Babysitter OBT Design Optimizer

Designs and optimizes One Big Table (OBT) patterns

install

source · Clone the upstream repo

git clone https://github.com/a5c-ai/babysitter

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/a5c-ai/babysitter "$T" && mkdir -p ~/.claude/skills && cp -r "$T/library/specializations/data-engineering-analytics/skills/obt-design-optimizer" ~/.claude/skills/a5c-ai-babysitter-obt-design-optimizer && rm -rf "$T"

manifest: library/specializations/data-engineering-analytics/skills/obt-design-optimizer/SKILL.md

OBT Design Optimizer

Overview

Designs and optimizes One Big Table (OBT) patterns. This skill balances denormalization benefits with maintainability for analytical use cases.

Capabilities

Column selection optimization
Denormalization strategy
Nested/repeated field design (BigQuery)
Clustering key selection
Partition strategy
Update frequency optimization
Query pattern analysis
Storage vs. performance tradeoffs

Input Schema

{
  "sourceModels": ["object"],
  "queryPatterns": ["object"],
  "platform": "snowflake|bigquery|redshift",
  "constraints": {
    "maxColumns": "number",
    "refreshFrequency": "string"
  }
}

Output Schema

{
  "obtDesign": {
    "columns": ["object"],
    "clustering": ["string"],
    "partitioning": "object"
  },
  "buildStrategy": "object",
  "refreshConfig": "object",
  "estimatedQueryImprovement": "percentage"
}

Target Processes

OBT Creation
BI Dashboard Development
Query Optimization

Usage Guidelines

Analyze source models and relationships
Document common query patterns
Define platform and constraints
Balance column count with query needs

Best Practices

Include only columns needed for known query patterns
Use appropriate clustering for common filter columns
Partition by date for time-series analysis
Schedule refreshes based on source update frequency
Monitor query performance and adjust design