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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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
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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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How to use the Adaptyv Bio Foundry API and Python SDK for protein experiment design, submission, and results retrieval. Use this skill whenever the user mentions Adaptyv, Foundry API, protein binding assays, protein screening experiments, BLI/SPR assays, thermostability assays, or wants to submit protein sequences for experimental characterization. Also trigger when code imports `adaptyv`, `adaptyv_sdk`, or `FoundryClient`, or references `foundry-api-public.adaptyvbio.com`.
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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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Time-blind friendly planning, executive function support, and daily structure for ADHD brains. Specializes in realistic time estimation, dopamine-aware task design, and building systems that actually work for neurodivergent minds.
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This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.
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Unified analysis pipeline for affinity-based proteomics platforms — Olink (PEA, NPX) and SomaLogic SomaScan (SOMAmer, RFU). Platform-aware QC, normalisation, differential abundance, volcano plots, heatmaps, and PCA.
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