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dbt-transformation-patterns

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.

69

Quality

83%

Does it follow best practices?

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SKILL.md
Quality
Evals
Security

Quality

Content

80%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The body is well-structured and highly actionable with copy-paste-ready dbt configuration and project layout, plus clean one-level progressive disclosure to details.md. Its main gap is the absence of an inline sequenced workflow with validation checkpoints.

Suggestions

Add a short sequenced 'Build a new model' workflow with explicit steps and a validation checkpoint (e.g. run `dbt test` after creating the model, only proceed on pass) to raise workflow clarity.

Trim or differentiate the 'When to Use This Skill' list from the frontmatter description to avoid restating the same triggers twice.

Surface one minimal executable SQL example (e.g. a stg_ model) inline so the pattern is fully self-contained without requiring details.md.

DimensionReasoningScore

Conciseness

The body is largely lean — a layer-flow diagram, a naming table, full dbt_project.yml, a project tree, and terse do/don't bullets — with only minor sections (the 'When to Use' list) that restate the description and could be trimmed.

4 / 5

Actionability

Provides copy-paste-ready concrete artifacts: a complete dbt_project.yml, a real project directory tree, and a naming-convention table with concrete examples like stg_stripe__payments and dim_customers.

5 / 5

Workflow Clarity

The body presents conceptual architecture (the medallion layer flow) and structure rather than an explicitly sequenced multi-step workflow with validation checkpoints; operational sequences live in references rather than inline.

3 / 5

Progressive Disclosure

SKILL.md is a clear overview with a single well-signaled one-level-deep reference ('Detailed pattern documentation lives in references/details.md'), and that referenced file exists and is substantive (447 lines).

5 / 5

Total

17

/

20

Passed

Description

87%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

The description is strong: it concisely names concrete capabilities and provides explicit 'Use when' trigger guidance scoped to dbt and analytics engineering. Minor keyword-variation coverage is the only weakness.

DimensionReasoningScore

Specificity

Names the dbt/analytics-engineering domain and lists several concrete actions ('model organization, testing, documentation, and incremental strategies') in third person, with only minor gaps in full coverage.

4 / 5

Completeness

Explicitly states both what it does ('Master dbt ... with model organization, testing, documentation, and incremental strategies') and when to use it ('Use when building data transformations, creating data models, or implementing analytics engineering best practices').

5 / 5

Trigger Term Quality

Includes natural user-facing phrases ('building data transformations', 'creating data models', 'analytics engineering best practices') alongside the named tool dbt, but lacks a few common synonyms/variations.

4 / 5

Distinctiveness Conflict Risk

Targets a clear niche (dbt for analytics engineering) with distinct triggers, making overlap with other skills minimal.

5 / 5

Total

18

/

20

Passed

Validation

100%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
wshobson/agents
Reviewed

Table of Contents

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