SCD Implementation Generator

Generates Slowly Changing Dimension implementations across platforms

509 stars

Best use case

SCD Implementation Generator is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Generates Slowly Changing Dimension implementations across platforms

Teams using SCD Implementation Generator should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/scd-implementation-generator/SKILL.md --create-dirs "https://raw.githubusercontent.com/a5c-ai/babysitter/main/library/specializations/data-engineering-analytics/skills/scd-implementation-generator/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/scd-implementation-generator/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How SCD Implementation Generator Compares

Feature / AgentSCD Implementation GeneratorStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Generates Slowly Changing Dimension implementations across platforms

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# 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

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