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.
74
62%
Does it follow best practices?
Impact
95%
1.20xAverage score across 3 eval scenarios
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./tests/ext_conformance/artifacts/agents-wshobson/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
100%
100%
Model YAML file naming
50%
100%
Source freshness config
40%
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
100%
83%
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
40%
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
100%
100%
DRY utility macro
100%
100%
dim_ prefix naming
100%
100%
Surrogate key
100%
100%
_loaded_at metadata
42%
100%
ref() macro used
100%
100%
dbt_utils tests
0%
100%
packages.yml
100%
100%
Model YAML documentation
100%
100%
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Table of Contents
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