Individual skill

data-catalog-entry

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

02-documentation-knowledge/data-catalog-entry/SKILL.md

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Repository stars (not skill ratings): 518
Repository last updated: Sep 25, 2026
License: MIT

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claude-code Repository topic
codex Repository topic
data-analytics Repository topic
skills Repository topic
data Derived category

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SKILL.mdcore · 2,479 bytes
SKILL.mdcore · 2,479 bytes
SKILL.mdcore · 2,479 bytes
assets/catalog_entry_template.mddoc · 1,803 bytes
references/catalog_standards.mddoc · 3,176 bytes
scripts/catalog_extractor.pyruns · 6,208 bytes

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# 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
1. **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.
2. **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.
3. **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).
4. **Assess data quality** — calculate or estimate completeness, freshness (hours since last update), and duplicate rate. Document known issues and how they affect downstream use.