---
name: scd-implementation-generator-91d3f9741f8d
description: Generates Slowly Changing Dimension implementations across platforms
version: 1.0.0
category: Data Modeling
skillId: SK-DEA-016
allowed-tools:
  - Read
  - Write
  - Edit
  - Glob
  - Grep
  - Bash
graph:
  domains: [domain:data-engineering]
  specializations: [specialization:data-engineering-analytics]
  skillAreas: [skill-area:data-warehouse-modeling, skill-area:dbt-modeling]
  roles: [role:analytics-engineer, role:data-engineer]
  workflows: [workflow:data-pipeline-deployment]
---

# SCD Implementation Generator

## Overview

Generates Slowly Changing Dimension implementations across platforms. This skill automates the creation of SCD patterns for proper historical tracking.

## Capabilities

- SCD Type 1/2/3/4/6 implementation
- MERGE statement generation
- dbt snapshot configuration
- Historical tracking optimization
- Surrogate key management
- Effective date handling
- Current flag management
- Mini-dimension design

## Input Schema

```json
{
  "dimension": {
    "name": "string",
    "columns": ["object"],
    "businessKey": ["string"]
  },
  "scdType": "1|2|3|4|6",
  "platform": "snowflake|bigquery|redshift|dbt",
  "trackingColumns": ["string"]
}
```

## Output Schema

```json
{
  "ddl": "string",
  "mergeStatement": "string",
  "dbtConfig": "object",
  "documentation": "string"
}
```

## Target Processes

- SCD Implementation
- Dimensional Model Design
- dbt Model Development

## Usage Guidelines

1. Define dimension structure with business keys
2. Select appropriate SCD type for business requirements
3. Specify target platform for syntax generation
4. Identify columns to track for historical changes

## Best Practices

- Use SCD Type 2 for attributes requiring full history
- Implement surrogate keys for dimension tables
- Use effective dates rather than just current flags
- Consider mini-dimensions for rapidly changing attributes
- Test SCD logic with representative change scenarios


---
Source attribution: a5c-ai
Original project: https://github.com/a5c-ai/babysitter
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Dave AI.

## Upstream license notice

MIT License

Copyright (c) 2026 Gabriel Moreira

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.



# Dave AI resource metadata

Creator / publisher: a5c-ai
Original project: https://github.com/a5c-ai/babysitter
Attribution basis: Upstream repository explicitly recorded in package mirror path

Fields of work: Data & Analytics, Development & Engineering, Operations, Product Management
Roles: Business Process Analyst, Data Analyst, Data Engineer, Data Scientist, Product Manager
Specialties: API, Database
Tools: Bigquery, Dbt, Redshift, Snowflake
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: fc881d2a22ece2c4276747a1913c7f32e40146ec91914047194f71410e2af0c3