prompt-optimization

# Prompt Optimization

Skill for analyzing and improving existing prompts.

## Optimization Workflow

```
1. ANALYZE current prompt
   ↓
2. IDENTIFY issues
   ↓
3. APPLY corrections
   ↓
4. VALIDATE improvement
   ↓
5. DOCUMENT changes
```

## Analysis Checklist

### Clarity
- [ ] Unambiguous instructions?
- [ ] Clearly defined objective?
- [ ] Precise vocabulary?

### Structure
- [ ] Well-delimited sections?
- [ ] Logical order?
- [ ] Clear hierarchy?

### Completeness
- [ ] Output format defined?
- [ ] Error cases handled?
- [ ] Examples if needed?

### Guardrails
- [ ] Explicit limits?
- [ ] Forbidden behaviors listed?
- [ ] Appropriate security?

## Common Problems and Solutions

### 1. Vague Instructions

**Before:**
```
Write a good summary.
```

**After:**
```
Write a 100-150 word summary that:
1. Captures the main idea in the first sentence
2. Includes 2-3 supporting key points
3. Uses accessible language (high school level)
4. Avoids technical jargon
```

### 2. Lack of Context

**Before:**
```
Analyze this code.
```

**After:**
```
Analyze this Python code focusing on:
- Performance (algorithmic complexity)
- Readability (PEP 8 conventions)
- Security (OWASP vulnerabilities)

Context: Code for production REST API, 10k requests/day.
```

### 3. Undefined Format

**Before:**
```
Give me recommendations.
```

**After:**
```
Provide 3-5 recommendations in this format:

## Recommendation [N]: [Short title]
**Impact:** [High/Medium/Low]
**Effort:** [High/Medium/Low]
**Action:** [1-2 sentence description]
```

### 4. No Error Handling

**Before:**
```
Translate this text to French.
```

**After:**
```
Translate this text to French.

IF the text is already in French:
  → Indicate "The text is already in French" and suggest style improvements.

IF the text contains technical jargon:
  → Keep technical terms in English with translation in parentheses.

IF the text is too long (>1000 words):
  → Ask for confirmation before proceeding.
```

### 5. Insufficient Emphasi