data-dictionary

Generate a data dictionary documenting entities, fields, and business rules.

19|3|Updated Feb 28, 2026

Search Tags:#data-model #data-dictionary #data-governance #business-rules #entity-relationship #data-ownership #data-definition

Ready to run

npx skills add https://github.com/qa-aman/claude-skills --skill data-dictionary-qa-aman

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: data-dictionary
Source: https://github.com/qa-aman/claude-skills/tree/main/skills/by-role/business-analyst/data-dictionary
Command: npx skills add https://github.com/qa-aman/claude-skills --skill data-dictionary-qa-aman

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Generate a data dictionary documenting entities, fields, and business rules. Use when the user says "data dictionary", "document the data model", "what fields are in this table", "data definitions", "field descriptions", "CRUD matrix", "entity relationship", "data catalogue", and similar queries.

Core Features & Use Cases

Centralizes data definitions, ownership, sources, and rules to reduce ambiguity across business units.
Documents entities, fields, relationships, data lineage, and governance rules for auditability.
Use Case: A BA wants to define and share field meanings across analytics, reporting, and ETL processes to align data understanding.

Quick Start

Create a living data dictionary that defines each field, its meaning, source, owner, and business rules.

Frequently Asked Questions about data-dictionary

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I create a data dictionary for my database schema and ETL processes?▼

To create a data dictionary, define and document each data element with its business description, data type, format, source, owner, rules, and lineage. This living dictionary centralizes definitions across database schemas, ETL processes, and reporting datasets to reduce ambiguity.

What is the best way to document data ownership and business rules for reporting datasets?▼

Documenting data ownership and business rules requires centralizing data definitions, sources, and governance rules for each field in reporting datasets. This ensures auditability and aligns data understanding across business units by clarifying field meanings and relationships.

Can I document entity relationships and data lineage across different business units?▼

Yes, you can document entity relationships and data lineage across business units by applying a centralized data dictionary to your data models and schemas. It captures entities, field relationships, and governance rules to ensure consistent data definitions and ownership.

What should be included in field descriptions for analytics and reporting datasets?▼

Field descriptions for analytics and reporting datasets should include a business description, data type, format, source, owner, rules, and lineage. This documentation clarifies data meanings and ensures consistent usage across ETL processes and business intelligence reporting.

When do I need a data dictionary for my data models?▼

You need a data dictionary for your data models when ambiguity around field meanings and data ownership arises across business units. It defines data elements, sources, and business rules to align data understanding for analytics, ETL processes, and reporting.

Does this approach support generating a CRUD matrix and data catalogue for governance?▼

Yes, this approach supports generating a data catalogue and CRUD matrix for governance by documenting entities, fields, and business rules. It establishes data ownership, definitions, and lineage to provide auditability and reduce ambiguity across the organization.