foryourhealth111-pixel/senior-prompt-engineer
bundled/skills/senior-prompt-engineer/SKILL.md
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.
$ install --global
skillsauthnpx skillsauth add foryourhealth111-pixel/vco-skills-codex senior-prompt-engineerInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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SKILL.md
- 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
# 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
# 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:
-
Technical Leadership
- Drive architectural decisions
- Mentor team members
- Establish best practices
- Ensure code quality
-
Strategic Thinking
- Align with business goals
- Evaluate trade-offs
- Plan for scale
- Manage technical debt
-
Collaboration
- Work across teams
- Communicate effectively
- Build consensus
- Share knowledge
-
Innovation
- Stay current with research
- Experiment with new approaches
- Contribute to community
- Drive continuous improvement
-
Production Excellence
- Ensure high availability
- Monitor proactively
- Optimize performance
- Respond to incidents
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