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