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data-catalog-entry

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

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How to use this skill

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

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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.
  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.

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