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.

Install with Tessl CLI

npx tessl i github:Dicklesworthstone/pi_agent_rust --skill data-quality-frameworks

What are skills?

Review — 77%

Does it follow best practices?

If you maintain this skill, you can automatically optimize it using the tessl CLI to improve its score:

npx tessl skill review --optimize ./path/to/skill

Learn more

Validation — 10 / 11 Passed

Validation for skill structure

SKILL.md

Review

Evals

Evaluation results

82%

17%

Inventory Data Quality Setup

GX expectation suite and checkpoint config

Criteria

Without context

With context

GX library import

100%

ExpectationSuite class

75%

100%

Primary key dual check

100%

Schema check with exact_match False

100%

Categorical set expectation

100%

Strict minimum for price

100%

Dateutil parseable check

100%

Dynamic freshness check

Row count sanity

71%

Statistical distribution check

Checkpoint run_name_template

100%

Checkpoint standard actions

37%

100%

Fail on validation error

66%

100%

Without context: $1.4811 · 5m 57s · 54 turns · 53 in / 15,965 out tokens

With context: $0.8648 · 2m 22s · 28 turns · 27 in / 7,602 out tokens

100%

23%

Sales Analytics dbt Test Coverage

dbt test suite with custom and generic tests

Criteria

Without context

With context

dbt_utils.recency test

100%

dbt_utils.at_least_one test

100%

dbt_utils.expression_is_true for business rule

100%

FK relationships test

100%

Email regex validation

40%

100%

Generic test macro syntax

100%

Generic test returns 0 rows on pass

100%

Singular test location

100%

Singular test zero-row pass

100%

Primary key coverage

100%

accepted_values for status

100%

Without context: $0.5218 · 2m 16s · 21 turns · 111 in / 8,206 out tokens

With context: $0.5834 · 1m 42s · 24 turns · 73 in / 5,833 out tokens

100%

68%

Formalizing the Payments Data Asset

Data contract YAML and quality pipeline orchestration

Criteria

Without context

With context

Contract apiVersion

100%

Contract kind field

100%

Metadata owner field

100%

Metadata contact field

28%

100%

Terms section

100%

SodaCL quality type

100%

Quality freshness check

100%

SLA section completeness

12%

100%

DataQualityPipeline class

22%

100%

QualityResult dataclass

77%

100%

Fail-fast ValueError

100%

Report before raise

60%

100%

Without context: $1.0253 · 4m 24s · 32 turns · 32 in / 15,680 out tokens

With context: $0.6127 · 1m 59s · 24 turns · 269 in / 7,224 out tokens

Evaluated: 4 days ago
Agent: Claude Code

Table of Contents

Inventory Data Quality Setup Sales Analytics dbt Test Coverage Formalizing the Payments Data Asset

Is this your skill?

If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.