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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Data Science & ML

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

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.

Key Features

013 GitHub stars

02Library of 12 prompting techniques including XML structuring, CoT, and prefilling

03Systematic complexity assessment for choosing the right prompting strategy

04Cost optimization strategies including prompt caching and token budgeting

05Integration guidance for MCP tools like Perplexity and Sequential Thinking

06Multi-level diagnostic framework to identify and fix poor prompt performance

Use Cases

01Designing robust system prompts for complex AI agents and tool-calling workflows

02Optimizing existing prompts to reduce token costs while improving response accuracy

03Troubleshooting hallucinations or formatting errors in LLM-driven applications

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add dhruvbaldawa/ccconfigs engineering-prompts

For use in Claude.ai and ChatGPT

Download Skill