How does this skill help reduce AI costs?

It includes a Prompt Optimizer that identifies redundant context and analyzes token usage, providing specific recommendations to shorten prompts while maintaining high performance.

Can I use this to test my RAG system's accuracy?

Yes, the built-in RAG Evaluator measures metrics like context relevance, answer faithfulness, and retrieval precision to identify specific failure points in your retrieval pipeline.

Does it support complex agent architectures?

It supports advanced patterns like ReAct and Plan-Execute, providing an Agent Orchestrator to validate tool configurations, estimate costs, and visualize execution flows.

How does it handle structured data requirements?

It provides a dedicated structured output design workflow that includes schema definition, format enforcement techniques, and validation tools to ensure reliable JSON or XML responses.

Senior Prompt Engineer

Name: Senior Prompt Engineer
Author: zhangzhang-111-i

byzhangzhang-111-i

0•

数据科学与机器学习

Optimizes LLM performance through advanced prompt engineering patterns, RAG evaluation frameworks, and agentic system design.

This skill empowers developers to architect sophisticated AI systems by providing automated tools for prompt optimization, token efficiency analysis, and RAG quality assessment. It bridges the gap between basic prompting and production-grade AI workflows, offering structured methodologies for agent orchestration, few-shot example design, and reliable structured output generation. Whether you are reducing latency, cutting token costs, or building complex multi-step agents, this skill provides the specialized guidance needed to ensure consistent and cost-effective LLM behavior.

主要功能

01Automated prompt analysis for token efficiency and instruction clarity

02RAG evaluation metrics for measuring context relevance and answer faithfulness

03Comprehensive library of advanced patterns including Chain-of-Thought and ReAct

04Agentic workflow visualization and tool configuration validation

050 GitHub stars

06Structured output design patterns for reliable JSON and XML responses

使用场景

01Reducing API costs and latency by optimizing prompt token counts

02Architecting and debugging complex multi-agent systems and tool-calling workflows

03Improving the accuracy and grounding of Retrieval-Augmented Generation systems

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add zhangzhang-111-i/claude-skills111 senior-prompt-engineer

For use in Claude.ai and ChatGPT

主要功能

01Automated prompt analysis for token efficiency and instruction clarity

02RAG evaluation metrics for measuring context relevance and answer faithfulness

03Comprehensive library of advanced patterns including Chain-of-Thought and ReAct

04Agentic workflow visualization and tool configuration validation

050 GitHub stars

06Structured output design patterns for reliable JSON and XML responses

使用场景

01Reducing API costs and latency by optimizing prompt token counts

02Architecting and debugging complex multi-agent systems and tool-calling workflows

03Improving the accuracy and grounding of Retrieval-Augmented Generation systems

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add zhangzhang-111-i/claude-skills111 senior-prompt-engineer

For use in Claude.ai and ChatGPT