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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.

78

1.84x
Quality

70%

Does it follow best practices?

Impact

94%

1.84x

Average score across 3 eval scenarios

SecuritybySnyk

Passed

No findings from the security scan

Fix and improve this skill with Tessl

tessl review fix ./tests/ext_conformance/artifacts/agents-wshobson/data-engineering/skills/data-quality-frameworks/SKILL.md

The canonical home for this skill is data-quality-frameworks in wshobson/agents

SKILL.md
Quality
Evals
Security

Quality

Content

65%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-structured pattern catalog with concrete, largely executable code across all three tools and clear sectioning. Its weaknesses are the absence of a consolidated step-by-step workflow with explicit validation and recovery checkpoints, and the monolithic single-file layout that inlines several long artifacts that would be better split into referenced files.

Suggestions

Add an explicit end-to-end workflow section (define suite -> build checkpoint -> run in CI -> review failures -> fix and re-run) with a validation checkpoint after each stage, so the batch validation process has clear feedback loops.

Split long artifacts into reference files (e.g. references/data-contract.md, references/custom-dbt-tests.md, references/pipeline.py) and keep SKILL.md as a concise overview with one-line pointers per pattern.

Trim the 'When to Use This Skill' section (it duplicates the frontmatter description) and drop the testing-pyramid ASCII diagram in favor of a one-line note, freeing tokens for the missing workflow guidance.

DimensionReasoningScore

Conciseness

The body is dense and code-dominated with little conceptual hand-holding, matching the 'efficient; minor instances of over-explanation' anchor. It is not 5 because the 'When to Use This Skill' section restates the description, the testing-pyramid ASCII diagram explains a concept Claude already knows, and the exhaustive contract YAML adds length without proportional value.

4 / 5

Actionability

Six patterns of concrete, mostly copy-paste-ready code (expectation suites, checkpoint YAML, dbt tests, custom tests, contracts, orchestration pipeline) match the 'mostly executable guidance; minor gaps' anchor. It misses 5 because of small correctness gaps, e.g. the Quick Start labels a Python snippet as 'daily_validation.yml' and mixes GX API styles (add_expectation_suite vs ExpectationSuite.add_expectation) that will not all run as written.

4 / 5

Workflow Clarity

The Quick Start gives a rough sequence (install, init, datasource, suite, checkpoint) and individual snippets check result.success, but there is no consolidated end-to-end workflow with explicit validation checkpoints or a fix-and-retry loop for what is a batch validation process, fitting the 'steps listed but checkpoints implicit' anchor. The batch-operation cap applies: validation exists only inside scattered snippets, not as an ordered workflow with recovery steps, so it cannot score 4.

3 / 5

Progressive Disclosure

Section headers provide reasonable navigation, but this ~590-line single file inlines substantial content that belongs in separate reference files (the full data contract specification, the pipeline class, the custom dbt test library), matching the 'some structure but content that should be separate is inline' anchor. It is above the 2 anchor because headers and pattern grouping keep it navigable, and there are no buried or nested references.

3 / 5

Total

14

/

20

Passed

Description

75%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 states what the skill does and when to use it, with specific tool names and natural trigger terms. The main weakness is that the 'when' clause mirrors the 'what' clause rather than broadening the trigger surface with synonyms and adjacent use cases like monitoring or CI/CD validation.

DimensionReasoningScore

Specificity

"Implement data quality validation with Great Expectations, dbt tests, and data contracts" names the domain and several concrete actions with specific tools, matching the 'several specific actions; minor gaps' anchor. It is not 5 because capabilities covered in the body (monitoring quality metrics, CI/CD automation) are absent, and not 3 because it lists far more than 1-2 actions.

4 / 5

Completeness

Both a clear what ("Implement data quality validation with Great Expectations, dbt tests, and data contracts") and an explicit "Use when..." clause are present. It does not clearly match the 5 anchor because the when-clause ("building data quality pipelines, implementing validation rules, or establishing data contracts") largely restates the what rather than adding distinct concrete trigger phrases.

4 / 5

Trigger Term Quality

Natural trigger phrases like "data quality pipelines", "dbt tests", "data contracts", and "validation rules" give good keyword coverage users would actually say. It falls short of the 5 anchor's comprehensive synonym coverage (missing "data testing", "data validation", "expectations") but is clearly above the 3 anchor's partial coverage.

4 / 5

Distinctiveness Conflict Risk

"Great Expectations, dbt tests, and data contracts" carves out a clear data-quality niche with distinct triggers, fitting the 'mostly distinct; minor overlap risk' anchor. Minor overlap remains with hypothetical dedicated dbt or Great Expectations skills, keeping it below the 5 anchor's minimal-conflict niche.

4 / 5

Total

16

/

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

skill_md_line_count

SKILL.md is long (591 lines); consider splitting into references/ and linking

Warning

Total

15

/

16

Passed

Repository
Dicklesworthstone/pi_agent_rust
Reviewed

Table of Contents

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