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.