≈49 tokens always: the name and description. ≈564 when used: this file. ≈1.2k more on demand in 2 files.
Data Catalog Entry
When to use
- A new table, view, or dataset has been created and needs to be discoverable
- Analysts keep asking the same questions about a table's meaning or ownership
- A compliance or audit requirement mandates documentation of sensitive data
- Onboarding new team members who need to understand available data assets
- Auditing catalog completeness to find undocumented tables
Process
- 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.
- 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.
- 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).
- Assess data quality — calculate or estimate completeness, freshness (hours since last update), and duplicate rate. Document known issues and how they affect downstream use.
- Document lineage — record upstream sources (where the data comes from) and downstream consumers (dashboards, models, reports that depend on it).
- 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.
Inputs the skill needs
- Connection or export from the database/source system for technical metadata
- Data owner contact for business context interview
- Knowledge of upstream sources and downstream consumers
- Applicable governance policies (PII classification, retention rules)
- Any existing partial documentation or data dictionary
Output
scripts/catalog_extractor.py — extracts schema and basic stats from a database table
assets/catalog_entry_template.md — completed catalog entry with technical, business, quality, lineage, and governance sections