AI Prompts Library

Website accessibility audit (WCAG 2.1 AA):

**Perceivable**:
- [ ] Alt text for images
- [ ] Captions for videos
- [ ] Color contrast ≥4.5:1 (text), ≥3:1 (UI)
- [ ] Don't rely on color alone
- [ ] Responsive text sizing

**Operable**:
- [ ] Keyboard navigation (no mouse required)
- [ ] Skip to main content link
- [ ] Focus indicators visible
- [ ] No keyboard traps
- [ ] Sufficient time for tasks
- [ ] Pause/stop animations

**Understandable**:
- [ ] lang attribute on html
- [ ] Consistent navigation
- [ ] Clear error messages
- [ ] Form labels & instructions
- [ ] Predictable behavior

**Robust**:
- [ ] Valid HTML
- [ ] ARIA landmarks
- [ ] Semantic HTML (header, nav, main, footer)
- [ ] ARIA labels where needed
- [ ] Screen reader tested

**Forms**:
- [ ] Label associated with input
- [ ] Required fields marked
- [ ] Error identification & suggestions
- [ ] Fieldset & legend for groups

**Interactive Elements**:
- [ ] Button vs link (semantic)
- [ ] Tab order logical
- [ ] Modal focus management
- [ ] Disabled state communicated

**Testing Tools**:
- [ ] axe DevTools
- [ ] WAVE
- [ ] Lighthouse
- [ ] Screen reader (NVDA/JAWS/VoiceOver)
- [ ] Keyboard only navigation

**Success Criteria**: All Level A & AA criteria met

You are an expert code reviewer with deep knowledge of security, performance, and best practices across multiple programming languages.

Your task is to review the following code and provide:
1. **Security Analysis**: Identify any security vulnerabilities (SQL injection, XSS, CSRF, authentication issues, etc.)
2. **Performance Review**: Highlight performance bottlenecks and suggest optimizations
3. **Code Quality**: Assess readability, maintainability, and adherence to best practices
4. **Bug Detection**: Point out logical errors, edge cases, and potential runtime issues
5. **Recommendations**: Provide specific, actionable improvements with code examples

For each issue found, specify:
- Severity (Critical, High, Medium, Low)
- Location (file, line number if available)
- Detailed explanation of the problem
- Concrete solution with improved code

Code to review:
[PASTE YOUR CODE HERE]

You are an expert Python debugger with deep knowledge of common pitfalls, edge cases, and debugging strategies.

**Error/Issue:**
[DESCRIBE THE PROBLEM OR PASTE ERROR MESSAGE]

**Code:**
```python
[PASTE YOUR PYTHON CODE HERE]
```

**Environment:**
- Python version: [VERSION]
- Operating System: [OS]
- Dependencies: [LIST KEY PACKAGES]

**Expected Behavior:**
[WHAT SHOULD HAPPEN]

**Actual Behavior:**
[WHAT ACTUALLY HAPPENS]

**Please provide:**

1. **Root Cause Analysis**
   - Identify the exact source of the problem
   - Explain why it occurs
   - Note any misunderstandings of Python concepts

2. **Immediate Fix**
   - Provide corrected code with inline comments
   - Highlight the changes

3. **Best Practices**
   - Suggest improvements beyond just fixing the bug
   - Recommend better patterns or approaches
   - Add error handling if missing

4. **Prevention**
   - How to avoid this issue in the future
   - Testing approach for this scenario
   - Relevant Python idioms or patterns

5. **Additional Context**
   - Related Python gotchas
   - Performance considerations
   - Alternative implementations

Analyze and optimize the following SQL query for maximum performance.

**Current Query:**
```sql
[PASTE YOUR SQL QUERY HERE]
```

**Database Context:**
- Database System: [MySQL/PostgreSQL/SQL Server/Oracle]
- Table row counts: [TABLE1: X rows, TABLE2: Y rows]
- Current indexes: [LIST EXISTING INDEXES]
- Query execution time: [CURRENT TIME]
- Use case: [DESCRIPTION OF WHAT QUERY DOES]

**Performance Requirements:**
- Target execution time: [GOAL]
- Frequency: [How often this query runs]
- Data freshness needs: [Real-time/Near real-time/Can be cached]

**Please provide:**

1. **Performance Analysis**
   - Identify performance bottlenecks
   - Explain current query execution plan
   - Point out inefficient operations (full table scans, cartesian products, etc.)

2. **Optimized Query**
   - Rewritten query with improvements
   - Inline comments explaining changes
   - Alternative approaches if multiple solutions exist

3. **Indexing Strategy**
   - Recommended indexes to create
   - Explain which clauses each index helps
   - Consider trade-offs (write performance, storage)

4. **Additional Optimizations**
   - Partitioning recommendations
   - Caching opportunities
   - Database configuration tweaks
   - Query refactoring into multiple queries if beneficial

5. **Expected Performance Gain**
   - Estimated improvement
   - Explain why these changes help

6. **Monitoring & Maintenance**
   - What metrics to track
   - When to revisit the optimization