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

51%

Does it follow best practices?

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Securityby

Passed

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

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

Quality

Content

25%Scale 1-5

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

This skill is essentially a hollow shell — it provides a high-level outline of data quality concepts but offers zero concrete, actionable guidance. There are no code examples, no configuration templates, no specific commands for Great Expectations or dbt, and no executable workflows. The entire substance is deferred to a referenced playbook file that doesn't exist in the bundle, making the skill nearly useless in practice.

Suggestions

Add concrete, executable code examples for at least one framework (e.g., a Great Expectations checkpoint configuration, a dbt test YAML, or a data contract schema definition) directly in the SKILL.md.

Replace the abstract instruction bullets with a specific multi-step workflow including validation checkpoints, e.g., '1. Create expectation suite: `great_expectations suite new ...` 2. Run validation: `great_expectations checkpoint run ...` 3. Check results and fix failures before proceeding.'

Either provide the referenced `resources/implementation-playbook.md` bundle file or inline the essential patterns and templates so the skill is self-contained enough to be useful.

Add at least one complete input/output example showing what a data quality check looks like in practice (e.g., a dbt test YAML file with expected pass/fail behavior).

Dimension	Reasoning	Score
Conciseness	The content is relatively short but includes some unnecessary sections like 'Use this skill when' and 'Do not use this skill when' that are somewhat obvious and don't add much value. The instructions themselves are extremely terse to the point of being unhelpful, but the overall file isn't padded with explanations of concepts Claude already knows.	3 / 5
Actionability	The instructions are entirely vague and abstract — 'Identify critical datasets,' 'Define expectations/tests,' 'Automate validation in CI/CD' — with zero concrete code, commands, configuration examples, or specific steps. There is no executable guidance whatsoever; it reads like a high-level checklist of goals rather than actionable instructions.	1 / 5
Workflow Clarity	There is a rough sequence implied (identify → define → automate → alert), but steps are poorly defined with no specifics, no validation checkpoints, and no feedback loops. For a skill involving data quality validation pipelines (which are multi-step and can involve destructive/batch operations), the lack of any concrete workflow or validation steps is a significant gap.	2 / 5
Progressive Disclosure	The skill references `resources/implementation-playbook.md` for detailed content, which is a reasonable structure, but no bundle files are provided so the reference is unverifiable and potentially broken. The SKILL.md itself contains almost no substantive content — it defers nearly everything to the playbook without providing even minimal inline examples or quick-start guidance.	2 / 5
	Total	8 / 20 Passed

Description

78%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.

This is a solid description that clearly communicates both what the skill does and when to use it, with good tool-specific trigger terms. Its main weakness is the lack of granular action descriptions—it says 'implement' but doesn't enumerate specific capabilities like creating expectation suites, configuring checkpoints, or defining contract schemas. Adding more concrete actions and a few additional trigger term synonyms would strengthen it.

Suggestions

Add more specific concrete actions such as 'create expectation suites, configure data checkpoints, define schema contracts, run validation checks' to improve specificity.

Include additional trigger term synonyms like 'data testing', 'data checks', 'schema validation', or 'data profiling' to improve keyword coverage.

Dimension	Reasoning	Score
Specificity	Names the domain (data quality validation) and lists specific tools (Great Expectations, dbt tests, data contracts), but doesn't describe concrete actions beyond 'implement' and 'validation rules'. Lacks detail on what specific operations are performed (e.g., schema validation, freshness checks, expectation suites).	3 / 5
Completeness	Clearly 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) with explicit trigger phrases.	5 / 5
Trigger Term Quality	Includes good natural keywords like 'data quality', 'validation', 'Great Expectations', 'dbt tests', 'data contracts', and 'validation rules'. Missing some common synonyms and variations like 'data testing', 'data checks', 'schema validation', 'data profiling', or 'expectation suites'.	4 / 5
Distinctiveness Conflict Risk	Fairly distinct due to the specific tooling mentioned (Great Expectations, dbt tests) and the focused domain of data quality validation. Minor overlap risk with general dbt skills or broader data pipeline skills, but the data quality focus narrows it well.	4 / 5
	Total	16 / 20 Passed

Validation

90%

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 — 10 / 11 Passed

Validation for skill structure

Criteria	Description	Result
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	10 / 11 Passed

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

Reviewed: 3 days ago

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