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.agent/skills/senior-prompt-engineer/SKILL.md

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+---
+name: senior-prompt-engineer
+description: World-class prompt engineering skill for LLM optimization, prompt patterns, structured outputs, and AI product development. Expertise in Claude, GPT-4, prompt design patterns, few-shot learning, chain-of-thought, and AI evaluation. Includes RAG optimization, agent design, and LLM system architecture. Use when building AI products, optimizing LLM performance, designing agentic systems, or implementing advanced prompting techniques.
+---
+
+# Senior Prompt Engineer
+
+World-class senior prompt engineer skill for production-grade AI/ML/Data systems.
+
+## Quick Start
+
+### Main Capabilities
+
+```bash
+# Core Tool 1
+python scripts/prompt_optimizer.py --input data/ --output results/
+
+# Core Tool 2  
+python scripts/rag_evaluator.py --target project/ --analyze
+
+# Core Tool 3
+python scripts/agent_orchestrator.py --config config.yaml --deploy
+```
+
+## Core Expertise
+
+This skill covers world-class capabilities in:
+
+- Advanced production patterns and architectures
+- Scalable system design and implementation
+- Performance optimization at scale
+- MLOps and DataOps best practices
+- Real-time processing and inference
+- Distributed computing frameworks
+- Model deployment and monitoring
+- Security and compliance
+- Cost optimization
+- Team leadership and mentoring
+
+## Tech Stack
+
+**Languages:** Python, SQL, R, Scala, Go
+**ML Frameworks:** PyTorch, TensorFlow, Scikit-learn, XGBoost
+**Data Tools:** Spark, Airflow, dbt, Kafka, Databricks
+**LLM Frameworks:** LangChain, LlamaIndex, DSPy
+**Deployment:** Docker, Kubernetes, AWS/GCP/Azure
+**Monitoring:** MLflow, Weights & Biases, Prometheus
+**Databases:** PostgreSQL, BigQuery, Snowflake, Pinecone
+
+## Reference Documentation
+
+### 1. Prompt Engineering Patterns
+
+Comprehensive guide available in `references/prompt_engineering_patterns.md` covering:
+
+- Advanced patterns and best practices
+- Production implementation strategies
+- Performance optimization techniques
+- Scalability considerations
+- Security and compliance
+- Real-world case studies
+
+### 2. Llm Evaluation Frameworks
+
+Complete workflow documentation in `references/llm_evaluation_frameworks.md` including:
+
+- Step-by-step processes
+- Architecture design patterns
+- Tool integration guides
+- Performance tuning strategies
+- Troubleshooting procedures
+
+### 3. Agentic System Design
+
+Technical reference guide in `references/agentic_system_design.md` with:
+
+- System design principles
+- Implementation examples
+- Configuration best practices
+- Deployment strategies
+- Monitoring and observability
+
+## Production Patterns
+
+### Pattern 1: Scalable Data Processing
+
+Enterprise-scale data processing with distributed computing:
+
+- Horizontal scaling architecture
+- Fault-tolerant design
+- Real-time and batch processing
+- Data quality validation
+- Performance monitoring
+
+### Pattern 2: ML Model Deployment
+
+Production ML system with high availability:
+
+- Model serving with low latency
+- A/B testing infrastructure
+- Feature store integration
+- Model monitoring and drift detection
+- Automated retraining pipelines
+
+### Pattern 3: Real-Time Inference
+
+High-throughput inference system:
+
+- Batching and caching strategies
+- Load balancing
+- Auto-scaling
+- Latency optimization
+- Cost optimization
+
+## Best Practices
+
+### Development
+
+- Test-driven development
+- Code reviews and pair programming
+- Documentation as code
+- Version control everything
+- Continuous integration
+
+### Production
+
+- Monitor everything critical
+- Automate deployments
+- Feature flags for releases
+- Canary deployments
+- Comprehensive logging
+
+### Team Leadership
+
+- Mentor junior engineers
+- Drive technical decisions
+- Establish coding standards
+- Foster learning culture
+- Cross-functional collaboration
+
+## Performance Targets
+
+**Latency:**
+- P50: < 50ms
+- P95: < 100ms
+- P99: < 200ms
+
+**Throughput:**
+- Requests/second: > 1000
+- Concurrent users: > 10,000
+
+**Availability:**
+- Uptime: 99.9%
+- Error rate: < 0.1%
+
+## Security & Compliance
+
+- Authentication & authorization
+- Data encryption (at rest & in transit)
+- PII handling and anonymization
+- GDPR/CCPA compliance
+- Regular security audits
+- Vulnerability management
+
+## Common Commands
+
+```bash
+# Development
+python -m pytest tests/ -v --cov
+python -m black src/
+python -m pylint src/
+
+# Training
+python scripts/train.py --config prod.yaml
+python scripts/evaluate.py --model best.pth
+
+# Deployment
+docker build -t service:v1 .
+kubectl apply -f k8s/
+helm upgrade service ./charts/
+
+# Monitoring
+kubectl logs -f deployment/service
+python scripts/health_check.py
+```
+
+## Resources
+
+- Advanced Patterns: `references/prompt_engineering_patterns.md`
+- Implementation Guide: `references/llm_evaluation_frameworks.md`
+- Technical Reference: `references/agentic_system_design.md`
+- Automation Scripts: `scripts/` directory
+
+## Senior-Level Responsibilities
+
+As a world-class senior professional:
+
+1. **Technical Leadership**
+   - Drive architectural decisions
+   - Mentor team members
+   - Establish best practices
+   - Ensure code quality
+
+2. **Strategic Thinking**
+   - Align with business goals
+   - Evaluate trade-offs
+   - Plan for scale
+   - Manage technical debt
+
+3. **Collaboration**
+   - Work across teams
+   - Communicate effectively
+   - Build consensus
+   - Share knowledge
+
+4. **Innovation**
+   - Stay current with research
+   - Experiment with new approaches
+   - Contribute to community
+   - Drive continuous improvement
+
+5. **Production Excellence**
+   - Ensure high availability
+   - Monitor proactively
+   - Optimize performance
+   - Respond to incidents

.agent/skills/senior-prompt-engineer/references/agentic_system_design.md

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+# Agentic System Design
+
+## Overview
+
+World-class agentic system design for senior prompt engineer.
+
+## Core Principles
+
+### Production-First Design
+
+Always design with production in mind:
+- Scalability: Handle 10x current load
+- Reliability: 99.9% uptime target
+- Maintainability: Clear, documented code
+- Observability: Monitor everything
+
+### Performance by Design
+
+Optimize from the start:
+- Efficient algorithms
+- Resource awareness
+- Strategic caching
+- Batch processing
+
+### Security & Privacy
+
+Build security in:
+- Input validation
+- Data encryption
+- Access control
+- Audit logging
+
+## Advanced Patterns
+
+### Pattern 1: Distributed Processing
+
+Enterprise-scale data processing with fault tolerance.
+
+### Pattern 2: Real-Time Systems
+
+Low-latency, high-throughput systems.
+
+### Pattern 3: ML at Scale
+
+Production ML with monitoring and automation.
+
+## Best Practices
+
+### Code Quality
+- Comprehensive testing
+- Clear documentation
+- Code reviews
+- Type hints
+
+### Performance
+- Profile before optimizing
+- Monitor continuously
+- Cache strategically
+- Batch operations
+
+### Reliability
+- Design for failure
+- Implement retries
+- Use circuit breakers
+- Monitor health
+
+## Tools & Technologies
+
+Essential tools for this domain:
+- Development frameworks
+- Testing libraries
+- Deployment platforms
+- Monitoring solutions
+
+## Further Reading
+
+- Research papers
+- Industry blogs
+- Conference talks
+- Open source projects

.agent/skills/senior-prompt-engineer/references/llm_evaluation_frameworks.md

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+# Llm Evaluation Frameworks
+
+## Overview
+
+World-class llm evaluation frameworks for senior prompt engineer.
+
+## Core Principles
+
+### Production-First Design
+
+Always design with production in mind:
+- Scalability: Handle 10x current load
+- Reliability: 99.9% uptime target
+- Maintainability: Clear, documented code
+- Observability: Monitor everything
+
+### Performance by Design
+
+Optimize from the start:
+- Efficient algorithms
+- Resource awareness
+- Strategic caching
+- Batch processing
+
+### Security & Privacy
+
+Build security in:
+- Input validation
+- Data encryption
+- Access control
+- Audit logging
+
+## Advanced Patterns
+
+### Pattern 1: Distributed Processing
+
+Enterprise-scale data processing with fault tolerance.
+
+### Pattern 2: Real-Time Systems
+
+Low-latency, high-throughput systems.
+
+### Pattern 3: ML at Scale
+
+Production ML with monitoring and automation.
+
+## Best Practices
+
+### Code Quality
+- Comprehensive testing
+- Clear documentation
+- Code reviews
+- Type hints
+
+### Performance
+- Profile before optimizing
+- Monitor continuously
+- Cache strategically
+- Batch operations
+
+### Reliability
+- Design for failure
+- Implement retries
+- Use circuit breakers
+- Monitor health
+
+## Tools & Technologies
+
+Essential tools for this domain:
+- Development frameworks
+- Testing libraries
+- Deployment platforms
+- Monitoring solutions
+
+## Further Reading
+
+- Research papers
+- Industry blogs
+- Conference talks
+- Open source projects

.agent/skills/senior-prompt-engineer/references/prompt_engineering_patterns.md

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+# Prompt Engineering Patterns
+
+## Overview
+
+World-class prompt engineering patterns for senior prompt engineer.
+
+## Core Principles
+
+### Production-First Design
+
+Always design with production in mind:
+- Scalability: Handle 10x current load
+- Reliability: 99.9% uptime target
+- Maintainability: Clear, documented code
+- Observability: Monitor everything
+
+### Performance by Design
+
+Optimize from the start:
+- Efficient algorithms
+- Resource awareness
+- Strategic caching
+- Batch processing
+
+### Security & Privacy
+
+Build security in:
+- Input validation
+- Data encryption
+- Access control
+- Audit logging
+
+## Advanced Patterns
+
+### Pattern 1: Distributed Processing
+
+Enterprise-scale data processing with fault tolerance.
+
+### Pattern 2: Real-Time Systems
+
+Low-latency, high-throughput systems.
+
+### Pattern 3: ML at Scale
+
+Production ML with monitoring and automation.
+
+## Best Practices
+
+### Code Quality
+- Comprehensive testing
+- Clear documentation
+- Code reviews
+- Type hints
+
+### Performance
+- Profile before optimizing
+- Monitor continuously
+- Cache strategically
+- Batch operations
+
+### Reliability
+- Design for failure
+- Implement retries
+- Use circuit breakers
+- Monitor health
+
+## Tools & Technologies
+
+Essential tools for this domain:
+- Development frameworks
+- Testing libraries
+- Deployment platforms
+- Monitoring solutions
+
+## Further Reading
+
+- Research papers
+- Industry blogs
+- Conference talks
+- Open source projects

.agent/skills/senior-prompt-engineer/scripts/agent_orchestrator.py

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+#!/usr/bin/env python3
+"""
+Agent Orchestrator
+Production-grade tool for senior prompt engineer
+"""
+
+import os
+import sys
+import json
+import logging
+import argparse
+from pathlib import Path
+from typing import Dict, List, Optional
+from datetime import datetime
+
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s - %(levelname)s - %(message)s'
+)
+logger = logging.getLogger(__name__)
+
+class AgentOrchestrator:
+    """Production-grade agent orchestrator"""
+    
+    def __init__(self, config: Dict):
+        self.config = config
+        self.results = {
+            'status': 'initialized',
+            'start_time': datetime.now().isoformat(),
+            'processed_items': 0
+        }
+        logger.info(f"Initialized {self.__class__.__name__}")
+    
+    def validate_config(self) -> bool:
+        """Validate configuration"""
+        logger.info("Validating configuration...")
+        # Add validation logic
+        logger.info("Configuration validated")
+        return True
+    
+    def process(self) -> Dict:
+        """Main processing logic"""
+        logger.info("Starting processing...")
+        
+        try:
+            self.validate_config()
+            
+            # Main processing
+            result = self._execute()
+            
+            self.results['status'] = 'completed'
+            self.results['end_time'] = datetime.now().isoformat()
+            
+            logger.info("Processing completed successfully")
+            return self.results
+            
+        except Exception as e:
+            self.results['status'] = 'failed'
+            self.results['error'] = str(e)
+            logger.error(f"Processing failed: {e}")
+            raise
+    
+    def _execute(self) -> Dict:
+        """Execute main logic"""
+        # Implementation here
+        return {'success': True}
+
+def main():
+    """Main entry point"""
+    parser = argparse.ArgumentParser(
+        description="Agent Orchestrator"
+    )
+    parser.add_argument('--input', '-i', required=True, help='Input path')
+    parser.add_argument('--output', '-o', required=True, help='Output path')
+    parser.add_argument('--config', '-c', help='Configuration file')
+    parser.add_argument('--verbose', '-v', action='store_true', help='Verbose output')
+    
+    args = parser.parse_args()
+    
+    if args.verbose:
+        logging.getLogger().setLevel(logging.DEBUG)
+    
+    try:
+        config = {
+            'input': args.input,
+            'output': args.output
+        }
+        
+        processor = AgentOrchestrator(config)
+        results = processor.process()
+        
+        print(json.dumps(results, indent=2))
+        sys.exit(0)
+        
+    except Exception as e:
+        logger.error(f"Fatal error: {e}")
+        sys.exit(1)
+
+if __name__ == '__main__':
+    main()

.agent/skills/senior-prompt-engineer/scripts/prompt_optimizer.py

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+#!/usr/bin/env python3
+"""
+Prompt Optimizer
+Production-grade tool for senior prompt engineer
+"""
+
+import os
+import sys
+import json
+import logging
+import argparse
+from pathlib import Path
+from typing import Dict, List, Optional
+from datetime import datetime
+
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s - %(levelname)s - %(message)s'
+)
+logger = logging.getLogger(__name__)
+
+class PromptOptimizer:
+    """Production-grade prompt optimizer"""
+    
+    def __init__(self, config: Dict):
+        self.config = config
+        self.results = {
+            'status': 'initialized',
+            'start_time': datetime.now().isoformat(),
+            'processed_items': 0
+        }
+        logger.info(f"Initialized {self.__class__.__name__}")
+    
+    def validate_config(self) -> bool:
+        """Validate configuration"""
+        logger.info("Validating configuration...")
+        # Add validation logic
+        logger.info("Configuration validated")
+        return True
+    
+    def process(self) -> Dict:
+        """Main processing logic"""
+        logger.info("Starting processing...")
+        
+        try:
+            self.validate_config()
+            
+            # Main processing
+            result = self._execute()
+            
+            self.results['status'] = 'completed'
+            self.results['end_time'] = datetime.now().isoformat()
+            
+            logger.info("Processing completed successfully")
+            return self.results
+            
+        except Exception as e:
+            self.results['status'] = 'failed'
+            self.results['error'] = str(e)
+            logger.error(f"Processing failed: {e}")
+            raise
+    
+    def _execute(self) -> Dict:
+        """Execute main logic"""
+        # Implementation here
+        return {'success': True}
+
+def main():
+    """Main entry point"""
+    parser = argparse.ArgumentParser(
+        description="Prompt Optimizer"
+    )
+    parser.add_argument('--input', '-i', required=True, help='Input path')
+    parser.add_argument('--output', '-o', required=True, help='Output path')
+    parser.add_argument('--config', '-c', help='Configuration file')
+    parser.add_argument('--verbose', '-v', action='store_true', help='Verbose output')
+    
+    args = parser.parse_args()
+    
+    if args.verbose:
+        logging.getLogger().setLevel(logging.DEBUG)
+    
+    try:
+        config = {
+            'input': args.input,
+            'output': args.output
+        }
+        
+        processor = PromptOptimizer(config)
+        results = processor.process()
+        
+        print(json.dumps(results, indent=2))
+        sys.exit(0)
+        
+    except Exception as e:
+        logger.error(f"Fatal error: {e}")
+        sys.exit(1)
+
+if __name__ == '__main__':
+    main()

.agent/skills/senior-prompt-engineer/scripts/rag_evaluator.py

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+#!/usr/bin/env python3
+"""
+Rag Evaluator
+Production-grade tool for senior prompt engineer
+"""
+
+import os
+import sys
+import json
+import logging
+import argparse
+from pathlib import Path
+from typing import Dict, List, Optional
+from datetime import datetime
+
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s - %(levelname)s - %(message)s'
+)
+logger = logging.getLogger(__name__)
+
+class RagEvaluator:
+    """Production-grade rag evaluator"""
+    
+    def __init__(self, config: Dict):
+        self.config = config
+        self.results = {
+            'status': 'initialized',
+            'start_time': datetime.now().isoformat(),
+            'processed_items': 0
+        }
+        logger.info(f"Initialized {self.__class__.__name__}")
+    
+    def validate_config(self) -> bool:
+        """Validate configuration"""
+        logger.info("Validating configuration...")
+        # Add validation logic
+        logger.info("Configuration validated")
+        return True
+    
+    def process(self) -> Dict:
+        """Main processing logic"""
+        logger.info("Starting processing...")
+        
+        try:
+            self.validate_config()
+            
+            # Main processing
+            result = self._execute()
+            
+            self.results['status'] = 'completed'
+            self.results['end_time'] = datetime.now().isoformat()
+            
+            logger.info("Processing completed successfully")
+            return self.results
+            
+        except Exception as e:
+            self.results['status'] = 'failed'
+            self.results['error'] = str(e)
+            logger.error(f"Processing failed: {e}")
+            raise
+    
+    def _execute(self) -> Dict:
+        """Execute main logic"""
+        # Implementation here
+        return {'success': True}
+
+def main():
+    """Main entry point"""
+    parser = argparse.ArgumentParser(
+        description="Rag Evaluator"
+    )
+    parser.add_argument('--input', '-i', required=True, help='Input path')
+    parser.add_argument('--output', '-o', required=True, help='Output path')
+    parser.add_argument('--config', '-c', help='Configuration file')
+    parser.add_argument('--verbose', '-v', action='store_true', help='Verbose output')
+    
+    args = parser.parse_args()
+    
+    if args.verbose:
+        logging.getLogger().setLevel(logging.DEBUG)
+    
+    try:
+        config = {
+            'input': args.input,
+            'output': args.output
+        }
+        
+        processor = RagEvaluator(config)
+        results = processor.process()
+        
+        print(json.dumps(results, indent=2))
+        sys.exit(0)
+        
+    except Exception as e:
+        logger.error(f"Fatal error: {e}")
+        sys.exit(1)
+
+if __name__ == '__main__':
+    main()