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approve-dms

Approve Data Model Specification documents by updating status and logging version history.

5|1|Updated Sep 23, 2025
Search Tags:#data modeling#data governance#version history#dms approval#specification sign-off#downstream artifacts
One-click install
npx skills add https://github.com/RDEWAI/Redefining-DataEngineering-With-AI --skill approve-dms
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: approve-dms
Source: https://github.com/RDEWAI/Redefining-DataEngineering-With-AI/tree/main/chapter-6/data-modeler-plugin/skills/approve-dms
Command: npx skills add https://github.com/RDEWAI/Redefining-DataEngineering-With-AI --skill approve-dms

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Manually tracking Data Model Specification (DMS) approval status and version history is error-prone and creates bottlenecks in data development workflows. This Skill automates the formal approval process to deliver consistent, auditable sign-off that unblocks teams to build downstream artifacts.

Core Features & Use Cases

  • Status Validation: Pre-checks for existing approval status and draft status warnings to prevent invalid state changes.
  • Automated Metadata Updates: Automatically updates the DMS status to Approved, refreshes the last modified date, and adds a version history entry.
  • Audit Logging: Creates a dated session note to track approval actions for governance and traceability.
  • Use Case: Data modelers can use this Skill to formally sign off on reviewed DMS documents, enabling data engineers and analysts to immediately start building STM, DQS, LLD and user stories from the approved specification.

Quick Start

Use the approve-dms skill to formally sign off on the latest Data Model Specification document to unblock downstream artifact creation.

Frequently Asked Questions about approve-dms

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

FAQPage Schema
How do I formally sign off on a Data Model Specification to unblock downstream work?▼

Data Model Specification sign-off is achieved by updating the document status to Approved and logging a version history entry, which unblocks creation of downstream artifacts like STM, DQS, LLD, and user stories.

What happens if I try to approve a DMS that is still in draft status?▼

Draft status warnings are enforced during DMS approval to prevent invalid state changes, ensuring the specification undergoes proper review governance before transitioning to an Approved state.

How does automated DMS approval handle version history and audit logging?▼

Automated DMS approval handles version history by refreshing the last modified date, adding a version history entry, and creating a dated session note to track the sign-off action for governance traceability.

Can I re-approve a Data Model Specification that already has an Approved status?▼

Pre-checks for existing approval status prevent re-approving an already Approved Data Model Specification, enforcing valid state transitions and maintaining proper data modeling review governance.

When do I need formal DMS approval in a data modeling workflow?▼

Formal DMS approval is needed in data modeling workflows when reviewed specifications require auditable sign-off to unblock downstream data development teams from building STM, DQS, LLD, and user stories.

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