# Data Catalog Entry

Create standardized metadata for data assets.

- Repository: https://github.com/nimrodfisher/data-analytics-skills (512 stars)
- Category: Engineering
- License: MIT
- Last updated: 2026-09-25
- Runs scripts: yes, review before use

## Install

Claude Code: `npx skills add nimrodfisher/data-analytics-skills --skill data-catalog-entry -g -a claude-code -y`
Codex: `npx skills add nimrodfisher/data-analytics-skills --skill data-catalog-entry -g -a codex -y`
Cursor: `npx skills add nimrodfisher/data-analytics-skills --skill data-catalog-entry -g -a cursor -y`
Gemini CLI: `npx skills add nimrodfisher/data-analytics-skills --skill data-catalog-entry -g -a gemini-cli -y`

## Description

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

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# 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.
5. **Document lineage**, record upstream sources (where the data comes from) and downstream consumers (dashboards, models, reports that depend on it).
6. **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.

The rest: https://github.com/nimrodfisher/data-analytics-skills/blob/HEAD/02-documentation-knowledge/data-catalog-entry/SKILL.md