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d2f3886ae1
Repository Restructure: - Move all 83 agent .md files to agents/ subdirectory - Add 15 workflow orchestrators from commands repo to workflows/ - Add 42 development tools from commands repo to tools/ - Update README for unified repository structure The commands repository functionality is now fully integrated, providing complete workflow orchestration and development tooling alongside agents. Directory Structure: - agents/ - 83 specialized AI agents - workflows/ - 15 multi-agent orchestration commands - tools/ - 42 focused development utilities No breaking changes to agent functionality - all agents remain accessible with same names and behavior. Adds workflow and tool commands for enhanced multi-agent coordination capabilities.
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model
| model |
|---|
| claude-opus-4-1 |
Machine Learning Pipeline
Design and implement a complete ML pipeline for: $ARGUMENTS
Create a production-ready pipeline including:
-
Data Ingestion:
- Multiple data source connectors
- Schema validation with Pydantic
- Data versioning strategy
- Incremental loading capabilities
-
Feature Engineering:
- Feature transformation pipeline
- Feature store integration
- Statistical validation
- Handling missing data and outliers
-
Model Training:
- Experiment tracking (MLflow/W&B)
- Hyperparameter optimization
- Cross-validation strategy
- Model versioning
-
Model Evaluation:
- Comprehensive metrics
- A/B testing framework
- Bias detection
- Performance monitoring
-
Deployment:
- Model serving API
- Batch/stream prediction
- Model registry
- Rollback capabilities
-
Monitoring:
- Data drift detection
- Model performance tracking
- Alert system
- Retraining triggers
Include error handling, logging, and make it cloud-agnostic. Use modern tools like DVC, MLflow, or similar. Ensure reproducibility and scalability.