Stream processing windowing designer
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 stream-processing-windowing-designerAssembled 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 optimal windowing strategies for stream processing
SKILL.md
2.0 KB, as published. Nobody here has run it
Stream Processing Windowing Designer
Overview
Designs optimal windowing strategies for stream processing. This skill provides expertise in window types, watermarks, and trigger strategies for streaming applications.
Capabilities
- Window type selection (tumbling, sliding, session, global)
- Watermark strategy design
- Late data handling
- Trigger configuration
- Window aggregation optimization
- State management recommendations
- Exactly-once semantics configuration
Input Schema
{
"useCase": "string",
"eventTimeField": "string",
"latencyRequirements": {
"maxLatencyMs": "number",
"allowedLateMs": "number"
},
"aggregations": ["object"]
}
Output Schema
{
"windowConfig": {
"type": "string",
"size": "string",
"slide": "string"
},
"watermarkConfig": "object",
"triggerConfig": "object",
"lateDataHandling": "object"
}
Target Processes
- Streaming Pipeline
- Feature Store Setup
Usage Guidelines
- Define use case and event time field
- Specify latency requirements
- List aggregation operations needed
- Consider late data arrival patterns
Best Practices
- Choose window type based on business requirements
- Configure watermarks based on expected lateness
- Use appropriate triggers for latency vs completeness tradeoff
- Plan state management for long windows
- Test with realistic event time distributions