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

70%

Does it follow best practices?

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

Quality

Content

53%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 well-organized, concise overview that correctly delegates detail to a referenced playbook, but it lacks concrete executable guidance and explicit validation checkpoints in a workflow that can block production pipelines. Progressive disclosure is sound in structure but the referenced file is missing and uses a non-conventional path.

Suggestions

Add concrete, executable examples (e.g. a Great Expectations expectation snippet or a dbt test command) so Instructions become copy-paste ready instead of abstract directives.

Insert an explicit validation checkpoint in the workflow (e.g. 'Run `ge validate` / `dbt test` and confirm all expectations pass before merging the pipeline change') to lift workflow clarity above the destructive/batch cap.

Place the detailed material at `references/implementation-playbook.md` (matching bundle convention) and ensure the file actually exists, or convert the inline reference to a markdown link so navigation is unambiguous.

Dimension	Reasoning	Score
Conciseness	The body is lean and directive with no concept over-explanation, but the opening sentence ('Production patterns for implementing data quality with Great Expectations, dbt tests, and data contracts to ensure reliable data pipelines') largely restates the frontmatter description and could be trimmed; a 5 requires every token to earn its place.	4 / 5
Actionability	Instructions are high-level directives ('Identify critical datasets', 'Define expectations/tests and contract rules') with no concrete code, commands, or specifics on how to author an expectation or wire dbt tests into CI, fitting the 'minimal concrete guidance; high-level hints' anchor rather than the pseudocode/gaps level 3.	2 / 5
Workflow Clarity	A rough sequence is present (identify -> define -> automate -> alert/own/remediate) but there are no validation checkpoints verifying expectations pass before promoting changes; because CI/CD validation can block pipelines (a batch/risky operation), the rubric caps workflow clarity at 3.	3 / 5
Progressive Disclosure	Good section structure with a single one-level-deep reference (`resources/implementation-playbook.md`) signaled both in Instructions and Resources; not a 5 because the referenced bundle file is absent from the workspace and the path uses `resources/` rather than the conventional `references/` directory.	4 / 5
	Total	13 / 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, third-person description that clearly answers both what the skill does and when to invoke it, anchored by specific tooling and concrete trigger phrases. The only minor gap is action enumeration and a few missing trigger synonyms.

Suggestions

Enumerate distinct verbs (e.g. 'define expectations, author dbt tests, negotiate data contracts') instead of one 'implement' to push specificity to 5.

Add common trigger synonyms like 'data tests' or 'expectations suites' to round out trigger term coverage.

Dimension	Reasoning	Score
Specificity	Names the domain plus three concrete tooling actions ('validation with Great Expectations, dbt tests, and data contracts'), giving several specific actions with only minor coverage gaps; not a 5 because 'implement' is one verb wrapping all three rather than distinct enumerated actions.	4 / 5
Completeness	Explicitly states 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') with concrete trigger phrases, matching the top anchor.	5 / 5
Trigger Term Quality	Natural trigger phrases ('building data quality pipelines, implementing validation rules, establishing data contracts') align well with user language, but a few common variants (e.g. 'data tests', 'expectations suite') are absent.	4 / 5
Distinctiveness Conflict Risk	The named frameworks (Great Expectations, dbt tests, data contracts) carve a clear niche with distinct triggers and minimal overlap risk with unrelated skills.	5 / 5
	Total	18 / 20 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: 3eff4af

Reviewed: 1 day ago

Table of Contents

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