claudekit/website/src/content/docs/commands/optimize.md

title, description

title	description
/optimize	Analyze and optimize code performance

/optimize

Purpose

Analyze and optimize code performance. Identifies bottlenecks, suggests improvements, and implements optimizations while ensuring correctness.

Usage

/optimize [file-or-function]

Arguments

• file-or-function: Path to file or function to optimize
  • File path: src/services/data-processor.ts
  • Function name: processUserData
  • Module: src/utils/

Workflow

Step 1: Analyze Current Performance

1. Identify bottlenecks

   • Analyze time complexity
   • Check space complexity
   • Look for redundant operations

2. Profile if possible

   • Check for N+1 queries
   • Identify unnecessary loops
   • Find repeated calculations

Step 2: Identify Opportunities

1. Algorithm improvements

   • Better data structures
   • More efficient algorithms
   • Reduce complexity

2. Caching opportunities

   • Memoization
   • Result caching
   • Computed properties

3. Async optimizations

   • Parallel execution
   • Batch operations
   • Lazy loading

Step 3: Implement Optimizations

1. Make targeted changes

   • Implement improvements
   • Preserve functionality
   • Add comments

2. Verify improvements

   • Test correctness
   • Measure performance
   • Compare before/after

Output

## Optimization Report: [Component]

### Current Performance
- Time complexity: O(n²)
- Space complexity: O(n)
- Estimated execution time: 500ms for 1000 items
- Issues found: 3

### Bottlenecks Identified

1. **Nested loop in data processing**
   - Location: `processData()` lines 45-60
   - Impact: High
   - Current: O(n²)

2. **Redundant database queries**
   - Location: `getUserData()` lines 120-135
   - Impact: Medium
   - Issue: N+1 query pattern

3. **Inefficient array operations**
   - Location: `filterResults()` lines 88-92
   - Impact: Low
   - Issue: Multiple array iterations

### Optimizations Applied

#### 1. Algorithm Improvement
**Before:**
```typescript
// O(n²) nested loop
for (const item of items) {
  for (const tag of tags) {
    if (item.tags.includes(tag)) {
      results.push(item);
    }
  }
}

After:

// O(n) with Set lookup
const tagSet = new Set(tags);
const results = items.filter(item =>
  item.tags.some(tag => tagSet.has(tag))
);

Impact: Time complexity reduced from O(n²) to O(n)

2. Caching Implementation

Before:

function calculateScore(user) {
  // Recalculates every time
  return expensiveCalculation(user);
}

After:

const scoreCache = new Map();
function calculateScore(user) {
  if (scoreCache.has(user.id)) {
    return scoreCache.get(user.id);
  }
  const score = expensiveCalculation(user);
  scoreCache.set(user.id, score);
  return score;
}

Impact: 95% cache hit rate in typical usage

3. Batch Processing

Before:

for (const userId of userIds) {
  await db.getUser(userId); // N queries
}

After:

const users = await db.getUsers(userIds); // 1 query

Impact: Reduced database queries from N to 1

Performance Improvements

Metric	Before	After	Improvement
Time Complexity	O(n²)	O(n log n)	90% faster
Execution Time	500ms	50ms	10x faster
Database Queries	100	1	99% reduction
Memory Usage	150MB	80MB	47% reduction

Testing

All existing tests pass:

• ✅ Unit tests: 45/45
• ✅ Integration tests: 12/12
• ✅ Performance benchmarks added

Recommendations

1. Monitor in production: Track actual performance metrics
2. Consider further optimization: Implement pagination for large datasets
3. Add benchmarks: Create performance regression tests

Files Modified

• src/services/data-processor.ts
• tests/data-processor.test.ts (added benchmarks)

## Optimization Strategies

### 1. Algorithm Optimization
- Replace inefficient algorithms
- Use appropriate data structures
- Reduce time/space complexity

### 2. Caching
- Memoize expensive calculations
- Cache API responses
- Use computed properties

### 3. Database Optimization
- Batch queries
- Add indexes
- Optimize query patterns
- Use eager loading

### 4. Async Optimization
- Parallel execution with `Promise.all()`
- Lazy loading
- Stream processing

### 5. Code-Level Optimization
- Avoid premature optimization
- Profile before optimizing
- Focus on hot paths

## Examples

```bash
# Optimize specific file
/optimize src/services/report-generator.ts

# Optimize by function name
/optimize calculateTotalSales

# Optimize entire module
/optimize src/utils/

# Optimize with performance persona
/optimize --persona=performance src/api/users.ts

Flags

Flag	Description
--profile	Include profiling data
--benchmark	Add performance benchmarks
--aggressive	More aggressive optimizations
--safe	Conservative optimizations only
--persona=performance	Performance-focused analysis

Best Practices

1. Profile First: Don't optimize without measuring
2. Preserve Correctness: Always verify behavior unchanged
3. Add Tests: Include performance regression tests
4. Document Changes: Explain why optimizations were made
5. Measure Impact: Compare before/after metrics

Common Optimizations

N+1 Query Prevention

// Before: N+1 queries
const users = await db.getUsers();
for (const user of users) {
  user.posts = await db.getPostsByUser(user.id);
}

// After: 2 queries
const users = await db.getUsers();
const posts = await db.getPostsByUsers(users.map(u => u.id));
// Map posts to users

Memoization

const memoize = (fn) => {
  const cache = new Map();
  return (...args) => {
    const key = JSON.stringify(args);
    if (cache.has(key)) return cache.get(key);
    const result = fn(...args);
    cache.set(key, result);
    return result;
  };
};

Debouncing

const debounce = (fn, delay) => {
  let timeout;
  return (...args) => {
    clearTimeout(timeout);
    timeout = setTimeout(() => fn(...args), delay);
  };
};

Related Commands

• /review - Code review with performance checks
• /test - Add performance tests
• /fix - Fix performance issues