
| connecting-to-data-source 82/100 · B | aws/agent-toolkit-for-aws | Create and troubleshoot AWS Glue connections to JDBC databases (Oracle, SQL Server, PostgreSQL, MySQL, RDS), Redshift, Snowflake, and BigQuery… |
| creating-data-lake-table 82/100 · B | aws/agent-toolkit-for-aws | Create managed Iceberg tables using Amazon S3 Tables (s3tables API namespace) with automatic compaction and snapshot management. Sets up table… |
| datalineage-bigquery-asset-impact-analysis 85/100 · B | google/skills | Analyzes the downstream impact (blast radius) using data lineage on Google Cloud when a BigQuery table or view is broken, stale, or modified… |
| datalineage-summary 87/100 · B | google/skills | Summarizes data lineage graphs on Google Cloud to help users debug data quality issues and understand data provenance for BigQuery and Cloud… |
| entity-creation 69/100 · D | honeydew-ai/honeydew-ai-coding-agents-plugins | Guides you through defining a new Honeydew entity from a data warehouse source — covering source type, granularity key, and initial attribute… |
| exploring-data-catalog 84/100 · B | aws/agent-toolkit-for-aws | Full inventory and audit of AWS Glue Data Catalog assets across S3 Tables, Redshift-federated, and remote Iceberg catalogs. Triggers on: inventory… |
| fabric-lakehouse 80/100 · B | github/awesome-copilot | Use this skill to get context about Fabric Lakehouse and its features for software systems and AI-powered functions. It offers descriptions of… |
| finding-data-lake-assets 84/100 · B | aws/agent-toolkit-for-aws | Resolve data lake and lakehouse asset references across Glue Data Catalog, S3, S3 Tables, and Redshift. Triggers on: find the table, where is our… |
| google-cloud-solution-agentic-ai-borderless-data-lakehouse 85/100 · B | google/skills | Discovers requirements and designs a borderless open data lakehouse using Lakehouse for Apache Iceberg and BigQuery data agents. Use when… |
| ingesting-into-data-lake 80/100 · B | aws/agent-toolkit-for-aws | Import data into the AWS data lake from S3 files, local uploads, JDBC databases (Oracle, SQL Server, PostgreSQL, MySQL, RDS, Aurora), Amazon… |
| migrating-to-amazon-redshift 76/100 · C | aws/agent-toolkit-for-aws | Guides an end-to-end data-warehouse migration to Amazon Redshift — discovery, schema/SQL/stored-procedure/macro/script conversion, data migration… |
| querying-aws-redshift 68/100 · D | aws/agent-toolkit-for-aws | Enables Redshift system-table (SYS_*) log publishing to S3 Tables in Apache Iceberg format for both Provisioned clusters and Serverless namespaces… |
| querying-aws-s3 67/100 · D | aws/agent-toolkit-for-aws | Queries S3 object metadata, tracks bucket activity, audits object changes, searches annotations, and analyzes storage metrics using S3 Metadata… |
| querying-data-lake 84/100 · B | aws/agent-toolkit-for-aws | Execute and manage Athena SQL queries across default and federated catalogs (Glue, S3 Tables, Redshift). Triggers on phrases like: query data, run… |
| redshift-guide 76/100 · C | aws/agent-toolkit-for-aws | Amazon Redshift is NOT PostgreSQL; corrects LLM mistakes; covers SQL, metadata, connectivity/auth, storage, query performance, and operations… |
| snowflake-semanticview 77/100 · C | github/awesome-copilot | Create, alter, and validate Snowflake semantic views using Snowflake CLI (snow). Use when asked to build or troubleshoot semantic views/semantic… |
How to choose data warehouse skills
Use the vendor skill for your warehouse. Google's BigQuery set covers basics, optimization, troubleshooting and BigQuery ML; AWS covers Redshift migration and data lake queries.
Warehouse queries cost money per byte or per second. Ask the agent to estimate scan size or use a dry run before it runs large queries, and give it a role limited to the datasets it needs.
Semantic layer skills (Honeydew, Snowflake semantic views) define metrics once so every query and dashboard agrees; add them when several teams report on the same numbers.
Ranked data warehouse shortlists
Related topics
Google Cloud 146Data Engineering and Pipelines 31AWS 151SQL and Databases 116Data Analysis 27MCP Servers 82Observability and Monitoring 54
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