Master dbt (data build tool) for analytics engineering with model organization, testing, documentation, and incremental strategies. Use when building data transformations, creating data models, or implementing analytics engineering best practices.
82
62%
Does it follow best practices?
Impact
96%
1.15xAverage score across 6 eval scenarios
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./plugins/data-engineering/skills/dbt-transformation-patterns/SKILL.mdStaging layer structure and source definitions
stg_ naming convention
100%
100%
Staging directory structure
100%
100%
Source YAML file naming
0%
100%
Model YAML file naming
0%
100%
Source freshness config
100%
100%
Source column tests
100%
100%
CTE pattern
100%
100%
source() macro used
100%
100%
Column renaming — id to entity_id
100%
100%
Column renaming — _loaded_at
0%
100%
Lowercase string fields
66%
100%
Cents-to-dollars conversion
100%
100%
Model YAML documentation
100%
100%
Incremental model strategies and intermediate layer
int_ prefix naming
100%
100%
fct_ prefix naming
100%
100%
Directory structure
100%
100%
ref() macro used
100%
100%
Incremental materialization
100%
100%
unique_key specified
100%
100%
Merge strategy for updates
100%
100%
merge_update_columns
0%
100%
is_incremental() filter
100%
100%
Filter uses max(updated_at)
100%
100%
Intermediate uses CTE structure
100%
100%
on_schema_change config
50%
0%
Project configuration, macros, and mart dimension models
Layer materializations
66%
66%
Schema assignments
100%
100%
vars for start date
100%
100%
generate_schema_name macro
100%
100%
limit_data_in_dev macro
50%
100%
DRY utility macro
100%
66%
dim_ prefix naming
100%
100%
Surrogate key
100%
100%
_loaded_at metadata
0%
100%
ref() macro used
100%
100%
dbt_utils tests
0%
100%
packages.yml
100%
100%
Model YAML documentation
80%
100%
Comprehensive model YAML testing patterns
accepted_values test
100%
100%
expression_is_true test
100%
100%
recency test on fact table
100%
100%
relationships test in model YAML
100%
100%
Primary key tests — unique + not_null
100%
100%
packages.yml with dbt_utils
100%
100%
Model-level descriptions
100%
100%
Column-level descriptions
100%
100%
Model YAML naming
100%
100%
not_null on numeric measures
100%
100%
Test coverage on both dim and fct
100%
100%
Partition-based incremental strategy
insert_overwrite strategy
0%
100%
partition_by config
100%
100%
Derived date partition column
50%
100%
is_incremental() filter with lookback
100%
100%
fct_ prefix naming
100%
100%
incremental materialization
100%
100%
ref() macro usage
100%
100%
Correct directory placement
100%
100%
CTE structure
100%
100%
_loaded_at metadata
0%
50%
No unique_key for insert_overwrite
33%
100%
Variable-driven models and dev environment workflow
var() usage in model SQL
100%
100%
No hardcoded dates in models
100%
100%
limit_data_in_dev used in model
100%
100%
limit_data_in_dev macro defined
100%
100%
vars declared in dbt_project.yml
100%
100%
generate_schema_name macro
100%
100%
Layer materializations in dbt_project.yml
100%
100%
Staging uses view materialization
100%
100%
Model references use ref()
100%
100%
dbt build mentioned in workflow
0%
100%
Separate dev and prod targets described
100%
100%
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Table of Contents
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