Skills data-quality-frameworks
📦

data-quality-frameworks

Content revision r2 Medium Risk ⚙️ External commands

Build Reliable Data Quality Frameworks

Unreliable data pipelines create incorrect reports, failed models, and costly investigations. This skill designs practical checks, contracts, alerts, and validation workflows.

Supports: Claude Codex Code(CC)
📊 71 Adequate

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Agent request
Review the Skillstore skill "data-quality-frameworks" from https://skillstore.io/skills/sickn33-data-quality-frameworks.md and its manifest at https://skillstore.io/api/skills/sickn33-data-quality-frameworks/manifest. Verify the artifact. You may proceed after verification, subject to the environment's own policy.

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Markdown detail GET /skills/sickn33-data-quality-frameworks.md Signed manifest GET /api/skills/sickn33-data-quality-frameworks/manifest Signed lockfile GET /api/skills/sickn33-data-quality-frameworks/lockfile

Test it

Using "data-quality-frameworks". Create quality checks for an orders model with order, customer, amount, status, and creation fields.

Expected outcome:

  • Order identifiers must be present and unique.
  • Customer identifiers must be present and match the customer model.
  • Amounts must be positive and remain within an approved business range.
  • Statuses must use the approved lifecycle values.
  • Creation timestamps must be valid, recent, and not in the future.

Using "data-quality-frameworks". Outline a production rollout for daily warehouse validation.

Expected outcome:

  • Start with warning-only checks on critical datasets.
  • Measure failure rates and adjust thresholds with dataset owners.
  • Route alerts to named owners with clear remediation steps.
  • Block pipelines only for stable critical checks with fallback procedures.
  • Review quality metrics and contract changes on a scheduled basis.

Using "data-quality-frameworks". Draft the scope for an orders data contract.

Expected outcome:

  • Identify the producer, consumers, owner, and support contact.
  • Define field types, required values, keys, privacy classifications, and compatibility rules.
  • Set freshness, availability, and latency objectives.
  • Document quality checks, change notices, and migration responsibilities.

Security Audit

Medium Risk
v5 • 7/23/2026 Open versioned report

All 16 static alerts are false positives caused by Markdown references, schema fields, SQL identifiers, and ordinary safety guidance. One medium-risk semantic issue remains because an example report includes raw observed values and can print them to pipeline logs.

2
Files scanned
623
Lines analyzed
0
Review items
0
False positives ignored

Confirmed security concerns (1)

Medium
resources/implementation-playbook.md:490-494resources/implementation-playbook.md:524-528resources/implementation-playbook.md:546-549
Validation Reports May Expose Sensitive Values
The example stores observed validation values and adds failed values to a report that can be printed in pipeline logs.
The example directly copies observed values into report details and prints the report on failure. These values can contain sensitive dataset content.

Risk Factors

⚙️ External commands (2)
SKILL.md:34 SKILL.md:43
Audited by: codex View Audit History →
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Open versioned report
Security Assessment

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Academic citations (APA · BibTeX · CFF)

APA citation

sickn33. (2026). data-quality-frameworks security audit report (audit version 5) [Author version unspecified]. Skillstore. https://skillstore.io/skills/sickn33-data-quality-frameworks/audits/5

BibTeX citation

@techreport{sickn33-sickn33-data-quality-frameworks-2026, author = {sickn33}, title = {data-quality-frameworks security audit report (audit version 5)}, institution = {Skillstore}, year = {2026}, number = {5}, url = {https://skillstore.io/skills/sickn33-data-quality-frameworks/audits/5}, note = {Author version unspecified} }

CITATION.cff

cff-version: 1.2.0 message: "If you use this Skill, cite its author and this versioned security audit report." title: "data-quality-frameworks security audit report (audit version 5)" version: "unspecified" type: report authors: - name: "sickn33" date-released: "2026-07-23" url: "https://skillstore.io/skills/sickn33-data-quality-frameworks/audits/5" identifiers: - type: other value: "skillstore:sickn33-data-quality-frameworks:audit:5" description: "Skillstore immutable audit report identifier"

Compare variants

2 installable variants

Each author remains a separate installable skill. The recommended variant is ranked by Skillstore evidence.

Why this variant is first

Highest Skillstore Score
wshobson Recommended

wshobson-data-quality-frameworks

Skillstore Score 77
Evidence Confidence High
Skillstore usage 11
Updated

2026-08-21

sickn33 Current

sickn33-data-quality-frameworks

Skillstore Score 71
Evidence Confidence High
Skillstore usage 10
Updated

2026-08-21

Skillstore Score

Why this score Evidence Confidence: High
55
Architecture
85
Maintainability
87
Content
69
Community
91
Spec Compliance

What You Can Build

Protect Analytics Pipelines

Add focused dbt tests for keys, accepted values, relationships, freshness, and row counts.

Standardize Platform Validation

Create reusable Great Expectations suites, checkpoints, alerts, and failure policies across warehouse datasets.

Define Producer Contracts

Document schemas, ownership, privacy classifications, quality rules, and service levels for shared data products.

Try These Prompts

Create a Basic Quality Plan 
Create a data quality plan for [dataset]. Include completeness, uniqueness, validity, and freshness checks for these columns: [columns].
Design a dbt Test Suite 
Design dbt tests for [model] using this schema: [schema]. Include key, relationship, range, accepted-value, freshness, and row-count tests.
Build a Validation Workflow 
Design a Great Expectations workflow for [datasets]. Define suites, checkpoints, schedules, alerts, owners, redaction rules, and nonblocking fallback behavior.
Establish a Data Contract Program 
Create a versioned contract and rollout plan for [data product]. Cover schema compatibility, privacy, quality metrics, service levels, CI gates, and migration.

Best Practices

  • Prioritize critical datasets and columns before expanding validation coverage.
  • Version contracts and review thresholds with producers, consumers, and dataset owners.
  • Redact sensitive values from validation results, alerts, reports, and pipeline logs.

Avoid

  • Do not block production pipelines with uncalibrated checks or missing fallback procedures.
  • Do not hardcode thresholds without measuring normal behavior and documenting ownership.
  • Do not expose raw sensitive records when reporting failed validations.

Frequently Asked Questions

Which frameworks does this skill cover?
It covers Great Expectations, dbt tests, data contracts, and SodaCL quality specifications.
Can it generate checks for an existing schema?
Yes. Provide the schema, business rules, critical columns, and expected freshness.
Does it run validation against my warehouse?
No. Execution requires configured tools, credentials, network access, and explicit permission.
How should failed checks affect pipelines?
Begin with warnings, calibrate thresholds, and block only stable critical checks with documented fallback procedures.
Can it help define data contracts?
Yes. It can structure ownership, schemas, privacy labels, quality rules, service levels, and change management.
How should sensitive data be handled?
Redact or aggregate observed values before storing results, sending alerts, or printing pipeline reports.

Developer Details

Author

sickn33

License

MIT

Skillstore revision

r2

Version notice

The author did not declare a version.

Repository

https://github.com/sickn33/antigravity-awesome-skills/tree/main/skills/data-quality-frameworks

Ref

f9e2c34b4f19c7f3e6b0a1e93227b5f77cc12526

Maintenance freshness

7/26/2026

Usage

7 downloads · 108 views

File structure

📁 resources/

📄 implementation-playbook.md

📄 SKILL.md