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testland/dbt-testing

Authors and runs dbt data tests (generic, singular, and custom-macro), parses test failure output from run_results.json, and gates dbt build on test results. Use when the user works with a dbt project, asks about model assertions, or needs CI gates on a data pipeline.

76

Quality

95%

Does it follow best practices?

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SecuritybySnyk

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Overview
Quality
Evals
Security
Files

Quality

Content

87%

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

A well-structured, actionable reference that earns its tokens with dbt-specific knowledge and executable examples, deferring detail to two clearly signaled, real bundle files. The main gap is an explicit feedback loop for the batch build/test workflow.

Suggestions

Add an explicit validate→fix→retry loop for the build/test workflow, e.g. 'After dbt build, parse run_results.json; if any test failed, fix the model/test and re-run dbt build --select <failed node>+' before proceeding.

Frame the CI integration section as a numbered sequence with a checkpoint (run build → upload run_results.json as artifact → fail the pipeline on status:fail), so the gating decision is an explicit validated step rather than prose.

Include a short 'verify the test actually ran' check (e.g. confirm the expected test node appears in run_results.json) so practitioners can detect a misconfigured data_tests block that silently no-ops.

DimensionReasoningScore

Conciseness

Lean and dbt-specific throughout — the tests/data_tests rename, the DAG-skip behavior, and the trailing-semicolon trap are exactly the non-obvious details Claude would not already know, with no padding about what dbt or SQL is.

3 / 3

Actionability

Fully executable, copy-paste-ready artifacts: a complete schema.yml, a singular-test .sql, a Jinja {% test %} macro, dbt test/build selector commands, and jq one-liners against run_results.json.

3 / 3

Workflow Clarity

Sections (Authoring → Running → Parsing failures → CI integration) imply a sequence, but there is no explicit validate→fix→retry feedback loop for what is a batch data-pipeline operation, which caps this dimension per the rubric.

2 / 3

Progressive Disclosure

SKILL.md is a concise overview that clearly signals two real one-level-deep references (references/macros.md and references/ci-integration.md, both present) plus an explicit References list and external doc links.

3 / 3

Total

11

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12

Passed

Description

100%

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

A strong, third-person description that concisely states concrete capabilities and pairs them with an explicit, natural-language trigger clause. It cleanly answers both what the skill does and when to invoke it with no fluff.

DimensionReasoningScore

Specificity

Lists multiple concrete actions — 'Authors and runs dbt data tests (generic, singular, and custom-macro), parses test failure output from run_results.json, and gates dbt build on test results' — naming the three test types plus parsing and CI gating.

3 / 3

Completeness

Explicitly answers both what (author/run/parse/gate) and when via a clear 'Use when the user works with a dbt project, asks about model assertions, or needs CI gates' trigger clause.

3 / 3

Trigger Term Quality

Natural dbt-user phrasing — 'dbt project', 'model assertions', 'CI gates on a data pipeline' — the terms a practitioner would actually say when reaching for this skill.

3 / 3

Distinctiveness Conflict Risk

Occupies a clear niche (dbt data tests + run_results parsing + build gating) with distinct triggers unlikely to fire for unrelated skills.

3 / 3

Total

12

/

12

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.

Reviewed

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