---
name: bi-semantic-layer-generator
description: Generates semantic layer definitions for BI tools from dimensional models
version: 1.0.0
category: BI Tools
skillId: SK-DEA-009
allowed-tools:
  - Read
  - Write
  - Edit
  - Glob
  - Grep
  - Bash
graph:
  domains: [domain:data-engineering]
  specializations: [specialization:data-engineering-analytics]
  skillAreas: [skill-area:data-analytics, skill-area:data-warehouse-modeling]
  roles: [role:analytics-engineer, role:data-analyst]
  workflows: [workflow:data-pipeline-deployment]
---

# BI Semantic Layer Generator

## Overview

Generates semantic layer definitions for BI tools from dimensional models. This skill automates the translation of dimensional models into platform-specific semantic definitions.

## Capabilities

- LookML generation (Looker)
- Tableau data model generation
- Power BI semantic model creation
- Cube.js schema generation
- dbt metrics layer integration
- Calculation and measure definitions
- Hierarchy generation
- Security filter generation
- Join path optimization

## Input Schema

```json
{
  "dimensionalModel": "object",
  "targetPlatform": "looker|tableau|powerbi|cubejs|dbt",
  "businessGlossary": "object",
  "securityRules": ["object"]
}
```

## Output Schema

```json
{
  "semanticModel": "object",
  "calculations": ["object"],
  "hierarchies": ["object"],
  "securityFilters": ["object"],
  "documentation": "string"
}
```

## Target Processes

- Metrics Layer
- BI Dashboard Development
- Data Warehouse Setup

## Usage Guidelines

1. Provide complete dimensional model definition
2. Specify target BI platform
3. Include business glossary for consistent naming
4. Define security rules for row-level filtering

## Best Practices

- Maintain consistency between semantic layer and source models
- Use business glossary terms for user-facing labels
- Implement hierarchies for drill-down analysis
- Configure appropriate caching strategies
- Document calculation logic for maintainability


---
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 Mega AI Hub.

## 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.



# Mega AI Hub 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, Product Management
Roles: Business Intelligence Analyst, Data Analyst, Data Scientist, Growth Product Manager, Product Manager
Specialties: API
Tools: Cubejs, Dbt, Lookml, Powerbi, Tableau
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: 8db752934e353a60526d7c84fa1a80b86acb3954bc146a8184b889968d0f82d5