Senior Prompt Engineer

Name: Senior Prompt Engineer
Author: davila7

davila7/claude-code-templates

903 installs
29.9k repo stars
Updated July 27, 2026
davila7/claude-code-templates

senior-prompt-engineer is a design skill that helps developers craft production-grade prompts and agentic system architectures delivering reliable, scalable LLM behavior from the first deployment.

About

senior-prompt-engineer is an advanced skill for engineers designing production agentic systems who need prompts and architectures that scale beyond prototype chatbots. The skill encodes production-first principles: plan for 10x load, target 99.9% uptime, maintain clear documented interfaces, and instrument observability from day one. It covers performance-by-design patterns—efficient algorithms, resource awareness, strategic caching, and batch processing—plus security and privacy controls including input validation, encryption, access control, and audit logging. Advanced sections address distributed processing and enterprise-scale fault tolerance for multi-step agent pipelines. Reach for senior-prompt-engineer when defining system prompts, tool policies, or multi-agent roles before coding orchestration frameworks like CrewAI or LangGraph. The skill emphasizes reliable first-run behavior for customer-facing agents rather than one-off prompt hacks, making it suitable for staff-level prompt and agent architecture reviews inside Claude Code or Cursor.

Production-First Design principles covering scalability, reliability, maintainability and observability
Performance by Design with efficient algorithms, resource awareness, strategic caching and batch processing
Security & Privacy built-in including input validation, encryption, access control and audit logging
Advanced patterns for distributed processing, real-time systems and ML at scale
Reliability practices: design for failure, retries, circuit breakers and health monitoring

Senior Prompt Engineer by the numbers

903 all-time installs (skills.sh)
+23 installs in the week ending Jul 28, 2026 (Skillselion tracking)
Ranked #1,161 of 16,659 AI & Agent Building skills by installs in the Skillselion catalog
Security screen: MEDIUM risk (skills.sh audit)
Data as of Jul 28, 2026 (Skillselion catalog sync)

npx skills add https://github.com/davila7/claude-code-templates --skill senior-prompt-engineer

Add your badge

Show developers this skill is listed on Skillselion. Paste this into your README.

[![Listed on Skillselion](https://skillselion.com/badge/skills/davila7/claude-code-templates/senior-prompt-engineer.svg)](https://skillselion.com/skills/davila7/claude-code-templates/senior-prompt-engineer)

Installs	903
repo stars	★ 29.9k
Security audit	3 / 3 scanners passed
Last updated	July 27, 2026
Repository	davila7/claude-code-templates ↗

How do you design production-grade agent prompts and systems?

Generate production-grade prompts and system designs that produce reliable, scalable agent behavior from the first run.

Who is it for?

Staff engineers and prompt architects shipping customer-facing LLM agents who need production reliability, security, and observability by design.

Skip if: One-off chat prompts, beginner prompt tuning, or projects with no production uptime or security requirements.

When should I use this skill?

The user designs agentic systems, writes production system prompts, or needs scalable multi-agent architecture with monitoring and security.

What you get

System prompt specs, agent role definitions, observability plan, security controls, and scalable orchestration architecture.

Production system prompt spec
Agent architecture document
Observability and security checklist

By the numbers

Design principles target 10x load scalability and 99.9% uptime

Files

SKILL.mdMarkdownGitHub ↗

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:

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

#!/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()

#!/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()

#!/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()

Related skills

Setup Matt Pocock SkillsScaffold the per-repo configuration that Matt Pocock’s engineering agent skills rely on so they understand the issue tracker, triage labels, and domain documentation la462k185k

Lark Skill MakerQuickly turn any Lark/Feishu OpenAPI call or multi-step workflow into a reusable agent skill with its own SKILL.md.379k15.8k

CavemanSlash token usage by roughly 75% while keeping every technical detail intact when working with Claude Code, Cursor or similar agents.378k92.5k

Lark AppsConnect Claude, Cursor or custom agents directly to Lark (Feishu) for messaging, document automation, approval workflows and enterprise data access.375k

Running Claude Code Via Litellm CopilotRun Claude Code at a fraction of the cost by routing requests through LiteLLM to the GitHub Copilot Chat API.270k72

Codex PetGenerate a complete Codex Pet spritesheet and metadata from one reference image without needing an OpenAI key or Codex Pro.246k8

How it compares

Pick this over basic prompt templates when agent systems need production SLOs, security controls, and observability—not single-shot chat instructions.

FAQ

What production targets does senior-prompt-engineer emphasize?

senior-prompt-engineer emphasizes designing agentic systems for 10x scalability, 99.9% uptime, maintainable documented interfaces, and full observability monitoring from the first deployment.

Does senior-prompt-engineer cover security for LLM agents?

Yes. senior-prompt-engineer builds in input validation, data encryption, access control, and audit logging as core agentic system design requirements alongside prompt structure.

When should teams invoke senior-prompt-engineer?

Invoke senior-prompt-engineer when architecting production agent prompts, multi-agent roles, or distributed fault-tolerant pipelines before implementing orchestration code.

Is Senior Prompt Engineer safe to install?

skills.sh reports 3 of 3 security scanners passed. Review the Security Audits panel on this page before installing in production.

AI & Agent Buildingagentsllmautomation

About

Senior Prompt Engineer by the numbers

Add your badge

How do you design production-grade agent prompts and systems?

Who is it for?

When should I use this skill?

What you get

By the numbers

Files

Senior Prompt Engineer

Quick Start

Main Capabilities

Core Expertise

Tech Stack

Reference Documentation

1. Prompt Engineering Patterns

2. Llm Evaluation Frameworks

3. Agentic System Design

Production Patterns

Pattern 1: Scalable Data Processing

Pattern 2: ML Model Deployment

Pattern 3: Real-Time Inference

Best Practices

Development

Production

Team Leadership

Performance Targets

Security & Compliance

Common Commands

Resources

Senior-Level Responsibilities

Agentic System Design

Overview

Core Principles

Production-First Design

Performance by Design

Security & Privacy

Advanced Patterns

Pattern 1: Distributed Processing

Pattern 2: Real-Time Systems

Pattern 3: ML at Scale

Best Practices

Code Quality

Performance

Reliability

Tools & Technologies

Further Reading

Llm Evaluation Frameworks

Overview

Core Principles

Production-First Design

Performance by Design

Security & Privacy

Advanced Patterns

Pattern 1: Distributed Processing

Pattern 2: Real-Time Systems

Pattern 3: ML at Scale

Best Practices

Code Quality

Performance

Reliability

Tools & Technologies

Further Reading

Prompt Engineering Patterns

Overview

Core Principles

Production-First Design

Performance by Design

Security & Privacy

Advanced Patterns

Pattern 1: Distributed Processing

Pattern 2: Real-Time Systems

Pattern 3: ML at Scale

Best Practices

Code Quality

Performance

Reliability

Tools & Technologies

Further Reading

Related skills