Install
$ agentstack add skill-lkrdev-lookml-skills-lookml-tests ✓ scanned · ✓ verified — works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
About
LookML Testing Standards
Testing is critical for maintaining trust in data. LookML tests allow us to verify that our semantic model behaves as expected and that the underlying data conforms to our assumptions.
1. File Organization
- Location: Define tests in
tests/[explore_name].test.lkml. - One Suite Per Explore: Each file should contain all the test definitions for a specific Explore.
- Naming Convention:
[explore_name].test.lkml(e.g.,orders.test.lkml).
2. Test Structure
Each test consists of an explore_source query and an assert statement.
test: [test_name] {
explore_source: [explore_name] {
column: [column_name] { field: [view_name].[field_name] }
filters: {
field: [view_name].[field_name]
value: "[value]"
}
}
assert: [assertion_name] {
expression: ${[view_name].[field_name]} [operator] [value] ;;
}
}
3. Types of Tests
A. Integrity Checks (Critical)
Verify that Primary Keys remain unique after joins. This is the best defense against "fanout" errors caused by incorrect one_to_many join definitions.
Example: Primary Key Uniqueness
test: orders_pk_is_unique {
explore_source: orders {
column: order_id {}
column: count {}
# Limit to recent data to save costs/time if table is large
filters: {
field: orders.created_date
value: "last 7 days"
}
}
assert: order_id_is_unique {
expression: ${orders.count} = 1 ;;
}
}
B. Accuracy Tests
Validate specific measure values against known constants or expectations.
Example: Revenue is Positive
test: revenue_is_positive {
explore_source: orders {
column: total_revenue {}
filters: {
field: orders.created_date
value: "yesterday"
}
}
assert: revenue_greater_than_zero {
expression: ${orders.total_revenue} >= 0 ;;
}
}
C. Business Logic Validation
Ensure calculations behave as expected. For example, checking that gross_margin is never greater than revenue or that lifetime_orders is never NULL for an active user.
Example: Logic Check
test: margin_less_than_revenue {
explore_source: orders {
column: total_revenue {}
column: total_margin {}
}
assert: margin_is_valid {
expression: ${orders.total_margin} <= ${orders.total_revenue} ;;
}
}
4. Best Practices
- Descriptive Extensions: Use informative names for tests (
orders_pk_is_unique) and assertions (order_id_is_unique). - Performance: Use filters (e.g.,
last 7 days) to limit the scan size for large tables, unless verifying full history is required. - Model Inclusion: Ensure
testfiles are included in the model file (e.g.,include: "/tests/*.test.lkml").
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: lkrdev
- Source: lkrdev/lookml_skills
- License: MIT
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.