SQLModel, SQL databases in Python, designed for simplicity, compatibility, and robustness.
85
Pending
Does it follow best practices?
Impact
85%
1.00xAverage score across 10 eval scenarios
Pending
The risk profile of this skill
{
"context": "This criteria evaluates how well the engineer uses SQLModel's connection pooling features to configure production and test database engines. The focus is specifically on proper usage of create_engine parameters for pool configuration.",
"type": "weighted_checklist",
"checklist": [
{
"name": "Uses create_engine",
"description": "Both functions use create_engine() from SQLModel to create database engines",
"max_score": 15
},
{
"name": "Production pool_size",
"description": "create_production_engine sets pool_size=10 parameter in create_engine",
"max_score": 15
},
{
"name": "Production max_overflow",
"description": "create_production_engine sets max_overflow=5 parameter in create_engine",
"max_score": 15
},
{
"name": "Production pool_timeout",
"description": "create_production_engine sets pool_timeout=30 parameter in create_engine",
"max_score": 15
},
{
"name": "Production pool_pre_ping",
"description": "create_production_engine sets pool_pre_ping=True parameter in create_engine",
"max_score": 15
},
{
"name": "Test StaticPool",
"description": "create_test_engine configures engine to use StaticPool via poolclass parameter or connect_args",
"max_score": 25
}
]
}docs
evals
scenario-1
scenario-2
scenario-3
scenario-4
scenario-5
scenario-6
scenario-7
scenario-8
scenario-9
scenario-10