dbt-model-generator
Dbt Model Generator - Auto-activating skill for Data Pipelines. Triggers on: dbt model generator, dbt model generator Part of the Data Pipelines skill category.
36
3%
Does it follow best practices?
Impact
97%
0.98xAverage score across 3 eval scenarios
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./planned-skills/generated/11-data-pipelines/dbt-model-generator/SKILL.mdQuality
Discovery
7%Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.
This description is essentially a title and category label with no substantive content. It fails to describe what the skill does, provides no natural trigger terms beyond the skill's own name (duplicated), and lacks any 'Use when...' guidance. It would be nearly useless for Claude to differentiate this skill from others in a large skill library.
Suggestions
Add concrete actions the skill performs, e.g., 'Generates dbt SQL models, creates schema.yml files, configures sources and staging layers, and scaffolds incremental or snapshot models.'
Add an explicit 'Use when...' clause with natural trigger scenarios, e.g., 'Use when the user asks to create dbt models, build data transformations, set up a dbt project, or generate SQL for a data warehouse.'
Include natural keyword variations users might say, such as 'dbt', 'data transformation', 'SQL model', 'staging model', 'data warehouse', 'ELT', 'schema.yml', 'sources', 'refs'.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | The description names the domain ('dbt model generator', 'Data Pipelines') but provides no concrete actions. It does not describe what the skill actually does—no mention of generating SQL models, creating schema files, configuring sources, or any other specific capability. | 1 / 3 |
Completeness | The description fails to answer 'what does this do' beyond the name itself, and the 'when' clause is essentially just restating the skill name as a trigger. There is no explicit 'Use when...' guidance with meaningful trigger scenarios. | 1 / 3 |
Trigger Term Quality | The only trigger terms listed are 'dbt model generator' repeated twice. It misses natural user phrases like 'create a dbt model', 'SQL transformation', 'staging model', 'data warehouse', 'dbt project', 'ref()', 'source()', or '.sql files'. | 1 / 3 |
Distinctiveness Conflict Risk | The mention of 'dbt' provides some niche specificity that distinguishes it from generic data or SQL skills. However, 'Data Pipelines' is broad and could overlap with ETL, Airflow, or other pipeline-related skills. | 2 / 3 |
Total | 5 / 12 Passed |
Implementation
0%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This skill is essentially an empty placeholder with no substantive content. It contains only meta-descriptions of what the skill could do without providing any actual dbt model generation guidance, code examples, SQL templates, or YAML configurations. It fails on every dimension because it teaches Claude nothing actionable about dbt model generation.
Suggestions
Add concrete, executable examples of dbt model YAML and SQL files (e.g., a staging model, an intermediate model, and a mart model) with proper ref() and source() usage.
Include a clear workflow: 1) Define sources in schema.yml, 2) Create staging models, 3) Build intermediate models, 4) Create mart models, 5) Validate with `dbt build --select model_name`.
Remove all meta-description sections ('Purpose', 'When to Use', 'Capabilities', 'Example Triggers') and replace with actionable content like naming conventions, materialization strategies, and testing patterns.
Add a quick-start section with a complete, copy-paste-ready dbt model example including the SQL file and corresponding schema.yml entry.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is entirely filler and meta-description. It explains what the skill does in abstract terms without providing any actual technical content. Every section restates the same vague information about 'dbt model generator' without teaching Claude anything it doesn't already know. | 1 / 3 |
Actionability | There is zero concrete guidance—no code, no commands, no dbt model examples, no SQL, no YAML configurations. The skill describes rather than instructs, offering only vague promises like 'provides step-by-step guidance' without actually providing any. | 1 / 3 |
Workflow Clarity | No workflow is defined at all. There are no steps, no sequences, no validation checkpoints. The content merely states it can provide 'step-by-step guidance' without including any actual steps. | 1 / 3 |
Progressive Disclosure | The content is a monolithic block of meta-descriptions with no meaningful structure. There are no references to detailed files, no quick-start section with real content, and no navigation to deeper resources. | 1 / 3 |
Total | 4 / 12 Passed |
Validation
81%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 9 / 11 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
allowed_tools_field | 'allowed-tools' contains unusual tool name(s) | Warning |
frontmatter_unknown_keys | Unknown frontmatter key(s) found; consider removing or moving to metadata | Warning |
Total | 9 / 11 Passed | |
- Commit
4dee593
- Reviewed
Table of Contents
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