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.

Quality

74%

Does it follow best practices?

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tessl review fix ./bundled/skills/data-quality-frameworks/SKILL.md

Quality

Content

65%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.

A highly actionable, code-rich skill that gives copy-paste-ready guidance for Great Expectations, dbt tests, data contracts, and pipeline orchestration. Its weaknesses are conciseness (conceptual padding Claude already knows), missing fix-retry feedback loops for batch validation, and a monolithic structure with no progressive disclosure into separate files.

Suggestions

Trim the 'Core Concepts' section: drop the testing-pyramid ASCII diagram and the definitions of completeness/uniqueness/validity that Claude already knows, keeping only the expectation-name mappings if useful.

Add an explicit validate-fix-retry loop to the automated pipeline pattern (e.g., on failure, log failed expectations, attempt remediation, then re-run the checkpoint before failing the pipeline).

Split the six Patterns into separate reference files (e.g., references/great_expectations.md, references/dbt_tests.md, references/data_contracts.md) and keep SKILL.md as a concise overview with one-level-deep links.

Dimension	Reasoning	Score
Conciseness	The body is dominated by valuable executable code, but the 'Core Concepts' section pads with concepts Claude already knows (the testing-pyramid ASCII diagram and a dimensions table defining completeness/uniqueness/validity) and the opening line restates the description.	3 / 5
Actionability	Fully executable, copy-paste-ready examples cover the common cases across all three tools: a Great Expectations suite builder, checkpoint YAML, dbt test schema, custom SQL tests, a data-contract YAML, and a complete orchestration pipeline class.	5 / 5
Workflow Clarity	The Quick Start and automated-pipeline pattern give a usable sequence with fail-fast checks (result.success / raise ValueError), but there are no explicit validate-then-fix-retry feedback loops for these batch operations, and checkpoints are implicit rather than spelled out.	3 / 5
Progressive Disclosure	Section headers are clear and well-organized, but everything lives in a single ~580-line SKILL.md with no bundle files and no one-level-deep references; the six pattern blocks are exactly the kind of content that would benefit from being split into separate reference files.	3 / 5
	Total	14 / 20 Passed

Description

83%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.

A strong, well-structured description that explicitly answers both what the skill does and when to use it, with concrete tool names that aid distinctiveness. The main weakness is that the listed actions are moderately high-level and could include more natural trigger synonyms.

Dimension	Reasoning	Score
Specificity	Lists several specific actions ('Implement data quality validation', 'building data quality pipelines', 'implementing validation rules', 'establishing data contracts') and names three concrete tools (Great Expectations, dbt tests, data contracts), with only minor gaps since the actions are somewhat high-level rather than granular operations.	4 / 5
Completeness	Clearly states what ('Implement data quality validation with Great Expectations, dbt tests, and data contracts') and explicitly when ('Use when building data quality pipelines, implementing validation rules, or establishing data contracts') with concrete trigger phrases.	5 / 5
Trigger Term Quality	Good coverage of natural terms users would say ('data quality validation', 'Great Expectations', 'dbt tests', 'data contracts', 'validation rules'), though a few natural synonyms or variants are missing relative to the comprehensive anchor.	4 / 5
Distinctiveness Conflict Risk	The three named frameworks give it a clear niche with distinct triggers, but 'data quality' and 'dbt tests' carry minor overlap risk with closely related data engineering or dbt-specific skills.	4 / 5
	Total	17 / 20 Passed

Validation

93%

Warnings & errors only

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

Validation — 15 / 16 Passed

Validation for skill structure

Criteria	Description	Result
skill_md_line_count	SKILL.md is long (588 lines); consider splitting into references/ and linking	Warning

	Total	15 / 16 Passed

Repository: foryourhealth111-pixel/Vibe-Skills
Path: bundled/skills/data-quality-frameworks/SKILL.md
Commit: d5ae560

Reviewed: 1 day ago

Table of Contents

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