Obt design optimizer
Skill a5c-ai/babysitter/library/specializations/data-engineering-analytics/skills/obt-design-optimizer
Babysitter enforces obedience on agentic workforces and enables them to manage extremely complex tasks and workflows through deterministic, hallucination-free self-orchestration
npx -y skills add a5c-ai/babysitter --skill obt-design-optimizerAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
What its author says it does
Copied from the file, not written here
Designs and optimizes One Big Table (OBT) patterns
SKILL.md
2.0 KB, as published. Nobody here has run it
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