OBT Design Optimizer
SkillMediaDesigns and optimizes One Big Table (OBT) patterns
Instructions available. Your AI can read the instructions. Execution depends on the setup they require.
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Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.
Then ask your AI: use the OBT Design Optimizer skill
What this skill tells your AI
The instructions your AI receives, as published by a5c-ai/babysitter in library/specializations/data-engineering-analytics/skills/obt-design-optimizer/SKILL.md and read by ahel’s review.
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
Signals
- GitHub stars
- 2k
- Forks
- 112
- Last commit
- Sep 2026
Advanced
- Item type
- skill
- Key
obt-design-optimizer- Source
- github.com/a5c-ai/babysitter
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