Data Driven Feature Builder

--- model: claude-opus-4-1 --- Build data-driven features with integrated pipelines and ML capabilities using specialized agents: [Extended thinking: This workflow orchestrates data scientists, data engineers, backend architects, and AI engineers to build features that leverage data pipelines, analytics, and machine learning. Each agent contributes their expertise to create a complete data-driven solution.] ## Phase 1: Data Analysis and Design ### 1. Data Requirements Analysis - Use Task tool with subagent_type="data-scientist" - Prompt: "Analyze data requirements for: $ARGUMENTS. Identify data sources, required transformations, analytics needs, and potential ML opportunities." - Output: Data analysis report, feature engineering requirements, ML feasibility ### 2. Data Pipeline Architecture - Use Task tool with subagent_type="data-engineer" - Prompt: "Design data pipeline architecture for: $ARGUMENTS. Include ETL/ELT processes, data storage, streaming requirements, and integration with existing systems based on data scientist's analysis." - Output: Pipeline architecture, technology stack, data flow diagrams ## Phase 2: Backend Integration ### 3. API and Service Design - Use Task tool with subagent_type="backend-architect" - Prompt: "Design backend services to support data-driven feature: $ARGUMENTS. Include APIs for data ingestion, analytics endpoints, and ML model serving based on pipeline architecture." - Output: Service architecture, API contracts, integration patterns ### 4. Database and Storage Design - Use Task tool with subagent_type="database-optimizer" - Prompt: "Design optimal database schema and storage strategy for: $ARGUMENTS. Consider both transactional and analytical workloads, time-series data, and ML feature stores." - Output: Database schemas, indexing strategies, storage recommendations ## Phase 3: ML and AI Implementation ### 5. ML Pipeline Development - Use Task tool with subagent_type="ml-engineer" - Prompt: "Implement ML pipeline for: $ARGUMENTS. Include feature engineering, model training, validation, and deployment based on data scientist's requirements." - Output: ML pipeline code, model artifacts, deployment strategy ### 6. AI Integration - Use Task tool with subagent_type="ai-engineer" - Prompt: "Build AI-powered features for: $ARGUMENTS. Integrate LLMs, implement RAG if needed, and create intelligent automation based on ML engineer's models." - Output: AI integration code, prompt engineering, RAG implementation ## Phase 4: Implementation and Optimization ### 7. Data Pipeline Implementation - Use Task tool with subagent_type="data-engineer" - Prompt: "Implement production data pipelines for: $ARGUMENTS. Include real-time streaming, batch processing, and data quality monitoring based on all previous designs." - Output: Pipeline implementation, monitoring setup, data quality checks ### 8. Performance Optimization - Use Task tool with subagent_type="performance-engineer" - Prompt: "Optimize data processing and model serving performance for: $ARGUMENTS. Focus on query optimization, caching strategies, and model inference speed." - Output: Performance improvements, caching layers, optimization report ## Phase 5: Testing and Deployment ### 9. Comprehensive Testing - Use Task tool with subagent_type="test-automator" - Prompt: "Create test suites for data pipelines and ML components: $ARGUMENTS. Include data validation tests, model performance tests, and integration tests." - Output: Test suites, data quality tests, ML monitoring tests ### 10. Production Deployment - Use Task tool with subagent_type="deployment-engineer" - Prompt: "Deploy data-driven feature to production: $ARGUMENTS. Include pipeline orchestration, model deployment, monitoring, and rollback strategies." - Output: Deployment configurations, monitoring dashboards, operational runbooks ## Coordination Notes - Data flow and requirements cascade from data scientists to engineers - ML models must integrate seamlessly with backend services - Performance considerations apply to both data processing and model serving - Maintain data lineage and versioning throughout the pipeline Data-driven feature to build: $ARGUMENTS