This skill should be used when the user asks to "optimize queries", "fix slow queries", "improve performance", "detect N+1", "add indexes", or when writing Django ORM queries that access related objects. Detects and fixes performance issues.
Scanned 5/28/2026
Install via CLI
Skills Directoryopenskills install smicolon/ai-kit---
name: performance-optimizer
description: This skill should be used when the user asks to "optimize queries", "fix slow queries", "improve performance", "detect N+1", "add indexes", or when writing Django ORM queries that access related objects. Detects and fixes performance issues.
---
# Performance Optimizer
Auto-detects and fixes Django ORM performance issues before they reach production.
## Activation Triggers
This skill activates when:
- Writing ORM queries
- Accessing related objects (foreign keys, M2M)
- Creating views that fetch data
- Mentioning "slow", "performance", "optimization"
- Looping over querysets
- Creating list/detail views
## Performance Targets
- ✅ NO N+1 queries
- ✅ Appropriate indexes on frequently queried fields
- ✅ Pagination for list endpoints
- ✅ `select_related()` for foreign keys
- ✅ `prefetch_related()` for reverse foreign keys and M2M
- ✅ Caching for expensive operations
## Auto-Detection Patterns
### Pattern 1: N+1 Query Detection
**❌ INEFFICIENT (N+1 query):**
```python
# Bad: 1 query for users + N queries for organizations
users = User.objects.all() # 1 query
for user in users: # N queries
print(user.organization.name) # Hits DB each time!
```
**✅ OPTIMIZED:**
```python
# Good: 2 queries total (1 for users + 1 JOIN for organizations)
users = User.objects.select_related('organization').all()
for user in users:
print(user.organization.name) # No DB hit!
```
### Pattern 2: Reverse Foreign Key N+1
**❌ INEFFICIENT:**
```python
# Bad: 1 + N queries
organizations = Organization.objects.all() # 1 query
for org in organizations: # N queries
users = org.users.all() # Hits DB each time!
print(f"{org.name}: {users.count()} users")
```
**✅ OPTIMIZED:**
```python
# Good: 2 queries total
organizations = Organization.objects.prefetch_related('users').all()
for org in organizations:
users = org.users.all() # Pre-fetched!
print(f"{org.name}: {users.count()} users")
```
### Pattern 3: Many-to-Many N+1
**❌ INEFFICIENT:**
```python
# Bad: 1 + N queries
users = User.objects.all() # 1 query
for user in users: # N queries
roles = user.roles.all() # Hits DB each time!
```
**✅ OPTIMIZED:**
```python
# Good: 2 queries total
users = User.objects.prefetch_related('roles').all()
for user in users:
roles = user.roles.all() # Pre-fetched!
```
### Pattern 4: Missing Indexes
**❌ SLOW:**
```python
# Frequent query without index
User.objects.filter(email='test@example.com') # Table scan if no index!
```
**✅ FAST:**
```python
# Model with index
class User(models.Model):
email = models.EmailField(unique=True) # Unique creates index
# OR
class Meta:
indexes = [
models.Index(fields=['email']), # Explicit index
]
```
### Pattern 5: Missing Pagination
**❌ DANGEROUS:**
```python
# Returns ALL records - memory issues with large tables
class UserViewSet(viewsets.ModelViewSet):
queryset = User.objects.all() # No pagination!
```
**✅ SAFE:**
```python
# Paginated automatically
class UserViewSet(viewsets.ModelViewSet):
queryset = User.objects.all()
pagination_class = PageNumberPagination # DRF auto-paginates
```
## Auto-Fix Process
### Step 1: Detect Anti-Pattern
When detecting:
```python
def get_users_with_organizations(self):
users = User.objects.all()
return [
{'user': u.email, 'org': u.organization.name}
for u in users
]
```
### Step 2: Analyze Query Pattern
Identify:
- Queryset: `User.objects.all()` (1 query)
- Foreign key access: `u.organization.name` (N queries)
- **Total:** 1 + N queries = N+1 problem!
### Step 3: Suggest Optimization
Provide:
> **N+1 Query Detected!**
>
> **Problem:**
> - Query: `User.objects.all()` → 1 query
> - Loop accesses `user.organization.name` → N queries
> - **Total:** 1 + N queries (inefficient!)
>
> **Impact:**
> - 100 users = 101 queries 😱
> - 1000 users = 1001 queries 🔥
>
> **Fix:**
> ```python
> users = User.objects.select_related('organization').all()
> # Now only 2 queries total!
> ```
>
> **Why:**
> - `select_related()` performs SQL JOIN
> - Fetches related data in single query
> - 500x faster for 1000 records!
### Step 4: Auto-Apply Fix
```python
# Optimized version
def get_users_with_organizations(self):
users = User.objects.select_related('organization').all()
return [
{'user': u.email, 'org': u.organization.name} # No DB hit!
for u in users
]
```
## Common Optimization Patterns
### Foreign Key (One-to-One, Many-to-One)
```python
# Use select_related() for foreign keys
User.objects.select_related('organization', 'created_by')
# Multiple levels
User.objects.select_related('organization__country')
```
### Reverse Foreign Key (One-to-Many)
```python
# Use prefetch_related() for reverse FK
Organization.objects.prefetch_related('users')
# With filtering
from django.db.models import Prefetch
Organization.objects.prefetch_related(
Prefetch('users', queryset=User.objects.filter(is_active=True))
)
```
### Many-to-Many
```python
# Use prefetch_related() for M2M
User.objects.prefetch_related('roles', 'permissions')
```
### Combined Optimization
```python
# Optimize multiple relations
users = User.objects.select_related(
'organization', # FK
'created_by' # FK
).prefetch_related(
'roles', # M2M
'permissions' # M2M
)
```
## Index Recommendations
Suggest indexes for:
### Frequently Filtered Fields
```python
class User(models.Model):
email = models.EmailField()
status = models.CharField(max_length=20)
class Meta:
indexes = [
models.Index(fields=['email']), # WHERE email = ...
models.Index(fields=['status']), # WHERE status = ...
]
```
### Composite Indexes
```python
class Order(models.Model):
user = models.ForeignKey(User)
status = models.CharField(max_length=20)
created_at = models.DateTimeField()
class Meta:
indexes = [
# Composite index for common query pattern
models.Index(fields=['user', 'status']),
# Index for time-based queries
models.Index(fields=['-created_at']), # DESC order
]
```
### Foreign Key Indexes
```python
# Django automatically creates indexes for:
# - Primary keys
# - Unique fields
# - Foreign keys
# Manual indexes needed for:
# - Composite queries
# - Ordering fields
# - Search fields
```
## Caching Strategies
### Query Caching
```python
from django.core.cache import cache
def get_active_users():
cache_key = 'active_users'
users = cache.get(cache_key)
if users is None:
users = list(User.objects.filter(is_active=True).values())
cache.set(cache_key, users, 300) # Cache 5 minutes
return users
```
### Model Method Caching
```python
from django.utils.functional import cached_property
class User(models.Model):
@cached_property
def full_name(self):
"""Expensive computation cached per instance."""
return f"{self.first_name} {self.last_name}".strip()
```
## Pagination Patterns
```python
# DRF Pagination
from rest_framework.pagination import PageNumberPagination
class StandardResultsSetPagination(PageNumberPagination):
page_size = 100
page_size_query_param = 'page_size'
max_page_size = 1000
class UserViewSet(viewsets.ModelViewSet):
pagination_class = StandardResultsSetPagination
```
## Database Query Analysis
Check queries using Django Debug Toolbar patterns:
```python
from django.db import connection
from django.test.utils import override_settings
@override_settings(DEBUG=True)
def test_user_list_queries(self):
"""Test that user list doesn't have N+1 queries."""
with self.assertNumQueries(2): # Should be exactly 2 queries
response = self.client.get('/api/users/')
# 1 query: Fetch users
# 1 query: Fetch organizations (prefetched)
```
## Performance Checklist
For every queryset, verify:
- ✅ `select_related()` for accessed foreign keys
- ✅ `prefetch_related()` for reverse FKs and M2M
- ✅ `.only()` or `.defer()` if fetching many fields
- ✅ Indexes on filtered/ordered fields
- ✅ Pagination for lists
- ✅ `.count()` instead of `len(queryset)`
- ✅ `.exists()` instead of `if queryset`
- ✅ Bulk operations instead of loops
## Before/After Examples
### Example 1: User List with Organization
**❌ Before (N+1):**
```python
# views.py
class UserViewSet(viewsets.ModelViewSet):
queryset = User.objects.all() # 1 query
# serializer accesses user.organization.name → N queries
```
**✅ After (Optimized):**
```python
# views.py
class UserViewSet(viewsets.ModelViewSet):
queryset = User.objects.select_related('organization').all() # 2 queries total
```
### Example 2: Organization with Users
**❌ Before (N+1):**
```python
def organization_summary(self):
orgs = Organization.objects.all() # 1 query
return [
{
'name': org.name,
'user_count': org.users.count() # N queries!
}
for org in orgs
]
```
**✅ After (Optimized):**
```python
from django.db.models import Count
def organization_summary(self):
orgs = Organization.objects.annotate(
user_count=Count('users') # Single query with aggregation
).all()
return [
{
'name': org.name,
'user_count': org.user_count # No DB hit!
}
for org in orgs
]
```
## Integration with Testing
```python
# Test that optimizations work
@pytest.mark.django_db
def test_user_list_performance(django_assert_num_queries):
"""User list should use select_related to avoid N+1."""
UserFactory.create_batch(100) # Create 100 users
with django_assert_num_queries(2): # Only 2 queries allowed
users = list(User.objects.select_related('organization').all())
for user in users:
_ = user.organization.name # Should not hit DB
```
## Success Criteria
✅ NO N+1 queries in codebase
✅ All foreign key access uses `select_related()`
✅ All reverse FK/M2M use `prefetch_related()`
✅ Appropriate indexes on all models
✅ Pagination on all list endpoints
✅ Bulk operations used instead of loops
## Behavior
**Proactive enforcement:**
- Detect N+1 queries automatically
- Suggest optimizations immediately
- Calculate performance impact (queries before/after)
- Add indexes to models
- Explain WHY the optimization matters
**Never:**
- Require explicit "optimize queries" request
- Wait for production slowness
- Just warn without fixing
**Block completion if:**
- N+1 queries detected
- Missing indexes on frequently queried fields
- No pagination on list endpoints
- Bulk creates/updates done in loops
This ensures code is performant from day one.
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