datahub-skills

Agent skills for working with DataHub — plan and review connectors, search the catalog, enrich metadata, trace lineage, manage data quality, and set up connections. Works with Claude Code, Cortex Code, Cursor, Codex, Copilot, Gemini CLI, Windsurf, and other Agent Skills-compatible tools.

What’s in here

Catalog interaction skills

Search

Search the DataHub catalog, discover entities, and answer ad-hoc questions about your data. Supports keyword search, filtered browse, column-name search, structured property queries, and multi-step question answering.

> Find revenue tables in Snowflake
> Who owns the customer pipeline?
> /datahub-search datasets tagged PII

Enrich

Add or update metadata in DataHub — descriptions, tags, glossary terms, ownership, and deprecation. Shows a before/after plan and asks for approval before making changes.

> Add a description to the orders table
> Tag these columns as PII
> /datahub-enrich set owner of revenue_daily to @jdoe

Lineage

Explore data lineage, trace upstream sources and downstream consumers, perform impact analysis, and map cross-platform data flows.

> What feeds into the revenue dashboard?
> Impact analysis for changing the orders table
> /datahub-lineage trace the customer pipeline

Quality

Manage data quality — create and run assertions (freshness, volume, SQL, field, schema), set up smart AI-inferred assertions, raise and resolve incidents, and configure notification subscriptions. Separates Open Source (diagnostic) from Cloud (full management) capabilities.

> Find datasets with failing assertions
> Create a freshness assertion on the orders table
> /datahub-quality raise an incident on the customer pipeline
> Subscribe me to assertion failures via Slack

Setup

Install the DataHub CLI, configure authentication, verify connectivity, and set up default scopes and profiles for the other interaction skills.

> Set up my DataHub connection
> /datahub-setup focus on Snowflake in the Finance domain
> Create a profile for the data-eng team

Evals

Runs your saved DataHub evals and reports how they did. An eval is a saved question plus the conditions its answer must meet.

Each eval is answered in a fresh agent with the DataHub tools attached, then reported back without a verdict so DataHub scores it with the same judge it uses for its own runs — which is what makes two runs comparable. The same command reports an answer produced anywhere else, so you can score another agent’s answer against the same conditions.

> Run our evals
> /datahub-evals run urn:li:eval:abc123
> /datahub-evals score this Slack answer against that eval