AI Prompting & Instruction Design

About

This skill provides a standardized framework for crafting high-fidelity prompts and system instructions, moving beyond simple wording to structured context engineering. It leverages 2025 industry standards for both reasoning and standard models, offering automated decision flows for code generation, complex reasoning, and multi-step workflows. By implementing XML-based structures and advanced techniques like Tree of Thoughts and ReAct, it ensures that Claude and other LLMs produce reliable, production-grade outputs while maximizing context window efficiency and caching.

Key Features

• Standardized XML/Markdown templates for context, tasks, and constraints
• Context stack prioritization to optimize for model recency and prompt caching
• Advanced technique library including Extended Thinking and Orchestrator-Worker patterns
• Model-agnostic best practices optimized for Claude, GPT, and reasoning models
• Automated decision flows for selecting zero-shot vs. chain-of-thought logic
• 2 GitHub stars

Use Cases

• Optimizing complex multi-step developer workflows into atomic, verifiable tasks
• Refining long-context prompts to improve accuracy and reduce hallucination in large codebases
• Designing robust system prompts for specialized AI coding agents and autonomous tools