Can this skill help reduce my API costs?

Yes, it includes specific strategies for token optimization and prompt caching that can save up to 90% on repeated context and reduce unnecessary reasoning overhead.

How does the Engineering Prompts skill improve Claude's performance?

It applies a systematic methodology that includes clarity, context, and positive framing, along with advanced techniques like Chain of Thought for complex reasoning tasks.

What is the 'progressive disclosure' approach mentioned in the skill?

It involves starting with minimal, foundational prompt structures and only adding advanced techniques when the complexity of the task requires them, avoiding over-engineering.

When should I use XML structure in my prompts?

XML tags are recommended for separating sections in complex prompts or when dealing with long context to improve Claude's ability to distinguish between instructions and data.

Prompt Engineering Toolkit

Name: Prompt Engineering Toolkit
Author: dhruvbaldawa

bydhruvbaldawa

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データサイエンスとML

Systematically designs and optimizes Claude prompts using a multi-level methodology for maximum effectiveness and cost efficiency.

概要

The Engineering Prompts skill provides a comprehensive framework for developing high-quality prompts for Claude, ranging from foundational clarity to advanced techniques like Chain of Thought and prompt chaining. It guides users through a diagnostic process to fix common issues like hallucinations or generic outputs and offers a catalog of 12 specific techniques to fine-tune performance. By applying progressive disclosure and empirical validation, this skill ensures that your AI interactions are structured for peak performance, minimal latency, and significant cost savings through optimized token usage and caching strategies.

主な機能

3 GitHub stars
Library of 12 prompting techniques including XML structuring, CoT, and prefilling
Systematic complexity assessment for choosing the right prompting strategy
Cost optimization strategies including prompt caching and token budgeting
Integration guidance for MCP tools like Perplexity and Sequential Thinking
Multi-level diagnostic framework to identify and fix poor prompt performance

ユースケース

Designing robust system prompts for complex AI agents and tool-calling workflows
Optimizing existing prompts to reduce token costs while improving response accuracy
Troubleshooting hallucinations or formatting errors in LLM-driven applications

概要

主な機能

3 GitHub stars
Library of 12 prompting techniques including XML structuring, CoT, and prefilling
Systematic complexity assessment for choosing the right prompting strategy
Cost optimization strategies including prompt caching and token budgeting
Integration guidance for MCP tools like Perplexity and Sequential Thinking
Multi-level diagnostic framework to identify and fix poor prompt performance

ユースケース

Designing robust system prompts for complex AI agents and tool-calling workflows
Optimizing existing prompts to reduce token costs while improving response accuracy
Troubleshooting hallucinations or formatting errors in LLM-driven applications