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.
84
66%
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.mdQuality
Discovery
75%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 well-structured with a clear 'Use when' clause and targets a distinct domain (dbt). Its main weakness is that the capability descriptions are somewhat categorical rather than listing concrete specific actions, and the trigger terms could be expanded to include more natural user language variations like specific dbt commands or concepts.
Suggestions
Replace category-level terms with more concrete actions, e.g., 'write staging and marts models, configure materializations, define schema tests, set up sources and refs, build incremental models'
Add more natural trigger terms users might use, such as 'SQL models', 'dbt run', 'dbt test', 'ref()', 'sources.yml', 'schema.yml', 'materializations', 'staging/marts layers'
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Names the domain (dbt/analytics engineering) and mentions some actions like 'model organization, testing, documentation, and incremental strategies,' but these are more like category labels than concrete specific actions. Compare to a score-3 example which would list things like 'create staging models, write schema tests, configure incremental materializations, generate dbt docs.' | 2 / 3 |
Completeness | Clearly answers both 'what' (model organization, testing, documentation, incremental strategies) and 'when' with an explicit 'Use when...' clause covering building data transformations, creating data models, or implementing analytics engineering best practices. | 3 / 3 |
Trigger Term Quality | Includes relevant terms like 'dbt', 'data build tool', 'data transformations', 'data models', and 'analytics engineering', which are good. However, it misses common natural variations users might say such as 'SQL models', 'ref()', 'sources', 'materializations', 'dbt run', 'dbt test', 'staging/marts', or '.yml schema files'. | 2 / 3 |
Distinctiveness Conflict Risk | The description is clearly focused on dbt specifically, which is a distinct tool/domain. Terms like 'dbt', 'data build tool', 'incremental strategies', and 'analytics engineering' create a clear niche that is unlikely to conflict with general SQL or data processing skills. | 3 / 3 |
Total | 10 / 12 Passed |
Implementation
57%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
The skill excels at actionability with comprehensive, executable dbt patterns covering the full model lifecycle. However, it suffers from being a monolithic document that tries to cover everything inline rather than using progressive disclosure. Adding explicit validation workflows (e.g., test after build, freshness checks before deployment) and splitting detailed patterns into referenced files would significantly improve it.
Suggestions
Split detailed SQL patterns (staging, intermediate, marts, macros, incremental) into separate referenced files (e.g., PATTERNS_STAGING.md, PATTERNS_INCREMENTAL.md) and keep SKILL.md as a concise overview with links.
Add an explicit build workflow with validation checkpoints, e.g., '1. dbt run --select staging → 2. dbt test --select staging → 3. Fix failures → 4. dbt run --select marts → 5. dbt test'
Remove the 'When to Use This Skill' section — this information is already conveyed by the skill description and title, and Claude can infer appropriate usage context.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is fairly comprehensive but includes some unnecessary verbosity. The 'When to Use This Skill' section restates what Claude would infer from context. The extensive inline SQL examples (300+ lines) could be trimmed or split into referenced files. However, most content is substantive and not padded with explanations of basic concepts. | 2 / 3 |
Actionability | Excellent actionability with fully executable SQL models, complete YAML configurations, working macro definitions, and copy-paste ready dbt commands. Every pattern includes real, runnable code with proper file paths and realistic data scenarios. | 3 / 3 |
Workflow Clarity | The model layers are clearly sequenced (sources → staging → intermediate → marts), and the dbt commands section provides operational steps. However, there are no explicit validation checkpoints or feedback loops — e.g., no guidance on running `dbt test` after `dbt run`, no error recovery steps for failed incremental builds, and no verification workflow for the multi-step build process. | 2 / 3 |
Progressive Disclosure | This is a monolithic wall of content (~350 lines) with no references to external files and no bundle files to support it. The extensive inline SQL patterns for staging, intermediate, marts, macros, and incremental strategies would benefit greatly from being split into separate referenced files, with SKILL.md serving as a concise overview. | 1 / 3 |
Total | 8 / 12 Passed |
Validation
90%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 10 / 11 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
skill_md_line_count | SKILL.md is long (557 lines); consider splitting into references/ and linking | Warning |
Total | 10 / 11 Passed | |
- Repository
- wshobson/agents
- Commit
112197c
- Reviewed
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