prompt-optimizer

What this skill does

# 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.