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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

57%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 body is a rich, actionable pattern catalog with concrete code across GE, dbt, data contracts, and orchestration, but it over-explains known concepts, mixes GX API versions, and inlines ~590 lines with no progressive file-splitting. Tightening Core Concepts, unifying the GX API, and extracting large examples into reference files would lift the lower dimensions.

Suggestions

Trim or remove the 'Core Concepts' Data Quality Dimensions table and Testing Pyramid diagram — these are concepts Claude already knows — and de-duplicate the status enum repeated across multiple patterns.

Unify the Great Expectations API style (legacy ExpectationConfiguration vs. new gx.get_context()/suite.add_expectation) so each snippet is copy-paste runnable against a single GX version.

Extract the large concrete blocks (full data contract YAML, the DataQualityPipeline class, the orders expectation suite) into one-level-deep reference files (e.g. contracts/EXAMPLES.md, pipelines/REFERENCE.md) linked from the body to improve progressive disclosure.

DimensionReasoningScore

Conciseness

The "Core Concepts" section explains the Data Quality Dimensions table and Testing Pyramid ASCII diagram — concepts Claude already knows — and the status enum ["pending", ...] is repeated across four patterns; the bulk is concrete code so it is mostly efficient, but the unnecessary explanation exceeds the "minor" threshold for a 4.

3 / 5

Actionability

Extensive executable code (pip install, GE expectation suites, dbt YAML, SQL generic/singular tests, data contract YAML, orchestration class) is mostly copy-paste ready, but the snippets mix the legacy fluent GX API (ExpectationConfiguration, ExpectationSuite(...)) with the newer gx.get_context()/suite.add_expectation API — minor version gaps.

4 / 5

Workflow Clarity

The skill is a pattern catalog rather than a guided sequenced workflow, and the batch validation pipeline (Pattern 6) only checks result.success at the very end with no mid-process validation checkpoints; the batch-operation cap at 3 applies.

3 / 5

Progressive Disclosure

No bundle files exist and ~590 lines are inlined in a single SKILL.md with good section headers but no references to separate files (only external doc links); content that could be split (full data contract YAML, pipeline class, GE suite builder) is all inline, so structure is present but content that should be separate is not split out.

3 / 5

Total

13

/

20

Passed

Description

83%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.

The description is well-crafted: third-person imperative voice, explicit what-and-when structure with concrete trigger phrases, and a clear niche anchored by specific tools. Minor gaps are missing synonyms in triggers and slight overlap risk with a general dbt skill.

DimensionReasoningScore

Specificity

Quotes "Implement data quality validation with Great Expectations, dbt tests, and data contracts" — names the domain plus three concrete mechanisms (GE, dbt tests, data contracts), which is several specific actions but not fully comprehensive (monitoring metrics and CI/CD automation covered in the body are absent).

4 / 5

Completeness

Explicitly 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 concrete trigger phrases, matching the anchor-5 example structure.

5 / 5

Trigger Term Quality

Trigger phrases "building data quality pipelines, implementing validation rules, or establishing data contracts" plus named tools (Great Expectations, dbt tests) give good keyword coverage, though common synonyms like "data testing" or standalone "data validation" are missing.

4 / 5

Distinctiveness Conflict Risk

Clear niche (data quality validation with named tooling) keeps conflict risk low, but "dbt tests" could overlap with a generic dbt skill — minor overlap risk with closely related skills.

4 / 5

Total

17

/

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.

Validation15 / 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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