performance

# Performance Optimization Skill

**Bonded to:** `caching-performance-agent`

---

## Quick Start

```bash
# Invoke performance skill
"My API is slow, help me optimize it"
"Set up Redis caching for my application"
"Configure load balancing for high availability"
```

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## Instructions

1. **Identify Bottlenecks**: Profile application, analyze metrics
2. **Choose Strategy**: Select caching, scaling, or optimization approach
3. **Implement Cache**: Set up Redis/Memcached with appropriate pattern
4. **Configure Scaling**: Horizontal or vertical based on needs
5. **Monitor Results**: Set up APM and track improvements

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## Caching Patterns

| Pattern | Use Case | Consistency | Complexity |
|---------|----------|-------------|------------|
| Cache-Aside | Read-heavy, tolerates stale | Eventual | Low |
| Write-Through | Write-heavy, needs consistency | Strong | Medium |
| Write-Behind | High throughput writes | Eventual | High |
| Refresh-Ahead | Predictable access | Strong | Medium |

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## Decision Tree

```
Performance Issue?
    │
    ├─→ High latency → Check database queries
    │     ├─→ Slow queries → Add indexes, optimize SQL
    │     └─→ Network → Add caching, reduce round-trips
    │
    ├─→ High CPU → Profile code
    │     ├─→ Algorithmic → Optimize algorithms
    │     └─→ Too much load → Scale horizontally
    │
    └─→ Memory issues → Analyze memory usage
          ├─→ Leaks → Find and fix leaks
          └─→ Large data → Implement pagination, streaming
```

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## Examples

### Example 1: Redis Cache-Aside
```python
import redis
import json
from functools import wraps

r = redis.Redis(host='localhost', port=6379, decode_responses=True)

def cache(ttl_seconds=3600):
    def decorator(func):
        @wraps(func)
        def wrapper(*args, **kwargs):
            cache_key = f"{func.__name__}:{args}:{kwargs}"
            cached = r.get(cache_key)
            if cached:
                return json.loads(cached)

            result = func(*args, **kwar