Prompt Engineering Expert

Expert prompt engineering assistant for Claude Code. Automatically optimizes user prompts using advanced techniques like role assignment, chain-of-thought, few-shot examples, and structured formatting.

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Features

• CLARITY Framework: Systematic approach covering Context, Logic, Action, Role, Input/Output, Tone, and Yardstick
• Advanced Techniques: Chain-of-thought, few-shot examples, structured output, constraint specification
• Pattern Library: Reusable patterns for common scenarios
• Multi-Domain Support: Code generation, content writing, data analysis, and more
• Educational: Explains why each optimization was made

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Installation

# Clone repository locally
git clone https://github.com/dreamor/prompt-optimizer-skill.git

# Install in Claude Code
cd ~/.claude/skills/
ln -s /path/to/prompt-optimizer-skill prompt-engineering-expert

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Usage

In Claude Code conversation:

• Say "optimize this prompt: [your prompt]"
• Or "improve this: [your prompt]"
• Or use /prompt-engineering-expert command

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The CLARITY Framework

Element	Description
Context	Provide relevant background and situation
Logic	Define the reasoning approach
Action	Specify the exact task or action
Role	Assign a specific expert role
Input/Output	Define input format and expected output
Tone	Specify writing style, tone, and voice
Yardstick	Set constraints, requirements, and quality criteria

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Example

Before

write a function to sort a list

After

# Role
You are a senior Python developer specializing in algorithms and data structures.

# Task
Implement a robust sorting function with comprehensive error handling.

# Instructions
1. Implement with type hints following Python 3.10+ standards
2. Include detailed docstrings with examples in Google style
3. Handle edge cases: empty list, None values, mixed types
4. Provide both ascending and descending sort options
5. Include time and space complexity analysis

# Constraints
- Use only Python standard library
- Follow PEP 8 style guidelines
- Include at least 5 test cases
- Keep function under 50 lines (excluding tests)

# Output Format
1. Function implementation with type hints
2. Comprehensive docstring with examples
3. Complexity analysis table
4. Test suite with pytest-style assertions
5. Usage examples

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Prompt Patterns

Chain-of-Thought

Think step by step and show your reasoning before providing the final answer.

Few-Shot Examples

Example 1:
Input: ...
Output: ...

Now process: [user input]

Critique-Refine

After generating your answer, review it for gaps and improve before finalizing.

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License

MIT