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

82%

Does it follow best practices?

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Securityby

Passed

No findings from the security scan

Quality

Content

65%

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

The body is concise and well-structured with good Use/Do-not-use framing, but its instructions stay at a high procedural level without executable specifics, lack validation checkpoints for a CI/CD batch workflow, and point to a resource file that is absent from the bundle.

Suggestions

Add concrete, executable guidance to Instructions — e.g. a minimal Great Expectations suite snippet, a dbt test command, or a data-contract YAML example — so the skill is copy-paste actionable rather than purely directional.

Insert an explicit validation checkpoint into the workflow (e.g. run the GE/dbt test suite in CI and only promote on a green run, with a fix-and-retry loop on failure) to satisfy the feedback-loop requirement for batch operations.

Provide the missing `resources/implementation-playbook.md` (or remove/correct the references to it) so the signaled one-level-deep reference actually resolves instead of leaving a dead end.

Dimension	Reasoning	Score
Conciseness	The body is lean — short bullet lists under tight section headers with no padding, no explanation of what data quality is, and no library primers — so every token earns its place and it assumes Claude's competence, matching the score-3 anchor.	3 / 3
Actionability	The Instructions give directional verbs ("Identify critical datasets and quality dimensions", "Define expectations/tests and contract rules", "Automate validation in CI/CD and schedule checks") but no concrete code, commands, or specifics, fitting the score-2 anchor of concrete-but-incomplete guidance with missing key details rather than the fully executable score-3 anchor.	2 / 3
Workflow Clarity	A clear sequence exists (identify → define → automate → set alerting/remediation), but there are no validation/verification checkpoints; since CI/CD data-quality validation is a batch operation, the rubric caps workflow clarity at 2, matching the "steps listed but validation gaps" anchor.	2 / 3
Progressive Disclosure	Sections are well organized and the body signals a one-level-deep reference ("open `resources/implementation-playbook.md`"), but that referenced file does not exist in the bundle (no references/scripts/assets/resources directories), so navigation is broken and cannot reach the score-3 "easy navigation" anchor.	2 / 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 well-crafted, third-person description that names concrete frameworks and tools, gives explicit "Use when" triggers with natural domain terms, and occupies a clear niche unlikely to conflict with other skills. It mirrors the structure of the rubric's good examples.

Dimension	Reasoning	Score
Specificity	"Implement data quality validation with Great Expectations, dbt tests, and data contracts" plus the Use-when "building data quality pipelines, implementing validation rules, or establishing data contracts" lists multiple specific concrete actions and named frameworks, matching the score-3 anchor rather than the single-action score-2 anchor.	3 / 3
Completeness	It explicitly answers both what ("Implement data quality validation with Great Expectations, dbt tests, and data contracts") and when ("Use when building data quality pipelines, implementing validation rules, or establishing data contracts"), matching the score-3 example with an explicit trigger clause.	3 / 3
Trigger Term Quality	Natural domain terms a user would say — "data quality pipelines", "validation rules", "data contracts", "Great Expectations", "dbt tests" — are well covered, matching the good-coverage score-3 anchor.	3 / 3
Distinctiveness Conflict Risk	The Great Expectations / dbt tests / data contracts niche has distinct triggers unlikely to fire for unrelated skills, matching the clear-niche score-3 anchor; it is far from the generic score-1 "Helps with code and documents".	3 / 3
	Total	12 / 12 Passed

Validation

100%

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 — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository: rmyndharis/antigravity-skills
Path: skills/data-quality-frameworks/SKILL.md
Commit: e63f7dd

Reviewed: about 5 hours ago

Table of Contents

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