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data-quality-frameworks

Implement data quality validation with Great Expectations, dbt tests, and data contracts. Use when building data quality pipelines, implementing validation rules, or establishing data contracts.

66

Quality

Does it follow best practices?

Impact

No eval scenarios have been run

SecuritybySnyk

Passed

No known issues

SKILL.md
Quality
Evals
Security

Quality

Content

57%

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

The overview structure and reference signaling are sound, but the body is undermined by a broken code fence that leaks ~35 lines of an undefined DataQualityPipeline class into prose, plus concept-explanation padding Claude does not need. Fixing the corrupted example and tightening the conceptual sections would lift the weaker dimensions.

Suggestions

Restore the missing opening ``` fence before the 'Summary:' fragment and define the DataQualityPipeline class so the pipeline example is complete and executable rather than orphaned prose under a bogus '## Summary: {total_passed}/{total_tables} tables passed")' header.

Trim or move the 'Data Quality Dimensions' table and ASCII 'Testing Pyramid' — these explain standard concepts Claude already knows; condense them or push them into references/details.md.

Add an explicit sequenced workflow with validation checkpoints (define expectations → run checkpoint → review failed expectations → fix data/expectations → re-validate → gate the pipeline) instead of a single fragmented validation snippet.

DimensionReasoningScore

Conciseness

Mostly concrete, but the 'Data Quality Dimensions' table and ASCII 'Testing Pyramid' explain standard concepts Claude already knows, and ~35 lines of an orphaned Python block (missing its opening fence) render as noisy prose under a bogus '## Summary: {total_passed}/{total_tables} tables passed")' header.

2 / 3

Actionability

The Quick Start gives executable GX commands and API calls, but the larger DataQualityPipeline example is incomplete — the class is never defined and the orphaned method body has no opening code fence, so it is not copy-paste ready.

2 / 3

Workflow Clarity

A validation gate exists only as a fragment inside the broken code ('if not all(r.passed...): raise ValueError'), but there is no clearly sequenced define-expectations → run-checkpoint → review-failures → fix-and-revalidate workflow with explicit checkpoints for a batch validation task.

2 / 3

Progressive Disclosure

The body signals a real one-level-deep reference ('Detailed pattern documentation lives in references/details.md. Read that file when the navigation tier above is insufficient.'), and that file exists, giving clear overview-to-detail navigation.

3 / 3

Total

9

/

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 pairs a concrete capability statement with an explicit 'Use when' trigger clause and named frameworks. It cleanly answers both what the skill does and when to invoke it. No changes needed.

DimensionReasoningScore

Specificity

Lists concrete actions ('Implement data quality validation', 'building data quality pipelines', 'implementing validation rules', 'establishing data contracts') tied to specific named tools (Great Expectations, dbt tests, data contracts), matching the multi-action anchor.

3 / 3

Completeness

Clearly states what ('Implement data quality validation with Great Expectations, dbt tests, and data contracts') and when via an explicit 'Use when building data quality pipelines, implementing validation rules, or establishing data contracts' trigger clause.

3 / 3

Trigger Term Quality

Natural terms a user would actually say are well covered: 'data quality', 'validation', 'Great Expectations', 'dbt tests', 'data contracts', and 'data quality pipelines'.

3 / 3

Distinctiveness Conflict Risk

The data-quality niche is anchored by distinct, named frameworks (Great Expectations, dbt tests, data contracts), making it unlikely to trigger 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.

Repository
wshobson/agents
Reviewed

Table of Contents

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