prompt-optimizer

Prompt Optimizer

Overview

This skill transforms user-provided prompts into high-quality, clear, and effective instructions optimized for AI models. Apply proven prompt engineering principles to enhance clarity, specificity, structure, and effectiveness. The skill uses a systematic workflow to analyze, identify improvement opportunities, and restructure prompts based on industry best practices.

When to Use This Skill

Activate this skill when users:

• Explicitly request prompt optimization or improvement
• Provide vague or unclear instructions that need refinement
• Ask for help making their requests more effective
• Submit poorly structured prompts that would benefit from reorganization
• Request guidance on how to better communicate with AI models
• Present complex tasks that need to be broken down into clearer instructions

Optimization Workflow

Follow this systematic process to optimize any prompt:

Step 1: Analyze the Original Prompt

Examine the user's prompt and identify:

Clarity issues:

• Ambiguous terms or vague requirements
• Implicit assumptions that should be explicit
• Missing context or background information

Specificity gaps:

• Lack of concrete constraints or requirements
• Undefined success criteria
• Missing audience or purpose information
• Unclear scope or boundaries

Structure problems:

• Disorganized or stream-of-consciousness format
• Missing logical flow
• Lack of clear sections or hierarchy

Format considerations:

• No specified output format
• Unclear expectations about length, tone, or style
• Missing examples or templates

Complexity assessment:

• Determine if the task is too complex for a single prompt
• Identify if the request would benefit from prompt chaining
• Assess if step-by-step reasoning is needed

Step 2: Identify the Core Intent

Determine the fundamental objective behind the user's request:

• What is the user ultimately trying to accomplish?
• What problem are they trying to solve?
• What would constitute a successful output?
• Who is the intended audience or consumer of the output?

Clarify these points with the user if they are not evident from the original prompt.

Step 3: Apply Optimization Principles

Enhance the prompt using these core principles:

Make it clear and direct:

• State requirements explicitly without assuming inference
• Remove ambiguity and vague language
• Use concrete, specific terms

Provide context and motivation:

• Explain WHY certain requirements matter
• Include relevant background information
• Describe the use case or scenario

Add specificity:

• Define concrete constraints (length, format, scope)
• Specify target audience
• Include quality criteria
• State any limitations or boundaries

Structure the request:

• Organize information logically
• Use clear sections or numbered points
• Separate different types of information (context, requirements, format)

Include examples when helpful:

• Provide input-output examples for complex formats
• Show desired tone or style through examples
• Demonstrate edge case handling

Allow for uncertainty:

• Explicitly permit expressing "I don't know"
• Request acknowledgment of limitations
• Prevent hallucination by encouraging honesty

Step 4: Consider Advanced Techniques

Evaluate if any advanced techniques would enhance the prompt:

Chain of Thought:

• Apply when the task requires reasoning or analysis
• Request step-by-step thinking for complex problems
• Use structured format to separate reasoning from answer

Prefilling:

• Use when a specific format is absolutely required (JSON, XML)
• Apply to eliminate unwanted preambles
• Utilize to establish immediate tone or style

Prompt Chaining:

• Break complex tasks into sequential steps
• Create a multi-stage workflow for intricate projects
• Design each prompt to build on previous outputs

Structured Output:

• Specify exact format requirements
• Provide schemas or templates
• Use tags or delimiters for different sections

Consult references/prompt-best-practices.md for detailed guidance on these techniques.

Step 5: Present the Optimized Prompt

Deliver the optimization in this format:

Analysis Section:

Original prompt issues identified:
- [List key problems with the original prompt]

Optimized Prompt:

[Present the complete optimized prompt in a code block for easy copying]

Improvement Explanation:

Key improvements made:
- [Explain major enhancements]
- [Highlight added specificity]
- [Note structural changes]
- [Mention any advanced techniques applied]

Optional - Usage Tips:

[If applicable, provide brief tips on how to further customize or use the optimized prompt]

Step 6: Iterate Based on Feedback

After presenting the optimized prompt:

• Ask if the optimization meets the user's needs
• Offer to adjust tone, length, or specificity
• Provide alternative formulations if requested
• Refine based on user feedback

Practical Guidelines

Balance is key: Not every prompt needs all advanced techniques. Match the optimization level to the task complexity.

Preserve user intent: Enhance clarity without changing the fundamental goal or adding unwanted requirements.

Consider the model: Modern models like Claude 4.x have strong instruction-following capabilities; leverage this by being direct and specific.

Stay practical: Focus on improvements that materially impact output quality, not cosmetic changes.

Be educational: When appropriate, briefly explain why certain changes improve the prompt, helping users learn to write better prompts independently.

Reference Resources

This skill includes comprehensive reference materials:

references/prompt-best-practices.md

• Detailed explanations of all core principles
• Advanced techniques with examples
• Troubleshooting guide for common issues
• Quality checklist and decision frameworks

Load this reference when:

• Users ask about specific prompt engineering concepts
• Deep explanation of a technique is needed
• Troubleshooting unusual or complex prompting challenges
• Users want to learn prompt engineering principles

references/examples.md

• Before-and-after optimization examples across multiple domains
• Real-world scenarios demonstrating transformation
• Pattern library showing common improvements

Load this reference when:

• Users want to see concrete examples
• Illustrating a specific type of optimization
• Users are learning and need to understand patterns
• Demonstrating the impact of optimization

Quality Standards

Ensure every optimized prompt includes:

• Clear, unambiguous objective
• Sufficient context for the AI to understand the goal
• Specific constraints and requirements
• Target audience or use case (when relevant)
• Expected output format or structure
• Quality criteria or success definition
• Permission to express uncertainty (when appropriate)

Common Optimization Patterns

Pattern 1: Vague Request → Specific Structured Task

• Original: "Write about marketing"
• Optimized: Adds audience, scope, length, structure, key points, tone

Pattern 2: Implicit Context → Explicit Context

• Original: Assumes AI knows the background
• Optimized: States context, explains why it matters, provides relevant details

Pattern 3: Single Complex Prompt → Prompt Chain

• Original: Tries to do everything in one request
• Optimized: Breaks into logical sequential steps with clear outputs

Pattern 4: Generic Output → Formatted Output

• Original: No format specification
• Optimized: Provides schema, template, or explicit structure

Pattern 5: Assumed Constraints → Stated Constraints

• Original: Expects AI to infer limits
• Optimized: Explicitly states length, tone, scope, what to include/exclude

Consult references/examples.md for detailed examples of each pattern.