Can it help with custom AI agents?

Yes, it provides specialized guidance for designing robust system instructions and behavioral constraints necessary for creating reliable autonomous agents.

Does it help reduce costs or token usage?

Yes, it includes strategies for context management and prompt compression to ensure your instructions are both effective and token-efficient.

What advanced prompting techniques are supported?

The skill supports chain-of-thought (CoT), few-shot prompting, role-based personas, and structured XML formatting to handle complex multi-step reasoning.

How does this skill improve my prompts?

It applies advanced techniques like structural XML tagging and few-shot learning to make AI responses more predictable, accurate, and easier to parse.

Prompt Engineering Expert

Name: Prompt Engineering Expert
Author: billlzzz18

bybilllzzz18

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Data Science & ML

Transforms Claude into a specialized prompt architect for designing, optimizing, and debugging complex AI instructions and agent behaviors.

About

The Prompt Engineering Expert skill equips Claude with advanced methodologies for crafting high-performance prompts and system instructions. It streamlines the development of AI-driven workflows by applying industry best practices like chain-of-thought prompting, XML tagging, and few-shot examples. Whether you are building complex AI agents or refining simple queries, this skill provides the analytical framework needed to maximize model output quality, manage context windows efficiently, and eliminate common prompting anti-patterns for more reliable and consistent AI interactions.

Key Features

Automated prompt optimization and refinement
System instruction and agent behavior design
XML-tag based structuring for complex tasks
Chain-of-thought and few-shot prompting patterns
Context window and token usage optimization
1 GitHub stars

Use Cases

Designing reusable prompt templates for automated production workflows
Converting vague user requests into precise, structured system prompts
Debugging and fixing inconsistent model responses through iterative refinement

About

Key Features

Automated prompt optimization and refinement
System instruction and agent behavior design
XML-tag based structuring for complex tasks
Chain-of-thought and few-shot prompting patterns
Context window and token usage optimization
1 GitHub stars

Use Cases

Designing reusable prompt templates for automated production workflows
Converting vague user requests into precise, structured system prompts
Debugging and fixing inconsistent model responses through iterative refinement