Can it help with agent design?

Yes, it includes comprehensive guides and scripts for designing, deploying, and monitoring autonomous agentic systems using frameworks like LangChain or LlamaIndex.

What is the Senior Prompt Engineer skill?

It is a specialized Claude Code skill designed to help developers build, optimize, and scale production-grade AI systems using advanced prompting and architecture patterns.

How does it help with RAG?

It provides specialized tools and workflows for evaluating and optimizing Retrieval-Augmented Generation systems to ensure high-quality data retrieval and synthesis.

Who is this skill for?

It is built for AI engineers, data scientists, and senior developers who need to move beyond basic prompting into enterprise-level AI product development.

Senior Prompt Engineer & AI Architect

Name: Senior Prompt Engineer & AI Architect
Author: goodnight000

bygoodnight000

数据科学与机器学习

Implements production-grade prompt engineering patterns, RAG optimization, and agentic system architectures for advanced AI products.

关于

This skill equips Claude with the specialized knowledge of a world-class prompt engineer and AI architect to help users design, evaluate, and optimize production-grade LLM applications. It provides structured guidance on advanced patterns like Chain-of-Thought, few-shot learning, and agentic workflows, alongside technical expertise in RAG evaluation, MLOps, and scalable system design. Whether you are building complex agent orchestrators or fine-tuning prompt performance for enterprise scale, this skill ensures your AI systems are secure, cost-effective, and highly performant.

主要功能

Agentic system architecture and multi-agent orchestration
Performance tuning for system latency, throughput, and cost optimization
RAG optimization and comprehensive evaluation frameworks
MLOps best practices for LLM deployment, monitoring, and security
Advanced prompt design patterns including few-shot and chain-of-thought
0 GitHub stars

使用场景

Designing production-ready autonomous agents for complex business logic automation
Optimizing LLM prompts to improve response accuracy while reducing token consumption
Building and evaluating enterprise RAG pipelines for high-reliability data retrieval

关于

主要功能

Agentic system architecture and multi-agent orchestration
Performance tuning for system latency, throughput, and cost optimization
RAG optimization and comprehensive evaluation frameworks
MLOps best practices for LLM deployment, monitoring, and security
Advanced prompt design patterns including few-shot and chain-of-thought
0 GitHub stars

使用场景

Designing production-ready autonomous agents for complex business logic automation
Optimizing LLM prompts to improve response accuracy while reducing token consumption
Building and evaluating enterprise RAG pipelines for high-reliability data retrieval