data-catalog-entry

name	data-catalog-entry
description	Create standardized metadata for data assets. Use when documenting new datasets, building data catalogs, improving data discoverability, or creating data dictionaries for teams.

When to use

• A new table, view, or dataset needs to be discoverable by other teams
• Analysts keep asking the same questions about what a table means or who owns it
• Compliance or audit requirements mandate documentation of sensitive data
• Onboarding a new team member who needs to understand available data assets
• Auditing the data catalog for undocumented tables

Process

1. Extract technical metadata — use scripts/catalog_extractor.py to pull schema, column types, row counts, and sample values from the database
2. Collect business context — interview the data owner to document: business purpose, update frequency, upstream sources, downstream consumers, and known limitations
3. Write column descriptions — for each column, write a plain-language description including what it represents, valid values, and any business rules
4. Assess data quality — document known quality issues, null rates for key columns, and any data quality SLAs
5. Document lineage — identify upstream sources and downstream consumers; note any transformations applied
6. Publish governance details — document data owner, steward, sensitivity classification, and access policy using assets/catalog_entry_template.md

Inputs the skill needs

• Required: database connection or export for technical metadata
• Required: data owner contact
• Optional: knowledge of upstream sources and downstream consumers
• Optional: applicable governance or compliance policies
• Optional: existing partial documentation to build on

Output

• scripts/catalog_extractor.py — automated schema extraction
• assets/catalog_entry_template.md (filled) — complete catalog entry with technical metadata, business context, column descriptions, lineage, and governance details