Prompt Engineering Lab

About

The Prompt Engineering Lab skill transforms requirements into sophisticated prompt artifacts through a systematic optimization workflow. It leverages advanced techniques such as XML tagging, Chain of Thought reasoning, and multi-shot prompting to ensure maximum performance across multiple LLM architectures. Beyond traditional user prompts, it specializes in optimizing LLM-targeted content, including agent instructions, skill definitions, and technical documentation, providing model-specific adaptations for Claude, GLM, and Gemini to improve instruction adherence and output quality.

Key Features

• Model-specific optimization for Claude 4, GLM 4.7, and Gemini 3
• Automated refinement of LLM-targeted documentation and instructions
• Evidence-based techniques including XML tagging and Chain of Thought
• Support for few-shot example generation and prompt chaining patterns
• 0 GitHub stars
• Structured multi-phase workflow for scoping, design, and delivery

Use Cases

• Adapting existing Claude prompts for performance on GLM 4.7 or Gemini models
• Building robust system prompts for production-grade AI agents
• Optimizing technical documentation to improve LLM tool-use and retrieval accuracy