Data Quality Frameworks
Data & Analyticsby wshobson
3/3 audits passMIT
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.
Data Quality Frameworks, published by wshobson under the MIT licence, teaches the agent to build validation into data pipelines. It defines six quality dimensions (completeness, uniqueness, validity, accuracy, consistency and timeliness), each paired with an example Great Expectations check, and a testing pyramid that runs from schema tests through single-column unit tests to cross-table integration tests.
The reference patterns cover a Great Expectations suite and checkpoint, built-in and custom dbt data tests, data contracts, and an automated quality pipeline that summarises pass rates across tables. The best-practice list is short: validate source data before transformations, add tests as issues surface, document each expectation, alert on failures and version your contracts; do not test every column, ignore warnings, skip freshness checks, hardcode thresholds or test tables in isolation. It is for data engineers setting up checks in an ELT stack or agreeing data contracts between teams, not for one-off profiling of a single file.
This is mainly a skill for coding agents such as Claude Code working in a data engineering repository; it can be imported into BusinessMCP as a playbook, but its tooling (Great Expectations, dbt) runs in your own pipeline, not in BusinessMCP.
What you can do with it
- Add a Great Expectations suite to a pipeline's source tables
- Write custom dbt tests for business rules
- Draft a data contract between a producing and consuming team
- Set up freshness and uniqueness checks with alerting
Run it on your business data
Imported into BusinessMCP, Data Quality Frameworks becomes a playbook your AI business analyst applies to your web analytics, funnels and connected databases.
Use Data Quality Frameworks in BusinessMCPInstall it in a coding agent
One command adds Data Quality Frameworks to your project.
npx skills add https://github.com/wshobson/agents --skill data-quality-frameworksHow we vetted it
- Source
- wshobson/agents at 4236bb9
- Licence
- MIT
- Security audits (skills.sh)
- Gen Agent Trust Hub: Pass · Socket: Pass · Snyk: Pass
- Bundled scripts
- None, instructions only
Checked 2026-09-25 against its skills.sh listing. How we vet skills
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Frequently asked questions
Which tools does Data Quality Frameworks cover?
Great Expectations suites and checkpoints, dbt data tests including custom ones, and data contracts, tied together in an automated quality pipeline.
What quality dimensions does it use?
Completeness, uniqueness, validity, accuracy, consistency and timeliness, each with an example check.
How do I install Data Quality Frameworks?
Run `npx skills add wshobson/agents --skill data-quality-frameworks`, or import it from the BusinessMCP dashboard as a playbook.
Keep exploring
Run Data Quality Frameworks against your whole business
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Use Data Quality Frameworks in BusinessMCP