Testing patterns for PHPUnit and Playwright E2E tests. Use when writing tests, debugging test failures, setting up test coverage, or implementing test patterns for ActivityPub features.
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Define database models with clear naming, appropriate data types, constraints, relationships, and validation at multiple layers. Use this skill when creating or modifying database model files, ORM classes, schema definitions, or data model relationships. Apply when working with model files (e.g., models.py, models/, ActiveRecord classes, Prisma schema, Sequelize models), defining table structures, setting up foreign keys and relationships, configuring cascade behaviors, implementing model validations, adding timestamps, or working with database constraints (NOT NULL, UNIQUE, foreign keys). Use for any task involving data integrity enforcement, relationship definitions, or model-level data validation.
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설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Core Rule: Models define data structure and integrity. Keep them focused on data representation, not business logic.
This Skill provides Claude Code with specific guidance on how to adhere to coding standards as they relate to how it should handle backend models.
Models: Singular, PascalCase (User, OrderItem, PaymentMethod)
Tables: Plural, snake_case (users, order_items, payment_methods)
Relationships: Descriptive and clear
user.orders (one-to-many)order.items (one-to-many)product.categories (many-to-many)Avoid generic names: data, info, record, entity
Timestamps on every model:
created_at = Column(DateTime, nullable=False, default=datetime.utcnow)
updated_at = Column(DateTime, nullable=False, default=datetime.utcnow, onupdate=datetime.utcnow)
Primary keys: Always explicit, prefer UUIDs for distributed systems or auto-incrementing integers for simplicity
Why: Auditing, debugging, data lineage tracking, soft deletes
Use constraints, not just application validation:
# NOT NULL for required fields
email = Column(String(255), nullable=False)
# UNIQUE constraints
email = Column(String(255), unique=True, nullable=False)
# CHECK constraints for business rules
age = Column(Integer, CheckConstraint('age >= 18'))
# Foreign keys with explicit cascade behavior
user_id = Column(Integer, ForeignKey('users.id', ondelete='CASCADE'))
Why: Database enforces rules even if application code bypassed. Defense in depth.
| Data | Type | Avoid |
|---|---|---|
| Email, URL | VARCHAR(255) | TEXT |
| Short text | VARCHAR(n) | TEXT |
| Long text | TEXT | VARCHAR |
| Money | DECIMAL(10,2) | FLOAT |
| Boolean | BOOLEAN | TINYINT |
| Timestamps | TIMESTAMP/DATETIME | VARCHAR |
| JSON data | JSON/JSONB | TEXT |
| UUIDs | UUID | VARCHAR(36) |
Why: Correct types enable database optimizations, constraints, and prevent data corruption.
Always index:
Example:
class Order(Base):
__tablename__ = 'orders'
id = Column(Integer, primary_key=True)
user_id = Column(Integer, ForeignKey('users.id'), index=True)
status = Column(String(50), index=True) # Frequently filtered
created_at = Column(DateTime, index=True) # Frequently sorted
Don't over-index: Each index slows writes. Index only queried columns.
Define both sides of relationships:
# One-to-many
class User(Base):
orders = relationship('Order', back_populates='user', cascade='all, delete-orphan')
class Order(Base):
user_id = Column(Integer, ForeignKey('users.id'))
user = relationship('User', back_populates='orders')
Cascade behaviors:
CASCADE: Delete related records (user deleted → orders deleted)SET NULL: Nullify foreign key (category deleted → product.category_id = NULL)RESTRICT: Prevent deletion if related records existNO ACTION: Database default, usually same as RESTRICTChoose based on business logic, not convenience.
Model-level validation (application):
@validates('email')
def validate_email(self, key, email):
if not re.match(r'^[^@]+@[^@]+\.[^@]+$', email):
raise ValueError('Invalid email format')
return email
Database-level constraints (see Data Integrity section)
Why both: Model validation provides clear error messages. Database constraints prevent data corruption if application bypassed.
YES:
@property def full_name)NO:
Models represent data structure, not behavior.
Normalize when:
Denormalize when:
Default to normalized. Denormalize only with evidence of performance issues.
Soft deletes:
deleted_at = Column(DateTime, nullable=True, index=True)
# Query only active records
query = session.query(User).filter(User.deleted_at.is_(None))
Polymorphic associations:
# Avoid if possible - complex and hard to maintain
# Prefer separate relationship fields or inheritance
Enums for fixed values:
from enum import Enum
class OrderStatus(str, Enum):
PENDING = 'pending'
PAID = 'paid'
SHIPPED = 'shipped'
DELIVERED = 'delivered'
status = Column(Enum(OrderStatus), nullable=False, default=OrderStatus.PENDING)
Test constraints and validation:
def test_user_email_required():
with pytest.raises(IntegrityError):
user = User(name='Test')
session.add(user)
session.commit()
def test_user_email_unique():
user1 = User(email='test@example.com')
user2 = User(email='test@example.com')
session.add(user1)
session.commit()
with pytest.raises(IntegrityError):
session.add(user2)
session.commit()
Test relationships:
def test_user_orders_cascade_delete():
user = User(email='test@example.com')
order = Order(user=user)
session.add(user)
session.commit()
session.delete(user)
session.commit()
assert session.query(Order).count() == 0
created_at and updated_at timestamps아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.
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Testing patterns for PHPUnit and Playwright E2E tests. Use when writing tests, debugging test failures, setting up test coverage, or implementing test patterns for ActivityPub features.
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Cloud laboratory platform for automated protein testing and validation. Use when designing proteins and needing experimental validation including binding assays, expression testing, thermostability measurements, enzyme activity assays, or protein sequence optimization. Also use for submitting experiments via API, tracking experiment status, downloading results, optimizing protein sequences for better expression using computational tools (NetSolP, SoluProt, SolubleMPNN, ESM), or managing protein design workflows with wet-lab validation.
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Add unsigned integer (uint) type support to PyTorch operators by updating AT_DISPATCH macros. Use when adding support for uint16, uint32, uint64 types to operators, kernels, or when user mentions enabling unsigned types, barebones unsigned types, or uint support.
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This skill should be used when the user asks to "create an agent", "add an agent", "write a subagent", "agent frontmatter", "when to use description", "agent examples", "agent tools", "agent colors", "autonomous agent", or needs guidance on agent structure, system prompts, triggering conditions, or agent development best practices for Claude Code plugins.
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Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.
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Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.
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