optimize

# Optimize Skill

## Overview

The optimize skill provides comprehensive on-demand performance and optimization analysis for your codebase. It identifies bottlenecks, slow builds, large bundles, inefficient code patterns, and opportunities for performance improvements across all supported technology stacks.

**When to Use**:
- Performance issues and slow response times
- Large bundle sizes and slow page loads
- Long build and compile times
- High memory usage
- Database query optimization
- API endpoint performance tuning
- CI/CD pipeline optimization

**Technology Coverage**:
- React/TypeScript/JavaScript (Vite, Webpack, Rollup)
- Go applications (build time, runtime performance)
- Rust projects (compile time, binary size)
- Python codebases (runtime optimization)
- Full-stack applications
- Database queries (SQL, ORM)

## Optimization Categories

### 1. Build Performance

**What Gets Analyzed**:
- Build duration and bottlenecks
- Dependency resolution time
- TypeScript compilation speed
- Asset processing (images, fonts)
- Code splitting effectiveness
- Cache utilization

**Common Issues**:
- Unnecessary re-builds of unchanged code
- Large dependency trees
- Inefficient TypeScript configuration
- Missing build caching
- Redundant asset processing

**Optimization Targets**:
- Reduce build time by 30-50%
- Enable incremental builds
- Optimize dependency resolution
- Improve cache hit rates

### 2. Bundle Size

**What Gets Measured**:
- Total bundle size (uncompressed/gzipped)
- Individual chunk sizes
- Duplicate dependencies
- Tree-shaking effectiveness
- Unused code in bundles
- Third-party library sizes

**Bundle Analysis**:
```
Bundle Size Breakdown:
├── vendor.js: 847 KB (312 KB gzipped)
│   ├── react-dom: 142 KB
│   ├── lodash: 71 KB (should use lodash-es)
│   ├── moment: 67 KB (consider date-fns)
│   └── ...
├── main.js: 234 KB (89 KB gzipped)
└── [lazy chunks]: 156 KB total
```

**Optimization Goals**:
- Keep initial bundle under 200 KB (gzipped)
- Lazy load non-critical code
- Remove duplicate dependencies
- Use lighter alternatives

### 3. Runtime Performance

**What Gets Profiled**:
- Function execution time
- Component render performance
- Memory allocation patterns
- Garbage collection pressure
- Event loop blocking
- Async operation efficiency

**Performance Metrics**:
- Time to First Byte (TTFB)
- First Contentful Paint (FCP)
- Largest Contentful Paint (LCP)
- Total Blocking Time (TBT)
- Cumulative Layout Shift (CLS)

**Detection Methods**:
- Profiling data analysis
- Flame graph generation
- Hot path identification
- Memory leak detection

### 4. Memory Usage

**What Gets Monitored**:
- Heap allocation patterns
- Memory leaks
- Large object retention
- Closure memory overhead
- Cache memory usage
- Buffer allocation

**Red Flags**:
- Growing heap over time (leak)
- Excessive garbage collection
- Large retained objects
- Detached DOM nodes (React)
- Unclosed connections/subscriptions

### 5. API and Database Performance

**What Gets Analyzed**:
- Query execution time
- N+1 query problems
- Missing database indexes
- API response times
- Network round trips
- Cache effectiveness

**Database Optimization**:
- Slow query identification
- Index recommendations
- Query plan analysis
- Connection pooling efficiency

## Analysis Patterns

### Identifying Bottlenecks

**Step 1: Measure Current Performance**

Collect baseline metrics:
- Build time: `time npm run build`
- Bundle size: Analyze with webpack-bundle-analyzer
- Runtime: Browser DevTools Performance tab
- API: Response time logs

**Step 2: Profile and Identify Hot Paths**

Find where time is spent:
- CPU profiling for computation
- Heap snapshots for memory
- Network waterfall for I/O
- Flame graphs for call stacks

**Step 3: Prioritize Optimizations**

Focus on:
1. Highest impact (largest bottleneck)
2. Lowest effort (quick wins)
3. Most frequent (called often)

**Step 4: Measure Impact**

After optimization:
- Re-run benchmarks
- Compare before/after metrics
- Validate improvements

### Tools and Commands

**JavaScript/TypeScript**:
```bash
# Bundle analysis
npx webpack-bundle-analyzer dist/stats.json

# Build performance
npm run build -- --profile --json > stats.json

# Runtime profiling
node --prof app.js
node --prof-process isolate-*.log > processed.txt
```

**Go**:
```bash
# Build time analysis
go build -x 2>&1 | ts '[%Y-%m-%d %H:%M:%S]'

# CPU profiling
go test -cpuprofile=cpu.prof -bench=.
go tool pprof cpu.prof

# Memory profiling
go test -memprofile=mem.prof -bench=.
go tool pprof mem.prof
```

**Rust**:
```bash
# Compile time analysis
cargo build --timings

# Binary size analysis
cargo bloat --release

# Runtime profiling
cargo flamegraph --bench benchmark_name
```

## Optimization Report Format

### Performance Report Structure

```markdown
# Performance Optimization Report

**Generated**: 2026-01-28 14:32:00
**Scope**: Full application analysis
**Baseline**: Established 2026-01-21

## Executive Summary

**Overall Performance Score**: 67/100 (Needs Improvement)

**Key Findings**:
- Build time: 142s (Target: <60s) - 58% slower
- Bundle size: 1.2 MB gzipped (Target: <200 KB) - 6x over
- LCP: 3.8s (Target: <2.5s) - Poor
- API p95: 847ms (Target: <500ms) - Slow

**Estimated Impact of Recommendations**:
- Build time: -65s (46% improvement)
- Bundle size: -800 KB (67% reduction)
- LCP: -1.5s (39% improvement)
- API p95: -400ms (47% improvement)

## Critical Bottlenecks

### [PERF-001] Lodash Full Library Import
**Category**: Bundle Size
**Impact**: HIGH
**Effort**: LOW

**Issue**: Full lodash library imported, adding 71 KB to bundle.

**Current**:
```typescript
import _ from 'lodash';
const result = _.debounce(handler, 300);
```

**Problem**: Imports entire library for single function.

**Recommendation**: Use lodash-es with tree-shaking.

**Optimized**:
```typescript
import { debounce } from 'lodash-es';
const result = debounce(handler, 300);
```

**Savings**: -65 KB gzipped

---

### [PERF-002] Moment.js for Simpl