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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.
48 lines
1.1 KiB
Markdown
48 lines
1.1 KiB
Markdown
---
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model: claude-opus-4-1
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---
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# Machine Learning Pipeline
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Design and implement a complete ML pipeline for: $ARGUMENTS
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Create a production-ready pipeline including:
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1. **Data Ingestion**:
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- Multiple data source connectors
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- Schema validation with Pydantic
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- Data versioning strategy
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- Incremental loading capabilities
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2. **Feature Engineering**:
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- Feature transformation pipeline
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- Feature store integration
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- Statistical validation
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- Handling missing data and outliers
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3. **Model Training**:
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- Experiment tracking (MLflow/W&B)
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- Hyperparameter optimization
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- Cross-validation strategy
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- Model versioning
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4. **Model Evaluation**:
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- Comprehensive metrics
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- A/B testing framework
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- Bias detection
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- Performance monitoring
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5. **Deployment**:
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- Model serving API
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- Batch/stream prediction
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- Model registry
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- Rollback capabilities
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6. **Monitoring**:
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- Data drift detection
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- Model performance tracking
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- Alert system
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- Retraining triggers
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Include error handling, logging, and make it cloud-agnostic. Use modern tools like DVC, MLflow, or similar. Ensure reproducibility and scalability.
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