What is the Senior Prompt Engineer skill?

It is a specialized capability for Claude Code designed to help developers build, optimize, and scale production-grade AI applications using advanced prompt engineering and system architecture.

How does this help with production performance?

It provides specific P95/P99 latency targets, throughput optimization strategies, and best practices for monitoring, security, and high availability.

What tech stack does this skill cover?

It includes support for major frameworks like LangChain, LlamaIndex, and DSPy, as well as MLOps tools like MLflow, Pinecone, and Kubernetes.

Does this skill support RAG implementation?

Yes, it includes specialized patterns for optimizing Retrieval-Augmented Generation (RAG) and frameworks for evaluating retrieval and generation performance.

Can I use this for agentic systems?

Absolutely. The skill provides technical references and implementation examples for designing and orchestrating complex multi-agent systems and autonomous workflows.

Senior Prompt Engineering

Name: Senior Prompt Engineering
Author: BbgnsurfTech

byBbgnsurfTech

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Data Science & ML

Optimizes LLM performance through advanced prompt design, agentic system architecture, and rigorous evaluation frameworks.

About

This skill equips Claude with the expertise of a world-class prompt engineer to help you build production-ready AI systems. It provides deep guidance on complex prompt patterns like Chain-of-Thought and few-shot learning, while offering architectural patterns for RAG optimization and multi-agent orchestration. Beyond simple prompting, it focuses on enterprise-grade requirements including latency targets, MLOps best practices, security compliance, and scalable data processing, making it an essential tool for developers transitioning AI prototypes into robust, high-performance products.

Key Features

3 GitHub stars
Agentic system architecture and multi-agent orchestration
Production MLOps, deployment strategies, and monitoring
Advanced prompt design patterns (Few-shot, Chain-of-Thought, DSPy)
Enterprise-scale security, PII handling, and cost optimization
RAG pipeline optimization and performance evaluation

Use Cases

Architecting autonomous agent workflows for complex task automation
Refining LLM prompts for production-grade reliability and cost efficiency
Designing and deploying scalable RAG systems with built-in evaluation

About

Key Features

3 GitHub stars
Agentic system architecture and multi-agent orchestration
Production MLOps, deployment strategies, and monitoring
Advanced prompt design patterns (Few-shot, Chain-of-Thought, DSPy)
Enterprise-scale security, PII handling, and cost optimization
RAG pipeline optimization and performance evaluation

Use Cases

Architecting autonomous agent workflows for complex task automation
Refining LLM prompts for production-grade reliability and cost efficiency
Designing and deploying scalable RAG systems with built-in evaluation