senior-prompt-engineer
Expert-level skill for building and optimizing production AI systems, covering RAG, agent orchestration, and model evaluation.
Install
mkdir -p .claude/skills/senior-prompt-engineer && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/889" && unzip -o skill.zip -d .claude/skills/senior-prompt-engineer && rm skill.zipInstalls to .claude/skills/senior-prompt-engineer
Activation
This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.
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.Key capabilities
- →Optimize LLM prompt performance
- →Design RAG evaluation pipelines
- →Orchestrate agentic workflows
- →Deploy scalable AI systems
- →Monitor production inference
How it works
The skill provides scripts for prompt optimization, RAG evaluation, and agent orchestration, supported by reference documentation on production patterns and system design.
Inputs & outputs
When to use senior-prompt-engineer
- →Build RAG evaluation pipelines
- →Orchestrate agentic workflows
- →Optimize LLM production performance
- →Deploy scalable AI systems
About this skill
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
When not to use it
- →Simple scripts without production requirements
- →Non-LLM based software development
Prerequisites
Limitations
- →Requires familiarity with distributed computing frameworks
- →Performance targets depend on infrastructure availability
How it compares
It provides a standardized, production-grade framework for AI system architecture rather than ad-hoc prompt testing.
Compared to similar skills
senior-prompt-engineer side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| senior-prompt-engineer (this skill) | 7 | 7mo | Review | Advanced |
| senior-ml-engineer | 6 | 7mo | Review | Advanced |
| moe-training | 3 | 7mo | Review | Advanced |
| llama-factory | 15 | 8mo | No flags | Advanced |
Try saying
Example prompts that trigger this skill in your AI assistant.
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