1---2name: data-catalog-entry3description: Create standardized metadata for data assets. Use when documenting new datasets, building data catalogs, improving data discoverability, or creating data dictionaries for teams.4---56# Data Catalog Entry78# When to use9- A new table, view, or dataset has been created and needs to be discoverable10- Analysts keep asking the same questions about a table's meaning or ownership11- A compliance or audit requirement mandates documentation of sensitive data12- Onboarding new team members who need to understand available data assets13- Auditing catalog completeness to find undocumented tables1415# Process161. **Extract technical metadata** — pull schema, column names, types, primary keys, foreign keys, and row count from `INFORMATION_SCHEMA` or the source system. Use `scripts/catalog_extractor.py` to automate this for database tables.172. **Collect business context** — interview the data owner to capture the business purpose, owning team, criticality (critical / high / medium / low), and known use cases. Record the business-friendly display name.183. **Write column descriptions** — for each column, write a one-sentence plain-language description, note example values, and document any business rules (valid values, constraints, format requirements).194. **Assess data quality** — calculate or estimate completeness, freshness (hours since last update), and duplicate rate. Document known issues and how they affect downstream use.205. **Document lineage** — record upstream sources (where the data comes from) and downstream consumers (dashboards, models, reports that depend on it).216. **Add governance details and publish** — specify access level (public/restricted/confidential), sensitivity (PII, financial, health), compliance tags, retention policy, and access instructions. Complete `assets/catalog_entry_template.md` and submit to the catalog.2223# Inputs the skill needs24- Connection or export from the database/source system for technical metadata25- Data owner contact for business context interview26- Knowledge of upstream sources and downstream consumers27- Applicable governance policies (PII classification, retention rules)28- Any existing partial documentation or data dictionary2930# Output31- `scripts/catalog_extractor.py` — extracts schema and basic stats from a database table32- `assets/catalog_entry_template.md` — completed catalog entry with technical, business, quality, lineage, and governance sections