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Data Catalog Entry

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data-catalog-entry

Data Catalog Entry is an Agent Skill for documents published in the nimrodfisher/data-analytics-skills repository on GitHub: a SKILL.md instruction file an AI agent can load to do this task.

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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  2. Copy and paste the prompt below.
  3. Review files and risks before approving any action.
Prompt to paste
Inspect the Agent Skill at https://github.com/nimrodfisher/data-analytics-skills/blob/HEAD/02-documentation-knowledge/data-catalog-entry/SKILL.md and all supporting files. Report provenance, permissions, scripts, dependencies, and security risks. Propose an exact installation into .agents/skills/data-catalog-entry, but wait for my approval before writing files or running scripts.

Copying this prompt does not install or run the skill. Review third-party files before use.

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