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data-catalog-entry

data-catalog-entry

github.com/nimrodfisherData & databases504226 installsUpdated 5w ago
Repository

About data-catalog-entry

A skill for documenting database tables and datasets so analysts, data owners, and governance teams can find and understand them. It combines technical metadata with business context, quality, lineage, and governance details in a catalog entry.

Key features

  1. 01Extracts schema and basic table statistics
  2. 02Captures ownership, purpose, and use cases
  3. 03Documents column meanings and business rules
  4. 04Records quality, freshness, and lineage
  5. 05Adds access, sensitivity, and retention details

Use cases

  1. 01Making a newly created dataset discoverable
  2. 02Documenting sensitive data for compliance or audit
  3. 03Helping new team members understand data assets
  4. 04Finding undocumented tables in a catalog

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/nimrodfisher/data-analytics-skills --skill data-catalog-entry

After installing, type/data-catalog-entryin Claude Code to confirm it loaded.

Bundled files

References and scripts installed along with SKILL.md.

assets/
catalog_entry_template.md
references/
catalog_standards.md
scripts/
catalog_extractor.py

Details

From the project README

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

Comments

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