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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.

57

Quality

72%

Does it follow best practices?

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SecuritybySnyk

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

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

SKILL.md
Quality
Evals
Security

Quality

Content

46%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.

The skill body is well-organized and token-efficient with sensible use/do-not-use scoping, but it provides almost no actionable guidance — every concrete detail is deferred to a playbook file that is missing from the bundle. As shipped, the skill leaves Claude with high-level directives and nowhere to go for specifics.

Suggestions

Add concrete, executable guidance to the Instructions section: example Great Expectations expectations (e.g., expect_column_values_to_not_be_null), specific dbt test types (not_null, unique, accepted_values, relationships), a data contract schema example, and the actual CI/CD commands to run validation.

Fix the broken reference: `resources/implementation-playbook.md` does not exist in the bundle. Either include the playbook file or restructure the path to match the bundle layout (e.g., references/implementation-playbook.md) so the pointer resolves.

Add validation checkpoints to the workflow, e.g., 'run expectations against a sample dataset and confirm they pass before deploying checks to CI/CD' and an explicit failure-handling loop (check fails → triage via alerting/ownership → fix rule or data → re-run).

DimensionReasoningScore

Conciseness

The body is lean (~44 lines) with no over-explanation of concepts Claude already knows, but the intro paragraph restates the frontmatter description and the playbook reference is duplicated in both the Instructions and Resources sections — minor instances that could be trimmed.

4 / 5

Actionability

Instructions are high-level hints ('Identify critical datasets and quality dimensions', 'Define expectations/tests and contract rules', 'Automate validation in CI/CD') with no commands, code, config examples, or tool-specific operations anywhere in the body. All substance is deferred to a reference file, matching the anchor for minimal concrete guidance with missing execution steps.

2 / 5

Workflow Clarity

A rough sequence exists (identify datasets → define expectations/contracts → automate in CI/CD → alerting/remediation), but there are no validation checkpoints or validate-fix-retry loops. For a skill governing batch CI/CD validation, missing verification steps cap this at 3 — the steps are listed but checkpoints are absent.

3 / 5

Progressive Disclosure

The structure pattern (lean overview + one clearly-signaled pointer to detail) is good, but the referenced file `resources/implementation-playbook.md` does not exist — no resources/, references/, scripts/, or assets/ directories are present in the bundle. Judged against the actual bundle structure, the single reference is dangling and the skill is a thin shell pointing to nothing, which breaks navigation entirely.

2 / 5

Total

11

/

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, well-formed description: it names a clear niche with specific tools, states concrete capabilities, and includes an explicit 'Use when' clause with natural trigger terms. Only minor improvements in action variety and synonym coverage would make it airtight.

DimensionReasoningScore

Specificity

The description lists several specific actions anchored to named tools — 'Implement data quality validation with Great Expectations, dbt tests, and data contracts', 'building data quality pipelines', 'implementing validation rules', 'establishing data contracts'. It falls short of anchor 5 because the verbs are repetitive and generic (implement/build/establish) and coverage has minor gaps (e.g., no mention of writing expectation suites or running tests).

4 / 5

Completeness

It explicitly answers both parts: 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') with concrete trigger phrases, mirroring the anchor-5 example structure. Not 4, because the 'when' clause is already explicit and specific rather than weakly implied.

5 / 5

Trigger Term Quality

Strong natural keyword coverage: 'data quality', 'data contracts', 'Great Expectations', 'dbt tests', 'validation rules', and 'data quality pipelines' are phrases users would naturally say. A few natural variations are missing ('data testing', 'expectation suites', 'data observability', 'data reliability'), which keeps it at anchor 4 rather than 5.

4 / 5

Distinctiveness Conflict Risk

The tool names 'Great Expectations' and 'dbt tests' plus the 'data contracts' concept define a clear niche with highly distinctive triggers, comparable to 'PDF files' in the anchor example. Overlap risk is minimal — only a general dbt or data-pipeline skill could plausibly compete, and the data-quality framing disambiguates.

5 / 5

Total

18

/

20

Passed

Validation

93%

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

CriteriaDescriptionResult

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-claude/skills/data-quality-frameworks/SKILL.md

Reviewed

Table of Contents

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