axiom-core-location

# Core Location Patterns

Discipline skill for Core Location implementation decisions. Prevents common authorization mistakes, battery drain, and background location failures.

## When to Use

- Choosing authorization strategy (When In Use vs Always)
- Deciding monitoring approach (continuous vs significant-change vs CLMonitor)
- Implementing geofencing or background location
- Debugging "location not working" issues
- Reviewing location code for anti-patterns

## Related Skills

- `axiom-core-location-ref` — API reference, code examples
- `axiom-core-location-diag` — Symptom-based troubleshooting
- `axiom-energy` — Location as battery subsystem

---

## Part 1: Anti-Patterns (with Time Costs)

### Anti-Pattern 1: Premature Always Authorization

**Wrong** (30-60% denial rate):
```swift
// First launch: "Can we have Always access?"
manager.requestAlwaysAuthorization()
```

**Right** (5-10% denial rate):
```swift
// Start with When In Use
CLServiceSession(authorization: .whenInUse)

// Later, when user triggers background feature:
CLServiceSession(authorization: .always)
```

**Time cost**: 15 min to fix code, but 30-60% of users permanently denied = feature adoption destroyed.

**Why**: Users deny aggressive requests. Start minimal, upgrade when user understands value.

---

### Anti-Pattern 2: Continuous Updates for Geofencing

**Wrong** (10x battery drain):
```swift
for try await update in CLLocationUpdate.liveUpdates() {
    if isNearTarget(update.location) {
        triggerGeofence()
    }
}
```

**Right** (system-managed, low power):
```swift
let monitor = await CLMonitor("Geofences")
let condition = CLMonitor.CircularGeographicCondition(
    center: target, radius: 100
)
await monitor.add(condition, identifier: "Target")

for try await event in monitor.events {
    if event.state == .satisfied { triggerGeofence() }
}
```

**Time cost**: 5 min to refactor, saves 10x battery.

---

### Anti-Pattern 3: Ignoring Stationary Detection

**Wrong** (wasted battery):
```