/dataasset

Create structured Markdown notes documenting data assets and their metadata.

49|12|Updated Jan 8, 2026

Search Tags:#data lineage #data catalog #data governance #metadata management #data asset #data documentation

Ready to run

npx skills add https://github.com/DavidROliverBA/ArchitectKB --skill dataasset

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: /dataasset
Source: https://github.com/DavidROliverBA/ArchitectKB/tree/main/.claude/skills/dataasset
Command: npx skills add https://github.com/DavidROliverBA/ArchitectKB --skill dataasset

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

This Skill streamlines the process of documenting data assets, ensuring that critical information about data entities is captured and discoverable for AI and human collaborators.

Core Features & Use Cases

Comprehensive Data Asset Documentation: Captures essential details like asset ID, data type, domain, classification, storage location, and format.
Ownership and Governance: Records data owners, stewards, and governance-related information (e.g., GDPR applicability, PII fields).
Relationship Mapping: Tracks current, planned, and deprecating consumers, as well as data lineage (derived from and feeds into).
Use Case: When a new Kafka topic is created for customer events, use this Skill to document its schema, who owns it, which systems consume it, and its refresh frequency, making it easily understandable for data engineers and analysts.

Quick Start

Use the /dataasset skill to create a new entry for the 'Customer Orders' data asset.

Frequently Asked Questions about /dataasset

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

FAQPage Schema

How do I document data assets for AI discovery and governance?▼

To document data assets for AI discovery, you create structured Markdown notes capturing technical metadata like asset ID, data type, storage location, format, and ownership. This maintains a detailed inventory of data entities and their interdependencies, making critical information easily discoverable for both AI and human collaborators.

What metadata should I capture when documenting a new database or API?▼

When documenting a database or API, capture technical metadata including asset ID, data type, domain, classification, storage location, and format. You should also record ownership details, consumer relationships, and governance information like GDPR applicability and PII fields to ensure comprehensive data lineage tracking.

How do I track data lineage and consumer relationships for an event stream?▼

Tracking data lineage for an event stream involves recording what the asset is derived from and what it feeds into. You also map current, planned, and deprecating consumers, alongside schema details and refresh frequency, to maintain clear interdependencies between data entities.

Does this data documentation approach work for both human collaborators and AI?▼

Yes, this data documentation approach works for both AI and human collaborators. By creating structured Markdown notes with standardized technical metadata and relationship mapping, it ensures that data assets are easily understandable for data engineers, analysts, and AI-driven data discovery systems alike.

What is the best way to maintain a data catalog of entities and their interdependencies?▼

The best way to maintain a data catalog of entities and their interdependencies is to systematically document each data asset using structured Markdown. This captures essential governance details, ownership, and lineage relationships, facilitating a detailed inventory that stays updated as systems evolve.

Can I record data ownership and governance details like PII fields in my data documentation?▼

Yes, you can record data ownership and governance details like PII fields in your data documentation. The documentation process specifically accommodates recording data owners, stewards, and governance-related information, ensuring compliance and clear accountability for each data asset.