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

68%

Does it follow best practices?

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tessl review fix ./plugins/AI-Agents-Safe-Coding-Skills/skills/data-quality-frameworks/SKILL.md

The canonical home for this skill is data-quality-frameworks in rmyndharis/antigravity-skills

Quality

Content

50%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.

Well-structured and token-efficient, but the Instructions offer only abstract directives with no executable examples, no validation feedback loops for a batch CI/CD workflow, and a dangling reference to a non-existent resources file.

Suggestions

Add at least one concrete, executable example per technology — e.g. a Great Expectations expectation_suite snippet, a dbt test YAML block, and a data-contract schema — so the guidance is copy-paste ready.

Insert an explicit validation checkpoint and retry loop in the Instructions (e.g. 'Run tests, review failures, fix, re-run; only merge when passing') for the CI/CD automation step.

Create the referenced resources/implementation-playbook.md or remove the broken reference so the progressive-disclosure navigation actually resolves.

Dimension	Reasoning	Score
Conciseness	The body is lean and well-organized with no over-explanation of known concepts, though the opening sentence restates the frontmatter description and the 'Use this skill when' section re-lists triggers already in the description.	4 / 5
Actionability	Instructions are high-level directives ('Identify critical datasets', 'Define expectations/tests', 'Automate validation in CI/CD') with no executable code, concrete dbt test snippets, Great Expectations calls, or contract YAML — the specific steps to execute are missing.	2 / 5
Workflow Clarity	A rough sequence exists (Identify → Define → Automate → Alert/Remediation) but there are no validation checkpoints or feedback loops for this batch/CI-CD validation workflow, capping the score at 3 per the batch-operations guideline.	3 / 5
Progressive Disclosure	Structure is clean with clear sections and a one-level-deep signaled reference to 'resources/implementation-playbook.md', but that referenced file does not exist in the bundle, so navigation fails to resolve.	3 / 5
	Total	12 / 20 Passed

Description

87%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 description that clearly defines both capability and trigger conditions using concrete, named technologies and an explicit 'Use when' clause. Minor specificity and synonym-coverage gaps keep it just below a perfect profile.

Dimension	Reasoning	Score
Specificity	Names the domain and several concrete techniques — 'Great Expectations, dbt tests, and data contracts' — but the single verb 'Implement' drives all of them, leaving minor coverage gaps rather than comprehensive distinct actions.	4 / 5
Completeness	Explicitly states the what ('Implement data quality validation with...') and the when ('Use when building data quality pipelines, implementing validation rules, or establishing data contracts') with concrete trigger phrases, matching the top anchor.	5 / 5
Trigger Term Quality	Includes natural terms a user would say — 'data quality pipelines', 'validation rules', 'data contracts', 'Great Expectations', 'dbt tests' — but misses common synonyms like 'data testing' or 'data observability'.	4 / 5
Distinctiveness Conflict Risk	Occupies a clear niche (data quality with named tools Great Expectations/dbt/data contracts) and uses distinct triggers, giving minimal conflict risk with other skills.	5 / 5
	Total	18 / 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
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	15 / 16 Passed

Repository: administrakt0r/AI-Agents-Safe-Coding-Skills
Path: plugins/AI-Agents-Safe-Coding-Skills/skills/data-quality-frameworks/SKILL.md
Commit: 57c135b

Reviewed: 3 days ago

Table of Contents

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